Intelligent Automation and Systems Engineering

Lecture Notes in Electrical Engineering

For further volumes: http://www.springer.com/series/7818

Sio-Iong Ao Mahyar Amouzegar Burghard B. Rieger l

Editors

Intelligent Automation and Systems Engineering

Editors Sio-Iong Ao International Association of Engineers Unit 1, 1/F, 37-39 Hung To Road Hong Kong, China [email protected]

Mahyar Amouzegar College of Engineering Cal Poly Pomona California State University 3801 West Temple Avenue Pomona, California 91768, USA

Burghard B. Rieger Universita¨t Trier Trier, Germany

ISSN 1876-1100 e-ISSN 1876-1119 ISBN 978-1-4614-0372-2 e-ISBN 978-1-4614-0373-9 DOI 10.1007/978-1-4614-0373-9 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2011933226 # Springer Science+Business Media, LLC 2011 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

A large international conference on Advances in Intelligent Automation and Systems Engineering was held at UC Berkeley, California, USA, October 20–22, 2010, under the auspices of the World Congress on Engineering and Computer Science (WCECS 2010). WCECS is organized by the International Association of Engineers (IAENG). IAENG is a non-profit organization dedicated to engineers and computer scientists, which was founded in 1968 and has been undergoing rapid expansion in recent years. The WCECS conferences have served as excellent venues for the engineering community to meet with each other and to exchange and share ideas in diverse areas of engineering and computer science. Moreover, WCECS continues to strike a well balance between theoretical research and application development. WCECS conference committees are formed with over two hundred members, who are mainly research center directors, deans, department heads (chairs), professors, and research scientists from over thirty countries. The full committee list is available at WCECS web site (www.iaeng.org/WCECS2010/committee. html). The conference participants were truly international, representing high levels of research and development from many countries. The response and the quality of papers in 2010 was excellent, we received more than seven hundred manuscripts, and after a thorough peer review process, 54.94% of the papers were accepted. This volume contains thirty-two revised and extended research articles written by prominent researchers participating in the conference. Topics covered include Expert system, Intelligent decision making, Knowledge-based systems, Knowledge extraction, Data analysis tools, Computational biology, Optimization, Experiment designs, Complex system identification, Computational modeling, and industrial applications. The book offers the state of the art research and development in intelligent automation and systems engineering. It also serves as an excellent reference book for researchers and graduate students working on intelligent automation and systems engineering. Sio-Iong Ao Mahyar Amouzegar Burghard B. Rieger v

Contents

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Evidence Fusion for Real Time Click Fraud Detection and Prevention. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chamila Walgampaya, Mehmed Kantardzic, and Roman Yampolskiy Biped Locomotion with Arm Swing, Based on Truncated Fourier Series and Bees Algorithm Optimizer . . . . . . . . . . . . . . . . . . . . . . . . . Ebrahim Yazdi, Vahid Azizi, and Abolfazle T. Haghighat

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Bio-Inspired Pneumatic Muscle Actuated Robotic System . . . . . . . . . . . Andrea Deaconescu and Tudor Deaconescu

4

Evaluation of an Orthopedic Surgical Robotic System Orthoroby on Bone Cadaver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yasin Gu¨ven and Duygun Erol Barkana

41

Natural Intelligence Based Automatic Knowledge Discovery for Medical Practitioners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Veenu Mangat

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Design and Optimal Control of a Linear Electromechanical Actuator for Motion Platforms with Six Degrees of Freedom . . . . . . . . Evzen Thoendel

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Parametric Identification of a Power-System Emulator . . . . . . . . . . . . . . Rube´n Salas-Cabrera, Oscar Martı´nez-Herna´ndez, Julio C. Rosas-Caro, Jonathan C. Mayo-Maldonado, E. Nacu´ Salas-Cabrera, Aaron Gonza´lez-Rodrı´guez, Hermenegildo Cisneros-Villegas, Rafael Castillo-Gutierrez, Gregorio Hernndez-Palmer, and Rodolfo Castillo-Ibarra

8

Enhanced Adaptive Controller Applied to SMA Wire Control. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Samah A.M. Ghanem

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A Standalone System to Train and Evaluate Operators of Power Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jose´ Tavira-Mondrago´n, Rogelio Martı´nez-Ramı´rez, Fernando Jime´nez-Fraustro, Roni Orozco-Martı´nez, and Rafael Cruz-Cruz Lube Oil Systems Models for Real Time Execution Used on a Training Power Plant Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . Edgardo J. Rolda´n-Villasana, Yadira Mendoza-Alegrı´a, and Iva´n F. Galindo-Garcı´a

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Stochastic Analysis and Particle Filtering of the Volatility . . . . . . . . . Hana Baili

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Numerical Simulation of Finite Slider Bearings Using Control Volume Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mobolaji Humphrey Oladeinde and John Ajokpaoghene Akpobi

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Automated Production Planning and Scheduling System for Composite Component Manufacture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mei Zhongyi, Muhammad Younus, and Liu Yongjin

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Assessment Scenarios of Virtual Prototypes of Mining Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Teodor Winkler and Jarosław Tokarczyk

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A Framework for the Selection of Logistic Service Provider Using Fuzzy Delphi and Fuzzy Topsis . . . . . . . . . . . . . . . . . . . . . Rajesh Gupta, Anish Sachdeva, and Arvind Bhardwaj

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Bee Algorithm for Solving Yield Optimization Problem for Hard Disk Drive Component under Budget and Supplier’s Rating Constraints and Hueristic Performance Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wuthichai Wongthatsanekorn and Nuntana Matheekrieangkrai Integrated Modeling of Functions and Requirements in Product Design and Factory Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D.P. Politze, J.P. Bathelt, K. Wegener, and D.H. Bergsjo¨ Towards a Strategy to Fight the Computer Science (Cs) Declining Phenomenon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marcela Porta, Katherine Maillet, Marta Mas, and Carmen Martinez Development of Power Plant Simulators and Their Application in an Operators Training Center . . . . . . . . . . . . . . . . . . . . . . . Jose´ Tavira-Mondrago´n and Rafael Cruz-Cruz

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Benefits of Unstructured Data for Industrial Quality Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Christian Ha¨nig, Martin Schierle, and Daniel Trabold

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Evaluation the Objectivity Measurement of Frequent Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Phi-Khu Nguyen and Thanh-Trung Nguyen

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Change-Point Detection Based on SSA in Precipitation Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Naoki Itoh and Ju¨rgen Kurths

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Non-linear Image Recovery from a Single Frame Super Resolution Using Pearson Type VII Density . . . . . . . . . . . . . . . . . . . . . . . . . Sakinah Ali Pitchay

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Research on the Building Method of Domain Lexicon Combining Association Rules and Improved TF*IDF . . . . . . . . . . . . . . Shouning Qu and Simon Xu

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Combining Multiscale and Multi Directional Analysis for Edge Detection Using a Statistical Thresholding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K. Padma Vasavi, E.V. Krishna Rao, M. Madhavi Latha, and N. Udaya Kumar

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Soft Vector Quantization with Inverse Power-Function Distributions for Machine Learning Applications . . . . . . . . . . . . . . . . . . . Mohamed Attia, Abdulaziz Almazyad, Mohamed El-Mahallawy, Mohamed Al-Badrashiny, and Waleed Nazih Anfis-Based P300 Rhythm Detection Using Wavelet Feature Extraction on Blind Source Separated Eeg Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Juan Manuel Ramirez-Cortes, Vicente Alarcon-Aquino, Gerardo Rosas-Cholula, Pilar Gomez-Gil, and Jorge Escamilla-Ambrosio A Hyperbola-Pair Based Road Detection System for Autonomous Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Othman O. Khalifa, Imran M. Khan, and Abdulhakam A.M. Assidiq Application of the Real-Time Concurrent Constraint Calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M. Gerardo and M. Sarria

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Teaching and Learning Routing Protocols Using Visual Educational Tools: The Case of EIGRP . . . . . . . . . . . . . . . . . . . . . . Jesu´s Expo´sito Marquina, Valentina Trujillo Di Biase, and Eric Gamess

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Relevance Features Selection for Intrusion Detection . . . . . . . . . . . . . . . Adetunmbi Adebayo Olusola, Oladele S. Adeola, and Oladuni Abosede Daramola

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A Visual Application for Teaching and Learning the Advanced Concepts of the Diffusing Update Algorithm for EIGRP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Valentina Trujillo Di Biase, Jesu´s Expo´sito Marquina, and Eric Gamess

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Chapter 1

Evidence Fusion for Real Time Click Fraud Detection and Prevention Chamila Walgampaya, Mehmed Kantardzic, and Roman Yampolskiy

1.1

Introduction

Web search is a fundamental technology for navigating the Internet and it provides access to information for millions of users per day. Internet search engine companies, such as Google, Yahoo, and Bing have revolutionized not only the use of the Internet by individuals but also the way businesses advertise to consumers (Immorlica et al. 2005; Mahdian 2006; Walgampaya et al. 2010). Typical search engine queries are short and reveal a great deal of information about user preferences. This gives search engine companies a unique opportunity to display highly targeted ads to the user. These search services are expensive to maintain and depend upon advertisement revenue to remain free (Mahdian 2006) for the end user. Many search service companies such as Google, Yahoo and MSN generate advertisement revenue by selling clicks. This business model is known as Pay-Per-Click (PPC) model. In the PPC model, internet content providers are paid for each time an advertisement link on their website is clicked leading to the sponsoring company’s content. There is an incentive for dishonest service providers to inflate the number of clicks their sites generate. In addition, dishonest advertisers tend to simulate clicks on the advertisements of their competitors to deplete their advertising budgets (Metwally et al. 2007). Generation of such invalid clicks either by humans or software with the intension to make money or deplete competitor’s budget is known as click fraud (CF). The diversity of CF attack types makes it hard for a single counter measure to attain desired results. Therefore, it becomes one of the new hot spots in research how to combine multiple data sources with multiple measures to provide the PPC

C. Walgampaya (*) Computer Engineering and Computer Science Department, University of Louisville, Louisville, KY 40292, USA e-mail: [email protected] S.‐I. Ao et al. (eds.), Intelligent Automation and Systems Engineering, Lecture Notes in Electrical Engineering 103, DOI 10.1007/978-1-4614-0373-9_1, # Springer Science+Business Media, LLC 2011

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system with more effective protection from CF. A real time click fraud detection and prevention system based on multi-model and multi-level data fusion is proposed in this paper. Each independent component can be considered as an invisible data mining module, in which “smart” software incorporates data mining into its functional components, often unbeknownst to the user (Han and Kamber 2006). Evidence for CF from multiple models are “fused” in this system, using Dempster-Shafer evidence theory (Shafer 1976), so that it achieves improved accuracy for detecting fraudulent traffic. Conversely it increases the quality of clicks reaching advertisers’ websites. This paper is organized as follows. In Sect. 1.2 an introduction to the multisensor data fusion and Dempster-Shafer evidence theory is given. Related work, where Dempster-Shafer theory was used as the fusion mechanism, is presented in Sect. 1.3. In Sect. 1.4 the fusion architecture of the CCFDP system is described, while a case study is explained in Sect. 1.5. Experimental results and discussion are given in Sect. 1.6. Conclusions are given in Sec. 1.7.

1.2

Multi-Sensor Data Fusion with Dempster-Shafer Evidence Theory

Data fusion is “a process dealing with the association, correlation, and combination of data and information from single and multiple sources to achieve refined position and identity estimates, and complete and timely assessments of situations and threats, and their significance” (Lambert 2009). The resulting information is more satisfactory to the user when fusion is performed than simply delivering the raw data (Wald 2001). Different fusion methods are discussed in literature, such as statistical estimation (Durrant-Whyte 1987; Hager et al. 1993), Kalman filter (Yukun et al. 2007), fuzzy integration (Solaiman et al. 1999), neutral networks (Dai and Khorram 1999), D-S evidence theory (Wu et al. 2002) and so on. Of these fusion methods, D-S evidence theory is widely known for better handling uncertainties. Moreover, it provides flexible information processing and can deal with asynchronous information (Ouyang et al. 2008). In the following section, terminology of theory of evidence and the notation used in this paper are defined.

1.2.1

Frame of Discernment

If Y denotes the set of yN ðyN 2 YÞcorresponding to N identifiable objects, let Y ¼ y1 ; y2 ; :::; yN be a frame of discernment. The power set of Y is the set containing all 2N possible subsets of Y, represented by PðYÞ ¼ ff; fy1 g; fy2 g; :::; fyN g; fy1 ; y2 g; fy1 ; y3 g; :::; Ygwhere f denotes the null set.

1 Evidence Fusion for Real Time Click Fraud Detection and Prevention

1.2.2

3

Basic Probability Assignment Function (BPA)

The BPA is a primitive of evidence theory. The BPA, represented by m, defines a mapping of the power set to the interval between 0 and 1, where the BPA of the null set is 0 and the summation of the BPA’s of all the subsets of the power set is1. The value of the BPA for a given set A, represented as mðAÞ, expresses the proportion of all relevant and available evidence that supports the claim that a particular element of Y belongs to the set A but to no particular subset ofA. The elements of PðYÞ that have none-zero mass are called focal elements. Formally, this description of m can be represented with the following three equations: m : PðYÞ ) ½0; 1 X A2PðYÞ

mðAÞ ¼ 1

mðfÞ ¼ 0

1.2.3

(1.1) (1.2) (1.3)

Belief Function Bel(A)

Given a BPA m, a belief function Bel is defined as: X BelðAÞ ¼ mðBÞ BA

(1.4)

The belief function BelðAÞ measures the total amount of probability that must be distributed among the elements of A.

1.2.4

Combination of Rule of Evidence m(c)

Supposed m1 and m2 are two mass functions formed based on information obtained from two different information sources in the same frame of discernment; according to Dempster’s orthogonal rule we define mðCÞ ¼ ðm1  m2 ÞðCÞ If C ¼ f ðm1  m2 ÞðCÞ ¼ 0

Else P ðm1  m2 ÞðCÞ ¼

A\B¼C

m1 ðAÞm2 ðBÞ 1K

(1.5)

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Where K represents basic probability mass associated with conflict defined as: K¼

X

m1 ðAÞm2 ðBÞ MaxUncovExampPerClass) { Run the PSO/ACO algorithm to discover the best rule predicting class C, called BestRule RS ¼ RS U BestRule TS ¼ TS–{training samples correctly covered by discovered rule} } } END;

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Each particle represents the antecedent of a candidate classification rule. The rule’s class is fixed for all the particles in each run of the algorithm since each run of the algorithm aims at discovering the best rule for a fixed class. This has the advantage of avoiding the problem of having different particles predicting different classes in the same population. Continuous values can be directly represented as a component of the vector associated with a particle and processed using the standard PSO. The vector can be converted into a set of terms and added to rule generated by ACO. For this purpose, we define upper and lower bounds for the continuous attribute in the rule. So there are two dimensions for each continuous attribute. These bounds are seeded by calculating range of all continuous attributes and adding the attribute’s value to range for upper bound and similarly deducting attribute’s value from range for lower bound. If the two bounds in rule condition conflict, they are omitted from rule, but the position and velocity of particle are updated. A particle contains a number of pheromone matrices equal to number of categorical attributes in the data set. Each pheromone matrix contains values for pheromones for each possible value that that attribute can take plus a flag value (the indifference flag) indicating whether or not the attribute is selected to occur in the decoded rule. Updating a particle’s pheromone (the probabilities of choosing attribute values) is done as follows: tcij ¼ tcij þ ð’1  Qc Þ; for all ij belongs to CurrentRule

(5.1)

tcij ¼ tcij þ ð’2  QP Þ; for all ij belongs to BestPastRule

(5.2)

tcij ¼ tcij þ ð’3  Q1 Þ; for all ij belongs to BestLocalRule

(5.3)

tcij ¼ tcij =ðSaiþ1 j¼1 tcij Þ

(5.4)

Where tcij is the amount of pheromone in the current particle c, for attribute i, for value j. Q is the quality of the rule as given by (5.5). F is a random learning factor in the range 0.1. Q ¼ ðTruePos=ðTruePos þ FalseNegÞÞ ðTrueNeg=ðFalsePos þ TrueNegÞÞ (5.5) The population is initialized in positions with nonzero qualities by taking a record from the class to be predicted and using its terms (attribute values) as the rule antecedent. Then a pruning procedure based on term quality is initially applied, and for other iterations a method similar to ACO’s pruning is applied for the final rule produced by each run of the hybrid PSO/ACO algorithm (Holden and Freitas 2007). The rule set so generated on a per class basis is ordered according to rule quality before it is used to classify examples. This helps in conflict resolution as any example is only considered covered by the first rule that matches it from the ordered rule list.

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V. Mangat

ACO/PSO with Precision Fitness

This algorithm uses a sequential covering approach similar to ACO/PSO to discover one classification rule at a time. Begin RS ¼ {} FOR EACH class C { TS ¼ {All training examples belonging to any class} WHILE (Number of uncovered training examples belonging to class C > MaxUncovExampPerClass) { Run the NRule algorithm to discover best nominal rule predicting class C called Rule Run the standard PSO algorithm to add continuous terms to Rule, and return the best discovered rule BestRule Prune BestRule RS ¼ RS U BestRule TS ¼ TS  {training examples covered by discovered rule} } } Order rules in RS by descending Quality Prune RS removing unnecessary terms and/or rules End; A single iteration of this loop only discovers rules based on nominal attributes, returning the best discovered rule. For the continuous part of the rule, a conventional PSO algorithm with constriction is used. The vector to be optimized consists of two dimensions per continuous attribute, one for an upper bound and one for a lower bound. At every particle evaluation, the vector is converted to a set of terms and added to Rule produced by the algorithm for fitness evaluation. If two bounds cross over, both terms are omitted from decoded rule, but Personal Best position is still updated in those dimensions using (5.6) vid ¼ wðvid þ c1 ’1 ðPid  xid Þ þ c2 ’2 ðPgd  xid ÞÞ xid ¼ xid þ vid

ð5:6Þ

To improve the performance of the PSO algorithm, each particle’s initial position is set to a uniformly distributed position between the value of a randomly chosen seed example’s continuous attribute and that value added to the range for that attribute (for upper bound) and at a uniformly distributed position between an example’s value and an example’s value minus the range for that attribute (for lower bound). The particles are prevented from fully converging using the

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Min-Max system. After the BestRule has been generated it is then added to the rule set after being pruned using ACO’s pruning method. But since this is computationally expensive, ACO pruning is applied only if the number of terms is less than a fixed number. Nominal attributes are handled by the NRule algorithm as follows: Begin Initialise population REPEAT for MaxInterations FOR every particle x Set Rule Rx ¼ “IF {null} THEN C” FOR every dimension d in x Use roulette selection to choose whether the state should be set to off or on. If it is on then the corresponding attribute-value pair set in the initialization will be added to Rx; otherwise (i.e., if off is selected) nothing will be added. LOOP Calculate Quality Qx of Rx P ¼ x’s past best state Qp ¼ P’s quality IF Qx > Qp Qp ¼ Qx P¼x END IF LOOP FOR every particle x P ¼ x’s past best state N ¼ the best state ever held by a neighbour of x according to N’s quality Qn FOR every dimension d in x IF Pd ¼ Nd THEN pheromone entry corresponding to the value of Nd in the current xd is increased by Qp ELSE IF Pd ¼ off AND seeding term for xd 6¼ Nd THEN pheromone entry for the off state in xd is increased by Qp ELSE pheromone entry corresponding to the value of Nd in the current xd is increased by Qp END IF Normalize pheromone entries LOOP LOOP LOOP RETURN best rule discovered End;

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Assume a Von Neumann architecture wherein each particle has four neighbours. Initially, pheromone state in each dimension is set to 0.9 for on and 0.1 for off. Quality, Q is defined using Precision as given by (5.7): Laplacecorrected Precision ¼ ð1 þ TPÞ=ð1 þ TP þ FPÞ If TP< MinTP; Q ¼ LaplaceCorrected Precision 0:1; ELSE Q ¼ LaplaceCorrected Precision

ð5:7Þ

where MinTP is the least number of correctly covered examples that a rule has to cover (Holden and Freitas 2008).

5.3.3

Shuffled Frog Leaping(SFL)

Shuffled frog-leaping algorithm (SFL) is a new memetic meta-heuristic algorithm with efficient mathematical function and global search capability. It involves a set of frogs that cooperate with each other to achieve a unified behavior for the system as a whole, producing a robust system capable of finding high quality solutions for problems with a large search space. It uses concept of memes. Memes spread through the behavior that they generate in their hosts. Like the gene complexes found in biology, memeplexes are groups of memes that are often found present in the same individual. Applying the theory of Universal Darwinism, memeplexes group together because memes will copy themselves more successfully when they are grouped together. The pseudocode of SFL algorithm (Eusuff and Lansey 2003; Elbeltagi et al. 2005) is as follows: Begin Generate random population of P solutions (frogs) For each individual i that belongs to P: calculate fitness (i) Sort the population P in descending order of their fitness Divide P into m memeplexes For each memeplex Determine the best and worst frogs Improve the worst frog position using (8) NewpositionXi ¼ CurrentpositionXi + NewVelocityVi (8) Repeat for a predefined specific number of iterations End for Combine the evolved memeplexes Sort the population P in descending order of their fitness Check if termination ¼ true End;

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An initial population of P frogs is created randomly. The structure of an individual frog for rule mining problem is composed of a set of attribute values. The velocity of individual i corresponds to the attribute update quantity covering all attribute values, the velocity of each individual is also created at random. The elements of position and velocity have the same dimension. In the next step, the frogs are sorted in a descending order according to their fitness. Fitness is defined as given by (5.5). The entire population is divided into m memeplexes, each containing n frogs. In this process, the first frog goes to the first memeplex, the second frog goes to the second memeplex, frog m goes to the mth memeplex, and frog m + 1 goes back to the first memeplex, etc. Within each memeplex, PSO is applied to improve only the frog with the worst fitness (not all frogs) in each cycle. If no improvement becomes possible in this case, then a new solution is randomly generated to replace that frog. The calculations then continue for a specific number of iterations. Rule pruning is done iteratively to remove one-term at a time from the rule while this process improves the quality of the rule, and the quality of the resulting rule is computed by (5.5).

5.3.4

SFL with Precision Fitness

As discussed in the previous section, the SFL algorithm has the ability to perform a flexible robust search for a good combination of terms (logical conditions) involving values of the various attributes. To prevent local optima, a submemeplex is constructed in each memeplex, which consists of frogs chosen on the basis of their respective fitness. The better the fitness, the easier it is chosen. So we modify the fitness function to suit our application requirements, which is that the false positives should be penalized severely. Quality (in turn fitness) is now defined using (5.7). The rest of the algorithm is the SFL discussed in previous subsection.

5.4 5.4.1

Experimental Setup Database

The data sets used for rule mining are from the STULONG data set. STULONG is an epidemiologic study carried out in order to elaborate the risk factors of atherosclerosis in a population of middle aged men (Chapman et al. 2000). The primary data for our experiment is sourced from ‘entry’ table of database. Our study focuses on identifying the relationship between alcohol intake (9 attributes), smoking (3 attributes), physical activities (4 attributes) and biochemical attributes (3 attributes). These can be used to classify a patient as hyperlipidemic or not. Hyperlipidemia is defined as the presence of high levels of cholesterol and/or triglycerides in the blood. It is not a disease but a

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metabolic derangement that can be secondary to many diseases and can contribute to many forms of disease, most notably cardiovascular disease. The main aim was to find out how previously known knowledge relates to STULONG data. Data corresponding to 1419 persons has been considered. The attributes were manually extracted. Continuous attributes need to be discretized for ACO. For this purpose, field knowledge of medical experts was used to discretize according to known standards of medical community. Though a large number of small intervals lead to a better model in terms of accuracy, yet discretization has to be controlled as there is a risk of overfitting of data in addition to large search space and an incomprehensible model if the number of intervals is too large. Nominal and categorical data was cleaned to handle missing values. ACO/PSO algorithms can handle both nominal and continuous attributes.

5.4.2

Setting of Parameters

For ACO, the following parameter values were taken: Number of Ants ¼ 2000, Minimum number of records per rule ¼ 15, maximum number of uncovered records ¼ 20(usually 10% of total records of class) and number of rules to test ant convergence ¼ 30. For PSO/ACO and PSO/ACO with PF, number of particles ¼ 100 and number of iterations ¼ 200. For PSO/ACO with PF, ACO pruning was used if rule has less than 20 terms. The value for minimum number of true positives ¼ 15, constriction factor w ¼ 0.729, social and personal learning coefficients, c1 ¼ c2 ¼ 2.05. Maximum number of uncovered examples per class was set to 20. Also, the constant factor of 0.1 in (5.7) was replaced with 0.4 in order to penalize false positives more severely, as this is desirable in medical domain. These values are not optimized. As SFLA is a relatively new algorithm, there is no theoretical basis for parameters setting. The number of memeplexes has been taken as 10, frog population is set to 10n (n being number of attributes), number of submemeplexes is set to 2n/3, number of independent runs was 30 and local exploration was carried out n times (Mangat 2010b).

5.5

Results

The first criterion used to analyze the performance of the various implemented techniques is predictive accuracy, defined in terms of cross validation accuracy rate, which in turn equals quotient between number of test cases correctly classified and the total number of test cases. A k-fold cross validation was used with value of k ¼ 10. This is a standard technique used to evaluate accuracy of data mining techniques. The other two criteria for performance evaluation are the number of rules in a rule set and the number of attribute value combinations or conditions per rule. Table 5.1 summarizes the results obtained by the combined ACO/PSO, ACO/ PSO with Precision Fitness, SFL and SFL with Precision Fitness algorithms.

5 Natural Intelligence Based Automatic Knowledge Discovery for Medical Practitioners

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Table 5.1 Comparison between ACO/PSO, ACO/PSO with PF, SFL and SFL with PF Predictive accuracy Number of rules in rule set Number of terms in rule ACO/PSO 85.1% 17.1  2.27 9.88  0.21 ACO/PSO with PF 91.5% 13.3  1.0 6.81  0.14 SFL 90.1% 8.2 5.3 SFL with PF 94.2% 6.1 4.4

5.6

Conclusion and Future Work

The success of a rule mining method depends on the quality of rules it uncovers. Rule quality can be viewed in terms of its accuracy and comprehensibility. A rule will be usable by a medical practitioner if it is accurate and easily understood. All four techniques studied provide accuracy comparable to other non SI based mining approaches. SFL with PF shows very good results. A system for rule mining over medical data needs to include several kinds of rules without making the system unwieldy. High confidence rules with low support are important to discover rare, but specific conditions. Very generalized rules with high support are required to monitor generic behavior. A system generating large number of rules or rules with too many conditions in the antecedent, tends to confuse the end user and is not interesting for medical knowledge discovery. Shuffled Frog Leaping with new quality measure of fitness performs the best in terms of comprehensibility and accuracy. This method also penalizes false positives severely, which is a desirable property for data mining in the medical domain. One drawback of the approach is the complexity of the algorithm. A possible further research direction is to introduce new data structures to reduce execution time. Certain domain specific constraints can be applied in the preprocessing phase to reduce the input data size. To balance between efficiency and exploration capability, extensive experiments need to be conducted with different settings of parameters to arrive at the optimal values for these algorithms. Acknowledgment I would like to extend my heartfelt gratitude to Prof. Renu Vig for her guidance and support in carrying out this study. I am also grateful to Panjab University authorities for providing support in the form of necessary infrastructure and tools. On a personal front, I am indebted to my family for always keeping my morale high.

References Kotsiantis S, Kanellopoulos D (2006) Association rules mining: a recent overview. GESTS International Transactions on Computer Science and Engineering 32(82):71–82 Ordonez C (2006) Comparing association rules and decision trees for disease prediction. ACM HIKM’06, November 11, 2006, Arlington, Virginia, USA Sordo M, Murphy DN A (2009) PSO/ACO approach to knowledge discovery in a pharmacovigilance context. Harvard Medical School, Charlestown, MA, USA

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Ghannad-Rezaie M, Soltanain-Zadeh H (2006) Senior Member, IEEE, M.-R. Siadat, K.V. Elisevich Medical data mining using particle swarm optimization for temporal lobe epilepsy. 2006 IEEE Congress on Evolutionary Computation Roddick JF, Fule P, Graco WJ (2003) Exploratory medical knowledge discovery experiences and issues. Health Insurance Commission, Australia. ACM SIGKDD Explorations Newsletter, 5(1) Tre´meaux J-M, Liu Y (2006) Mining for association rules in medical data. http://naku.dohcrew. com/dea-ecd/Tremeaux-Liu-2006.pdf Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, pp 487–499 Han J, Pei J (2000) Mining frequent patterns by pattern-growth: methodology and implications. ACM SIGKDD Explorations Newsletter 2(2):14–20 Wang C, Tjortjis C (2004) PRICES: an efficient algorithm for mining association rules. Lecture Notes in Computer Science 3177:352–358 Yuan Y, Huang T (2005) A matrix algorithm for mining association rules. Lecture Notes in Computer Science 3644:370–379 Parthasarathy S (2002) Efficient progressive sampling for association rules. ICDM 2002, pp 354–361 Chuang K, Chen M, Yang W (2005) Progressive sampling for association rules based on sampling error estimation. Lecture Notes in Computer Science 3518:505–515 Li Y, Gopalan R (2004) Effective sampling for mining association rules. Lecture Notes in Computer Science 3339:391–401 Do Tien Dung, Hui Siu Cheung, Fong Alvis (2003) Mining frequent itemsets with category-based constraints. Lecture Notes in Computer Science 2843:76–86 Tseng M, Lin W, Jeng R (2005) Maintenance of generalized association rules under transaction update and taxonomy evolution. Lecture Notes in Computer Science 3589:336–345 Manning A, Keane J (2007) Data allocation algorithm for parallel association rule discovery. Lecture Notes in Computer Science 2035:413–420 Schuster A, Wolff R (2001) Communication-efficient distributed mining of association rules. In: Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, Santa Barbara, California, pp 473–484 Cheung D, Han J, Ng V, Fu A, Fu Y (1996) A fast distributed algorithm for mining association rules. In Proceedings of 1996 International Conference on Parallel and Distributed Information Systems, Miami Beach, Florida, pp 31–44 Dorigo M, Bonaneau E, Theraulaz G (2000) Ant algorithms and stigmergy. Future Generation Computer Systems 16:851–871 Eberhart RC, Shi Y (2001) Particle swarm optimization: developments, applications and resources. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Seoul, Korea Eusuff MM, Lansey KE (2003) Optimization of water distribution network design using the shuffled frog leaping algorithm. Journal of Water Resources Planning and Management, American Society of Civil Engineers, Reston, June 2003, pp 210–225 Holden N, Freitas AA (2007) A hybrid PSO/ACO algorithm for classification. In Proceedings of the 9th Genetic and Evolutionary Computation Conference Workshop on Particle Swarms: The Second Decade (GECCO ’07), pp 2745–2750, ACM Press, London, UK, July 2007 Holden N, Freitas A (2008) A hybrid PSO/ACO algorithm for discovering classification rules in data mining. Journal of Artificial Evolution and Applications (JAEA) Elbeltagi E, Hegazy T, Grierson D (2005) Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics, Elsevier Science Publishers, Amsterdam, January 2005, pp 43–53 Chapman P, Clinton J, Kerber R, Khabaza T, Reinartz T, Shearer C, Wirth R (2000) CRISP-DM 1.0. Technical report, The CRISP-DM Consortium, 2000 Mangat V (2010a) Swarm intelligence based technique for rule mining in the medical domain. International Journal of Computer Applications IJCA 4(1) Mangat V (2010b) Increasing performance of rule mining in the medical domain using natural intelligence concepts. Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA, pp 529–534

Chapter 6

Design and Optimal Control of a Linear Electromechanical Actuator for Motion Platforms with Six Degrees of Freedom Evzen Thoendel

6.1 6.1.1

Introduction History

Motion platforms with six degrees of freedom, also known as hexapods, are possibly the most popular robotic manipulators used in simulation technology. This parallel mechanism was first described by Gough (1957), who constructed an octahedral hexapod to test the behaviour of tyres subjected to forces created during airplane landings. The first document providing a detailed description of this structure used as an airplane cockpit simulator was published in 1965 by Steward (1966) (hence the name of the structure). The Stewart platform is a closed kinematic system with six degrees of freedom and six adjustable length arms (see Fig. 6.1). Compared to other similar structures, its main advantage is high rigidity and a high input-power-to-device-weight ratio. Until the 1990s of the last century, the main obstacle hindering more intensive application development was the insufficient computing power of the available hardware. Determining the position of a hexapod is significantly more difficult than that of conventional serial structures. With regard to the device being controlled in real time, the main challenges are the transformation of coordinates and speed of resolving mathematical procedures (Liu et al. 1993). Despite the computing power issue having been more or less removed in recent years, direct kinematic transformation (i.e. transforming the length of arms to the position of the frame) still remains a challenge.

E. Thoendel (*) Department of Electric Drives and Traction, Czech Technical University, Prague e-mail: [email protected] S.‐I. Ao et al. (eds.), Intelligent Automation and Systems Engineering, Lecture Notes in Electrical Engineering 103, DOI 10.1007/978-1-4614-0373-9_6, # Springer Science+Business Media, LLC 2011

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Fig. 6.1 Current state – motion platform with hydraulic cylinders

6.1.2

Use in Simulation Technology

The Stewart platform is frequently used in simulation technology to simulate motion effects in vehicle or airplane simulators. By using this equipment, it is possible to simulate the forces acting upon the pilot (driver) during the flight (journey), thus bringing the simulator even closer to reality. The concept and role of motion effect simulation in training is discussed, for instance, in Lee (2005); apart from describing the structure of simulators, the book expressly underlines the role of this aspect during emergency event training. With motion effects being generally perceived before other kinds of perceptions (Thoendel 1981), they provide the first possibility of detecting undesired and dangerous behaviour of the airplane or vehicle.

6.1.3

Current Status

Due to the significant weight of the simulator cockpit fitted with the required audiovisual equipment and controls (the weight of the cockpit described in Thoendel (2008), for instance, amounts to 1.5 t) and the need to achieve high

6 Design and Optimal Control of a Linear Electromechanical Actuator. . .

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dynamics levels, the platform’s linear actuators are usually implemented as hydraulic cylinders in simulation technology applications. In spite of this solution meeting the above requirements for sufficient power and dynamics levels, it suffers from the fundamental drawbacks of all hydraulic systems. These drawbacks include, in particular, higher spatial, temporal and financial installation requirements resulting from the need to employ a sufficiently powerful hydraulic aggregate and piping. In addition, hydraulic solutions suffer from relatively high noise levels, which cannot be avoided by separating the aggregate from the platform, as this would inevitably result in losses in the hydraulic piping, translating into decreases in dynamics. Moreover, the aggregate cannot be enclosed in a sound-proof box as this solution does not ensure sufficient dissipation of excess heat; installing the aggregate in a closed room requires an air-conditioning unit to be used, further increasing the costs. The drawbacks of hydraulic systems include environmental aspects too. In particular, hydraulic oil has to be replaced after a certain number of operation hours and can, in cases of system malfunctions and breakdowns, cause local pollution. Therefore, any malfunctions of hydraulic systems have to be resolved with utmost care and attention. Hence, in instances where it is envisaged that the device will be transported on a regular basis (such as the light sports aircraft simulator described in Thoendel (2009)) it is often preferable to use an electric motion system, which, being more affordable and requiring significantly shorter installation times, makes the device more attractive to customers. Compared to hydraulic solutions, electric systems benefit from many advantages – in particular much less noise, higher energy efficacy and more sophisticated control methods. However, besides their indisputable advantages, electric systems also have several disadvantages. The electromechanical transmission is subject to higher friction, increasing the wear and tear of the actuator. Therefore, the lifetime of electric systems is typically somewhat shorter than that of hydraulic solutions. In addition, electric systems require a procedure to bring the device to a safe halt after unexpected power cuts. In the case of hydraulic systems, the ‘safety landing’ procedure is catered for using oil from an appropriately sized hydraulic accumulator. With regard to UPS units significantly increasing the price of the system, power failure emergencies are typically handled using mechanical locks which, in case of an unexpected power cut, fix the platform in its current position. Nonetheless, emergency descents of platforms with an electric drive still remain problematic. Due to their many pros, there have been growing calls to replace, in certain applications, hydraulic cylinders with electric linear actuators while keeping their static and dynamic properties. The first certified aircraft simulator in the world using an electric motion system was finished in 2006 and, according to Repas and Murthy (2009), this trend will prevail in future as well. The following section provides a detailed description of an electric linear actuator intended for the application mentioned above. The main emphasis has been put on the preparation of a mathematical model which will be subsequently used to design the optimal control (Thoendel 2010).

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Electromechanical Linear Actuator

With regard to the forces required, the electromechanical actuator is based on converting rotary motion to linear motion via a ball screw. Benefitting from high efficiency, rigidity and accuracy ball screws are often used in machine tool construction. The design of the whole electromechanical actuator is shown in detail in Fig. 6.2.

6.2.1

Mathematical Model of the Electromechanical Actuator

In order to further analyse the electromechanical system and, in particular, with respect to the need to design the optimal control, this section deals with the mathematical model to be used. However, it is not necessary at this point to

Fig. 6.2 Electromechanical actuator

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fine-tune all model parameters, the main aim being to ensure that the model reflects all significant dynamic properties of the electromechanical actuator and that it works with quantities which can be easily derived or directly measured in the system. The following assumptions were made before designing the mathematical model: • For economical reasons, position or velocity is measured only at one location (motor shaft or ball screw). Therefore, the electromechanical system will be modelled as a system with one degree of freedom. • With respect to the above, the model will ignore the torsional rigidity effects of the ball screw and the mechanical compliance of the connection between the shaft and the ball screw. As shown later by the control results, these factors have only a minor impact on the quality of control with respect to the criteria used to assess regulation quality. The differential equation of motion can be defined immediately after replacing the system with a single virtual body with the generalized mass mred subject to all forces and moments Fred. Called the ‘Generalized Forces Method’, the basic equation can be written as: mred
x€ þ

1 dmred 2 x_ ¼ Fred : 2 dx

(6.1)

In addition, the following properties apply (see, for instance, Nozicka 1991): • The kinetic energy of the generalized mass is equal to the sum of all kinetic energies of the elements of the system. • The virtual work of the generalized force is equal to the sum of the virtual works of all forces and moments in the system. The kinetic energy of the mechanical system can be expressed as: 1 1 1 Ek ¼ m
x_ 2 þ I
’_ 2 ¼ mred
x_ 2 ; 2 2 2

(6.2)

where m is the mass of the load, x the displacement of the ball screw, I the moment of inertia of the rotary parts of the system and j the rotation angle. The relationship between the translational position x and the rotation angle is defined by the conversion constant kmex, while: ’ ¼ kmex
x:

(6.3)

By substituting into the previous expression, we obtain the following equation: mred ¼ I
k 2mex þ m:

(6.4)

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According to the virtual work principle, the following relationship holds true: Fred
dx ¼

N X

dW j ¼ Mh
d’  mg
dx  Ft
dx;

(6.5)

j¼1

_ is the friction force. where Ft ¼ Ft0
sgnðxÞ The above expression can be used to determine the generalized force Fred: _ Fred ¼ Mh
kmex  mg  Ft0
sgnðxÞ;

(6.6)

where Mh is the motor torque, m the load mass and g the gravitational acceleration. By substituting into the basic ‘Generalized Forces Method’ equation, we obtain the equation of motion for the mechanical part of the actuator: 

 _ I
k2mex þ m
x_ ¼ Mh
kmex  mg  Ft0
sgnðxÞ:

(6.7)

The driving torque Mh is generated by an AC servomotor controlled by a servo driver. With regard to these ddevices being shipped by the manufacturer with the optimum current/moment regulator settings, the device shall be modelled as a firstdegree dynamic system with the time constant t. With respect to small time constants, the remaining dynamic properties of the servo drive can be left out of consideration, playing only a negligible role in the overall behaviour of the system and being irrelevant for the control design. Servo drive dynamics can be expressed with the transfer function Gel ðsÞ ¼

kel Mh ðsÞ ¼ ; t
sþ1 uðsÞ

(6.8)

where u is the variable corresponding to the required moment and, by the same token, the input signal of the system and kel the electric constant of the motor.

6.2.2

Design of the Optimal Control

For the sake of convenience, the mathematical model will be written using matrices of state and transformed into the discrete form. Non-linearity caused by friction forces can be compensated for by adding to or subtracting from the input signalu the value uFt corresponding to the friction force Ft0, and therefore will be left out of consideration. Below are the equations of state of the system: x^_ ¼ A^ x þ B^ u; y ¼ C^ x þ D^ u;

(6.9)

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where 2

2 kmex 3 0 6 t
mred 7 6 6 7 6 ; B¼4 0 A ¼6 1 0 0 7 4 5 1 kel 0 0  3 t 2 2 3 1 0 0 0 0 7 6 6 0 1 07 6 7 7; D ¼ 6 0 0 7; C ¼6 7 6 4 5 4 15 0 0 0 0 t 3 2 " # x_ u 7 6 : x^ ¼4 x 5; u^ ¼ 1 Mh 0

0



mg 3 mred 7 7 0 5; 0

ð6:10Þ

The corresponding discrete form of the system for the sampling period T can be written as: x^ððn þ 1ÞT Þ ¼ Ad x^ðnTÞ þ Bd u^ðnTÞ; yðnT Þ ¼ C^ xðnTÞ þ D^ uðnTÞ;

ð6:11Þ

Ad ¼ eAT ; Z T AT eAt dt
B: Bd ¼ e

ð6:12Þ

where

0

In designing the optimal control, the following quality criteria will be considered: • The regulated variable will be the displacement of the ball screw x. • In simulation technology (where the device in question is used to reproduce forces), system response time (i.e. transmission bandwidth) is the most important and essential quality parameter. • With regard to small steady-state deviations from the desired position not being perceptible to persons sitting in the simulator cockpit, regulation accuracy is not critical in this application. • In spite of this requirement not being as critical as, for instance, in machine tools control, where similar mechanisms involving ball screws are often used, unit step response should result in small overshoots only. • Control has to be sufficiently robust to react flexibly to changes in the load m, which can be a value from the preset interval m 2 h0; mmax i:

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In terms of control theory, it is advisable to use as much information about the controlled system as possible. Ideally, we should be able to either directly measure or somehow derive all state variables. In this case, the control law equals to (see, for instance, Rossiter 2003): u ¼ K x
x^ þ K r
ref þ u0 ;

(6.13)

where Kx is the row vector, Kr the scalar, ref the reference/desired position and u0 ¼ mg=kel kmex the constant compensating the effects of the load. It can be proved that using state space control (state feedback loops) it is theoretically possible to control the behaviour of the system as required (Havlena and Stecha 2002). In practice, however, one is limited by the input signal, which amounts to finite values from the range u 2 humin ; umax i: The following sections focus on determining the optimal state space control to regulate the position of the electromechanical linear actuator. The optimal control is one which minimises the optimality criterion; let us now define a quadratic criterion based on the control quality requirements set forth hereinabove: min J ¼ min u

u

N X

J n ¼ min

n¼1

u

N X

J yn þ J un þ J overshoot ; n

(6.14)

n¼1

where, at the optimisation horizon N, Jy penalises the deviation from the desired position: J yn ¼ q1 ½ yðnÞ  ref ðnÞ2 ;

(6.15)

with Ju penalising the input signal if it exceeds the allowed limits: 8 < q2 ðu  umax Þ2 J un ¼ q2 ðu  umin Þ2 : 0

u>umax ; u > > > > plds ¼ wr lqs þ ðr s þ r L Þ½  ðLlfd þ Lmd Þlds =L þ Lmd lfd =L > > > > > < plfd ¼ r fd Lmd lds =L  r fd ðLls þ Lmd Þlfd =L þ vfd > pia ¼ r a ia =Laa  Laf wr if =Laa þ va =Laa > > > > > > > pif ¼ r f if =Lff þ va =Lff > > > : pwr ¼ PðT e1  T e2 Þ=ð2J 2 þ PJ 1 Þ 8 > < vqs ¼ r L lqs =ðLls þ Lmq Þ vds ¼ r L ðLlfd þ Lmq Þlds =L þ r L Lmd lfd =L > : wr

(7.14)

(7.15)

where Te1, L and Te2 are defined in (7.2), (7.9) and (7.11) respectively. It is clear that expression (7.14) is the state equation and (7.15) is the output equation. The following remarks are in order: Remarks • Some of the experimental variables to be measured are the 3-phase instantaneous voltages (original variables) at the terminals of the generator, that is [vas vbs vcs]T. Then the electrical outputs (vqs and vds in (7.15)) defined in the rotor reference frame can be calculated by using a particular case of expression (7.4). • The experimental synchronous generator that is employed in this work does not have any damper winding. This is the reason for not including any state equation associated with those windings. • Since the 3-phase voltages at the terminals of the generator are balanced the 0s voltage (v0s) is zero 8t. This is the reason for not including any 0s equation and/or variable. • The external resistive circuit that is connected at the terminals of the generator is considered to have parameters that are known. Additionally, it is clear that a resistive circuit does not increase the order of the system to be tested.

7.2.4

Linearized System

The dynamic model presented in (7.14) and (7.15) can be linearized by using the Taylor’s expansion about an equilibrium point. It is important to note that during steady state conditions the machine variables (original state variables) of the DC motor are normally constants. In contrast, during steady state conditions the stator

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machine variables (original stator state variables) of the synchronous generator consist of a balanced 3-phase sinusoidal set. Transforming the machine variables of the generator to the rotor reference frame allows us to obtain an augmented model (motor-generator set) that has an equilibrium point instead of having a state vector containing periodic solutions during steady state conditions. The existence of the equilibrium point is a basic requirement for our linearization process. The linearized dynamic system follows. pDx ¼ ADx þ BDu Dy ¼ CDx þ EDu

(7.16)

where Dx ¼ [Dlqs Dlds Dlfd Dia Dif Dwr]T is state vector, Dy ¼ [Dvqs Dvds Dwr]T is the measurable output, Du ¼ [Dva Dvfd]T is the input and A ¼ ½ A1 2 6 6 6 6 A1 ¼ 6 6 6 6 4

A2

ðr s þ r L Þ=ðLls þ Lmq Þ wr0 0 0 0   k1 k2 k3 lds0 þ Lmd lfd0 =L

2

A3

A4

3

A6 

A5 2

3

wr0

7 6 ðrs þ r L ÞðLlfd þ Lmd Þ=L 7 7 7 6 7 7 6 7 7 6 r fd Lmd =L 7; A2 ¼ 6 7 7 7 6 0 7 7 6 7 7 6 5 5 4 0 k1 k2 k3 lqs0

3

2

3

2

3

2 3 0 7 6 6 ðr s þ r L ÞLmd =L 7 0 7 6 6 7 7 6 0 7 6 6 7 7 6 7 6 6 7 6 rfd ðLls þ Lmd Þ=L 7 0 0 7 6 6 7 7 6 ; A5 ¼ 6 A3 ¼ 6 ; A4 ¼ 6 7 7 7 L w =L 0 6 ra =Laa 7 6 af r0 aa 7 7 6 7 6 4 r f =Lff 5 7 6 5 4 5 4 0 0 k1 Laf ia0 k1 Laf if 0 k1 k2 Lmd lqs0 =L 0

2

lds0

0

0

7 6 6 0 lqs0 7 6 6 7 6 6 7 6 6 0 0 7; B ¼ 6 A6 ¼ 6 6 L i =L 7 6 1=L af f 0 aa 7 aa 6 6 7 6 6 5 4 4 1=Lff 0 0

0

0

3

07 7 7 17 7; C ¼ ½ C1 07 7 7 05 0

C2 

7 Parametric Identification of a Power-System Emulator

2

r L =ðLls þ Lmq Þ 6 C1 ¼ 4 0

85

3 2 0 0 0 7 6 rL ðLlfd þ Lmq Þ=L 5; C2 ¼ 4 r L Lmd =L 0

0

0

0

0

0 0

3 0 7 05

0

1

E¼0 where k 1 ¼ P=ðJ 1 P þ 2J 2 Þ; k2 ¼ 3P=4 k3 ¼ ðLlfd þ Lmd Þ=L þ 1=ðLls þ Lmq Þ and x0 ¼ [lqs0 lds0 lfd0 ia0 if0 wr0]T is the equilibrium point where the linearization was performed.

7.3

Experimental Setup

Important components of the experimental system are a personal computer, a National Instruments AT-MIO-16E-10 data acquisition card, NI Labview software, two custom made PWM-based power electronics converters, an incremental encoder, a custom made microcontroller based design for defining several random sequences and signal conditioning circuits for measuring several variables. A block diagram of the experimental setup is shown in Fig. 7.1. To identify parameters in (7.16) an experimental test is carried out. Basically, the experimental test consists of applying (at the same time) two different random sequences to the incremental inputs of the system Du. During this test, we restrict those inputs such that the dynamics of the augmented system is located in a small neighborhood of the equilibrium point. Microcontroller in Fig. 7.2A contains the assembler source program for defining the random sequence. The PWM signal is generated by using the design presented in Fig. 7.2B. The schematic diagram of the power stage and the DC motor is depicted in Fig. 7.2C. A design similar to the one that is presented in Fig. 7.2 is used to apply the random sequence to the field winding of the synchronous generator. The only difference is that both designs contain different random sequences. In addition, a circuit is designed for conditioning the signals to be measured, see Fig. 7.3. Once the variables have been isolated and have adequate amplitudes, they are connected to the analog inputs of the data adquisition board, see Fig. 7.3. Additional diagrams of the experimental setup for the parametric identification can be found in Martinez-Hernandez (2009).

86

Fig. 7.1 Block diagram of the experimental setup

Fig. 7.2 Circuit for applying a random sequence to the DC motor

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Fig. 7.3 Signal conditioning circuit

7.4

Results

The measured trace of the PWM field winding voltage of the synchronous generator (vfd) is depicted in Fig. 7.4. After some off-line filtering and subtracting the input corresponding to the equilibrium point (vfd0) the PWM voltage in Fig. 7.4 becomes the incremental voltage (Dvfd) depicted in Fig. 7.5. It is evident that the measured PWM field winding voltage corresponds to an incremental voltage having a timevarying (random) period. Figure 7.5 also shows the ideal (not measured) trace of the corresponding random sequence. Similar traces were obtained for the other incremental input, i.e. for the measured voltage of the DC motor (Dva). Incremental inputs (Dva and Dvfd) having time-varying (random) periods clearly affect the measured voltages in original coordinates at the generator terminals, see the voltage vas shown in Fig. 7.6. As it was explained earlier, the experimental voltages in the rotor reference frame (Dvqs and Dvds) can be obtained by performing an off-line calculation, i.e. transforming the incremental version of the measured 3-phase voltages at the terminals of the generator [vas vbs vcs]T. As an example, voltage denoted by Dvds (expressed in the rotor reference frame) is shown in Fig. 7.7. It is important to note that the unsymmetrical nature of the voltage in Fig. 7.7 is related to the initial condition of the angle employed in the mapping (7.4). Once we obtained the transient experimental data corresponding to the output Dy ¼ [Dvqs Dvds Dwr]T and the input Du ¼ [Dva Dvfd]T, we utilized Matlab for the off-line processing of the data. In particular, prediction-error technique is used

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Fig. 7.4 Measured PWM field winding voltage of the synchronous machine

Fig. 7.5 Incremental field winding voltage of the synchronous machine

to calculate the parameters in (7.16). It is important to note that the filtered versions of the inputs (as the one presented in Fig. 7.5) were employed just to tune the incremental values. The prediction-error algorithm is actually performed by using the PWM version of the incremental inputs.

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Fig. 7.6 Measured phase a voltage at the terminals of the synchronous machine

Fig. 7.7 Incremental ds voltage of the synchronous machine

A good agreement is observed when the measured and simulated outputs are compared, see Figs. 7.8 and 7.9. It is clear that the simulated response is obtained by solving the linear system that results of substituting the calculated parameters.

90

Fig. 7.8 Comparison between the measured and simulated output (Dvds)

Fig. 7.9 Comparison between the measured and simulated output (Dwr)

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A key assumption here is that the model order and the structure of the vector/ matrices used as a base for the prediction-error algorithm is the same as the one defined by model in (7.16). The resulting vectors/matrices are 3 3 3 3 2 2 2 2 201:1 134:57 0 0 6 134:57 7 6 4; 678:2 7 6 16; 183 7 6 0 7 7 7 7 7 6 6 6 6 7 7 7 7 6 6 6 6 7 6 6 820:93 7 6 2; 897:8 7 6 0 7 0 7 7 7 7 6 6 6 6 A1 ¼ 6 7; A2 ¼ 6 7; A3 ¼ 6 7; A4 ¼ 6 130:7 7 0 0 0 7 7 7 7 6 6 6 6 7 7 7 7 6 6 6 6 5 5 5 4 4 4 4 0 0 0 0 5 89; 708 1; 920:4 6; 539:3 44:234 3 140:86 7 6 6 104:78 7 7 7 6 6 7 7 6 6 7 7 6 6 0 0 7; A6 ¼ 6 7 A5 ¼ 6 6 3; 689:9 7 6 30:377 7; 7 7 6 6 7 7 6 6 5 4 73:486 5 4 0 2

3

0 0

2

2

1; 944:6

6 6 6 6 0 B¼6 6 0:045635 6 6 4 0:11965 0

0 2

152:93 C¼4 0 0

0 0

0 1:6195 0

0 3:8727 0

3 0 07 7 7 17 7 07 7 7 05 0

3 0 0 0 0 0 05 0 0 1

The initial condition of the incremental state vector is Dxð0Þ ¼ ½0:0025939

2:0613

0:64278

4:8217

0:13137 2:094T:

References Biro K, Szabo L, Iancu V, Hedesiu HC, Barz V (2001) On the synchronous machine parameter identification. Technical University of Cluj-Napoca Dehghani M, Nikravesh SKY (2008) State-space model parameter identification in large-scale power systems. IEEE Trans Power Syst 23(3):1449–1457 Dehghani M, Nikravesh SKY (2009) Estimation of synchronous generator parameters in a large scale power system. JICICI 5(8):2141–2150 Dehghani M, Karrari M, Malik OP (2007) Synchronous generator model identification using linear H identification method. IFAC Workshop ICPS 07 Cluj-Napoca, Romania Ghomi M, Sarem YN (2007). Review of synchronous generator parameters estimation and model identification. Islamic Azad University of Toyserkan, UPEC 2007, pp 228–235 Hasni M, Djema S, Touhami O, Ibtiouen R, Fadel M, Caux S (2007) Synchronous machine parameter identification in frequency and time domain. Serb J Elec Eng 4(1):51–69

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Krause PC, Wasynczuk O, Sudhoff SD (2004) Analysis of electrical machinery and drive systems. Wiley, IEEE Press Power Engineering Kyriakides E, Heydt G (2004) Estimation of synchronous generator parameters using an observer for damper currents and a graphical user interface. Arizona State University Department of Electrical Engineering, Tempe Martinez-Hernandez O (2009) On the modeling and parametric identification of a motor-generator set, MSc. thesis (in spanish). Instituto Tecnologico de Cd. Madero. Cd. Madero, Mexico Salas-Cabrera R, Martinez-Hernandez O, Castillo-Ibarra R, Rosas-Caro JC, Gonzalez-Rodriguez A, Salas-Cabrera EN, Cisneros-Villegas H, Castillo-Gutierrez R, Hernandez-Palmer G (2010) On the modeling and parametric identification of a motor-generator set. In: Lectures notes in engineering and computer science: proceedings of the world congress on engineering and computer science 2010. WCECS, San Francisco, pp. 942–948. Salas-Cabrera R, Martinez-Hernandez O, Castillo-Ibarra R, Rosas-Caro JC, Gonzalez-Rodriguez A, Salas-Cabrera EN, Cisneros-Villegas H, Castillo-Gutierrez R, Hernandez-Palmer G (2011) Modeling and parametric identification of a turbine-generator emulator. Eng Lett IAENG 19(1):68–74 Vermeulen HJ, Strauss JM, Shikoana V (2002) Online estimation of synchronous generator parameters using PRBS perturbations. IEEE Trans Power Syst 17(3):694–700

Chapter 8

Enhanced Adaptive Controller Applied to SMA Wire Control Samah A.M. Ghanem

8.1

Introduction

Besides the mainly verified or validated properties of the modeling approaches of the SMA focusing to the modeling of the different observed effects, all these SMA models lack the applicability in different engineering applications and in the control of the SMA since they don’t describe a general behaviour of any SMA type or shape; idealizing the material constants and thereby disallowing their application into different systems, and into different applications scenarios and also for modelbased control design purposes. However, if an SMA wire within a control loop is replaced by another SMA wire with new properties, or with an SMA plate, etc. those mathematical models may be in detail weak to perform the control task as they lack generalization properties from an engineering point of view, and weak in understanding this actuator material behavior as an I/O-system with structured behavior. Model-based control approaches may allow in combination with suitable design approaches the design and the realization of robust control algorithms and therefore the general usage of the SMA not only to be used as actuator but also for application where the shape memory effect should not be used i.e. the actuation where the inverse model can cancel the expected behaviour of the SMA, therefore it is an open research topic as far as no SMA generalized behavioural based model exit (Ghanem 2010). Non-Model-based control systems are limited to the usage of the SMA as actuators in applications and need tuning for its procedures any time the material physical characteristics are changed. As a result, non-model-based adaptive control approaches in the position control of the SMA is of vital importance to be studied (Ghanem 2010; Ghanem et al. 2010).

S.A.M. Ghanem (*) Institute of Telecommunications, University of Porto, Portugal e-mail: [email protected] Birzeit University, Palestine S.‐I. Ao et al. (eds.), Intelligent Automation and Systems Engineering, Lecture Notes in Electrical Engineering 103, DOI 10.1007/978-1-4614-0373-9_8, # Springer Science+Business Media, LLC 2011

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The adaptive approaches in control theory performs a redesign of the controller in accordance to the changing parameters of the plant. Adaptive control is a good choice in the shape memory alloy (SMA) wire actuator plant case. This is because control of SMA is a hard process due to the non-linearity exhibited by the SMA material. That is, the hysteretic behavior of the material causing the shape memory effect exhibited within it. However, methods used for controlling the position of the SMA lacks the simplicity and the adaptability to the SMA characteristics since environmental changes influence the characteristics of the SMA (Ghanem 2010; Reynolds 2003; Chirani et al. 2003; Bouvet et al. 2004; Dutta et al. 2005; Teh and Featherstone 2003; Song et al. 2003; Bizdoaca et al. 2006; Benzaoui et al. 1999; Silva 2007). The proposed adaptive controller is derived through mathematical equations of the MRAC and STC adaptive control (Ghanem et al. 2010; Slontine and Li 1999). The concept of PID classical control functionality was used to let the PID proportional, integral, and derivative Kp, Ki, and Kd parameters respectively to be used as the estimator parameters or the adaptation parameters of the adaptive controller proposed. The enhanced version of adaptive controllers that can be used to control linear or non-linear plants will be derived step by step, the robustness of this controller has been evaluated through a hard control problem which is the SMA wire, however, this adaptive controller can be used to tune the PID parameters giving more robust solutions; i.e. real time adaptive tuning, such that the parameters fit the plant in an easier way than using other well known tuning procedures in control. The chapter will firstly introduce the basic two approaches of adaptive control used in the derivation of this adaptive controller, then it will discuss the shape memory alloy wire position control using the designed PID adaptive controller, a tuned PI controller, and the experimentally enhanced version of the adaptive controller. Finally, the experimental results of the enhanced adaptive controller and the tuned PI controller under different sets of actuation frequencies will be evaluated.

8.2

Adaptive Control Approaches

Since adaptive control performs a redesign of the controller in accordance to the changing parameters of the plant, adaptive control is a good choice in the application of SMA wire actuator plant control. The next sections will introduce the basic two approaches of adaptive control that leads to the way how the PID adaptive controller is derived and tested on the SMA wire.

8.2.1

MRAC Control

Model reference adaptive controller basic principle is to build a reference model that specifies the desired output of the controller, and then the adaptation law adjusts the unknown parameters of the plant so that the tracking error converges to zero (Slontine and Li 1999).

8 Enhanced Adaptive Controller Applied to SMA Wire Control

8.2.2

95

STC Control

Self tuning adaptive controller basic principle of is to have a parameter estimator that estimates recursively the unknown parameters of the plant and feeds it to the controller. This recursive estimation based on the parameters that fit the past inputoutput criteria of the plant (Slontine and Li 1999).

8.3

Adaptive Control with Combined MRAC And STC

A combined approach that joins MRAC and STC will be introduced in building a PID adaptive controller. The reason for calling this approach a combined approach, is that the estimated parameters will be the controller parameters; which will adapt to the plant unknown parameters recursively like in STC, while the tuning will be according to the tracking error convergence to zero like in MRAC, since the reference model here is the plant itself, and that is the basis for calling the SMA actuator as the SMA actuator plant. The equations corresponding to the MRAC controller for a first order plant are the following: vðtÞ ¼ ½ r

y T

where vðtÞdenotes the signal vector, r is the reference input signal, and y is the output signal.


 a^r a^ðtÞ ¼ : a^y where a^ðtÞ is the vector of the controller adaptive parameters; defined by their derivatives in terms of plant parameters to derive the adaptation law as follows: a^_ r ¼ sgnðbp Þger a^_ y ¼ sgnðbp Þ gey

(8.1)

where, a^_ r is the derivative of the estimated parameter corresponding to the reference signal r and a^_ y is the derivative of the estimated parameter corresponding to plant output signal y, e is the error signal, sgn(bp) determines the direction of search for the proper controller parameter, and g is the adaptation coefficient.

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8.3.1

Building an Adaptive Controller

Choose the position as the estimated parameter and the position error as the second estimated parameter. Let the signal vector be: vðtÞ ¼ ½ e

y T

Here e is the position error, and y is the actual position. Let the controller estimated parameters be:
a^ðtÞ ¼

a^s a^y



Where a^s is the estimated parameter corresponding to error signal, and a^y is the estimated parameter corresponding to actual position signal, then the adaptation law is the following: a^_ s ¼ sgnðbp Þgez a^_ y ¼ sgnðbp Þgey

(8.2)

Substitute bp as 1 since there is no transfer function for the SMA wire dynamics to build the controller.

8.3.2

Building the PID Adaptive Controller

Using the error signal as one of the signals used for adaptation helps the controller to react. And since there is no equation expressing the plant dynamics, to build a PID adaptive controller the assumption will be to use the controller parameters as the estimated parameters of the plant; while this will help the PID controller to self-tune itself; which is a combined approach of MRAC and STC. Figure 8.1 shows a block diagram describing the idea. In Fig. 8.1., eðtÞ denotes the error signal; which equals the desired position subtracted from the actual position of the SMA to insure convergence of the error to zero, like in MRAC. The reference model of the MRAC is the plant itself; the adaptation coefficients are the estimator coefficients of the STC to let the PID adapt its controller coefficients to the plant. The block attached to the PID is a block to explain that the output of the controller is converted using blocks that derive the current into the SMA wire. Based on the formulas of regular PID controllers, the _ _ _ idea in Fig. 8.1 is that the k^p , k^i , and k^d are the estimated parameters of the plant

8 Enhanced Adaptive Controller Applied to SMA Wire Control

97

Fig. 8.1 The PID adaptive controller combined MRAC and STC

while they are recursively tuning their corresponding controller parameters as well with the following adaptation law, so that the PID controller coefficients are just the integral of the adaptation law. ð ^ ) kp ¼ ge2 dt:

_ k^p ¼ ge2 _ k^i ¼ ge

ð

ð ð ^ edt ) ki ¼ ge edt dt:

de _ k^d ¼ ge dt

ð de ^ ) kd ¼ ge dt: dt

(8.3)

k^p is the proportional gain, k^i is the integral gain, k^d is the derivative gain. While _^ _ kp is the proportional estimated parameter, k^i is the integral estimated parameter, _ and k^d is the derivative estimated parameter. So the integral form of the estimated vector is: 2

3 k^p a^ðtÞ ¼ 4 k^i 5 k^d And the signal vector corresponding to each of them like normal PID controller signals as follows:
vðtÞ ¼ eðtÞ

ð eðtÞdt

eðtÞ ¼ yA ðtÞ  yr ðtÞ

 deðtÞ T : dt

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S.A.M. Ghanem

where yA(t) is the actual position of the SMA wire and yr(t) is the desired position. The output of the PID Controller will be the following sum of the proportional, integral, and derivative outputs: YðtÞ ¼

X

bðiÞ

To simplify the representation of equations in time domain, the s-domain will be used. Where an integral is a division by s, and a derivative is a multiplication by s. And instead of using t as the time samples, i will be used as the iteration number, as follows: 1 bP ðiÞ ¼ ge3 ðiÞ ¼ kp e S bI ðiÞ ¼ ge3 ðiÞ

1 e ¼ ki S2 s

bD ðiÞ ¼ ge3 ðiÞs ¼ kD es Then the output of the controller can be expressed by the following stable system, YðiÞ ¼

X

YðiÞ ¼ ge3

8.4

P;I;D

bðiÞ ¼ bp ðiÞ þ bI ðiÞ þ bD ðiÞ

ðs4 þ s2 þ 1Þ : s3

Position Control of The SMA

The Shape Memory Alloys (SMA) are smart materials that are difficult to control due to its hysteretic behavior and unpredictable changes to its characteristics under temperature, electrical excitation, or mechanical forces. Such type of behavior encourages the usage of adaptive control approaches in the control of the SMA wire. However, the SMA will be used to test the validity and robustness of the derived adaptive controller in controlling the position of the SMA wire. The SMA experimental setup includes all the hardware and software components required for the experiment: The NiTi SMA wire, a constant load connected to the wire, a slider or a removable block to enable the movement of the wire and the connected mass under negative and positive elongations. Software components mainly include the MATLAB/SIMULINK and the dSPACE data acquisition system for testing real time changes on the SMA wire. The SMA wire used in the experiments is a Nickel Titanium wire, with the parameters mentioned in Table 8.1.

8 Enhanced Adaptive Controller Applied to SMA Wire Control Table 8.1 Parameters of the NiTi Shape Memory Alloy

8.4.1

Length (m) 0.55

Radius (m) 0.0002

Density (Kg/m3) 6,500

99

Area (m2) 6.9115*104

Volume (m3) 6.9115*108

Adaptive Controller Test on SMA Position Control

To measure the output current from the SMA actuator wire, the values of voltages should be divided by the resistance R ¼ 8O to approximate the current which is the square root of this value and saturated in the range 0 to 1.7A with pffiffiffiffiffiffiffiffiffiffiffiffiffi1:7 iðtÞ ¼  YðtÞ=8 : Fig. 8.2 shows the block diagram for the PID adaptive control0

ler in equation (3). A similar PID adaptive controller has been derived by Feng Lin, et al. (2000) using Fre´chet derivative and SISO system formulas, while here simplified formulas and combination of adaptive approaches lead to this model.

8.4.1.1

Design of the PI Controller

The PID adaptive controller shows a single shot high performance results, in the experimental environment. However, the readings for the Kp, Ki, and Kd gains on real time allows the design of a classical tuned PI controller that is robust and consistent in the control of the position of the SMA wire in (Ghanem 2010; Ghanem et al. 2010).

8.4.1.2

Design of the Enhanced Adaptive Controller

Although simulation results are perfect for the PID adaptive controller, however, in the real world where no idealization exit, a set of recursive experiments on the PID adaptive controller proved inconsistency in the controller. This leads to the need improve the controller design and to test its behavior experimentally. The mathematical adaptation law in (3) will not change, while the adaptation mechanism will be modified. Such that the estimated parameters corresponding to kp, ki, and kd will be the same, while the controller parameters already called kp, ki, and kd will be changed so that it is not similar to proportional, integral, and derivative gains respectively. The error signal will be used as the signal vector such that: vðtÞ ¼ ½eðtÞ eðtÞ

eðtÞT

for all controller parameters instead of using the proportional, integral, and derivative components of the error signal to get the corresponding controller parameters; which are not anymore PID parameters. In comparison with the previous PID adaptive controller parameters, the kp is same as it was; dependent on the error signal, and the ki and kd are equally likely. This change indeed results in an enhanced version of a new adaptive controller, Fig. 8.3 shows the block diagram of the enhanced adaptive controller. The controller was tested in the control of the SMA wire and it gives a consistent robust control.

Fig. 8.2 PID adaptive controller for position control of the SMA

100 S.A.M. Ghanem

Fig. 8.3 Enhanced adaptive controller for position control of the SMA

8 Enhanced Adaptive Controller Applied to SMA Wire Control 101

102

8.4.2

S.A.M. Ghanem

Experimental Results

A detailed set of experimental results have been introduced in (Ghanem et al. 2010) using this controller setup, however, we sum up the following summarized points: 1. Equations of MRAC controller with adaptation law (1) do not respond experimentally and give no response to control the position of the SMA. 2. Refinement of equation (1) into (2) to make the parameter dependant on the error signal instead of the reference signal makes the controller starts to respond and control the position of the SMA, while the performance is low. 3. The PID adaptive controller with adaptation law in (3) was tested and it shows excellent results in controlling the position of the SMA. Note that there is a time interval needed for adaptation almost equal to 30 seconds. Multiple set of experiments show that the adaptive controller with error vector signal is an enhanced adaptive controller with more consistency and less adaptation time than the PID adaptive controller. 4. The enhanced adaptive controller was tested under different set of frequencies and gives high performance and robustness in the control of SMA under different sets of excitation frequencies (Ghanem 2010; Ghanem et al. 2010). 5. For this SMA actuator plant in Table 8.1, it is proved that chosing an adaptation coefficient g ¼ 1 under different sets of experiments gives the best performance, any increase or decrease of g will affect the performance of the adaptive controller. 6. For this SMA in Table 8.1, the online Kp, Ki, and Kd parameters running the adaptive controller under different sets of adaptation coefficients gives the following values for the PID gains Kp ¼ [13.2–13.9]; Ki ¼ [0.3;1]; and for Kd ¼ [0.33;0.39]. So, these values can be used for tuning a PID controller with coefficient’s ratios [Kp: Ki: Kd] ¼ [14:1:0.3], which are the tuned PI controller parameters. Hence, this adaptive controller can be used to tune the Kp, Ki and Kd coefficients for linear or non-linear plants with unknown dynamics. 7. Using a chaotic wave input signal, Fig. 8.4 shows the results of using an arbitrary chaotic signal as the desired position for both the PI controller and the enhanced adaptive controller. The chaotic signal is generated using the Chua’s equations set (Yousef Al-Sweiti 2006). The performance of both controllers is very good with few delays in the actual position, but the overall performance of the PI controller is better. The adaptive controller shows that its adaptability is not mainly based on the input signal consistency, but it is dependent on the plant dynamics and the error signal as well. 8. Experimenting the tuned PI controller under different set of excitation frequencies. The PI controller tuned using the adaptive controller leads to better performance (Ghanem 2010; Ghanem et al. 2010). 9. Comparing the control error for both, the tuned PI controller and the enhanced adaptive controller under different set of excitation frequencies leads to better performance in the PI controller, Fig. 8.5 shows the comparison. Note that the experimental results were taken in real time from the user interface of the dSPACE data acquisition system connected to the SMA test rig.

8 Enhanced Adaptive Controller Applied to SMA Wire Control

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Fig. 8.4 PI and enhanced PID adaptive controller results using chaotic signal as the desired position. Time in (s) versus desired position (dotted) and actual position (solid) in (mm)

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S.A.M. Ghanem

Fig. 8.5 Position control error (mm) for PI controller (dashed), and enhanced adaptive controller (dotted) under different excitation frequencies (Hz)

8.5

Conclusion and Future Work

In this chapter, we introduced the design of an adaptive controller using a combination of two basic adaptive approaches in control, the MRAC, and the STC. The PID adaptive version is used to derive a tuned PI controller. The PI controller and the enhanced adaptive controller are used to control the position of the SMA wire. The controller can be used to control different linear or non-linear plants with unknown dymanics, i.e. similar to the SMA. The adaptive control introduced as a smart alternative for the Non-model based approaches that lack the adaptability to such type of materials that change its behavior by changing its characteristics. While also adaptive control in such kind of difficult non-linear control problems stand as alternative for a model-based controller in their non-dependency on the temperature, i.e. the characteristic changes in the case of the SMA. However, it is still required to build model-based controllers that allows a general usage of the SMA not only to be used as actuator, but also for different engineering applications, where the application will not be only focused on the control of the position. Therefore, it is an open research topic as far as no SMA generalized behavioural based model exit.

References Arbab Chirani S, Aleong D, Dumont C, McDowell D, Patoor E (2003) Superelastic behavior modeling in shape memory alloys. Journal de Physique 4(112):205–208 Benzaoui H, Chaillet N, Lexcellent C, Bourjault A (1999) A Non linear motion and force control of shape memory alloys actuators. Proceedings of SPIE - The International Society for Optical Engineering 3667:337–348

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Bizdoaca N, Hamdan H, Selisteanu D (2006) Fuzzy Logic Controller for a Shape Memory Alloy Tentacle Robotic Structure. University of Craiova, Romania, IMRAC, France Bouvet C, Calloch S, Lexcellent C (2004) A phenomenological model for pseudoelasticity of shape memory alloys under multiaxial proportional and nonproportional loading. European Journal of Mechanics, A/Solids 23(1):37–61 Dutta SM, Ghorbel FH, Dabney JB (2005) Modeling and control of a shape memory alloy actuator. In: Proceedings of the 20th IEEE International Symposium on Intelligent Control, ISIC ’05 and the 13th Mediterranean Conference on Control and Automation, MED, art. no. 1467151, pp 1007–1012, 2005 Teh YH, Featherstone R (2003) A new control system for fast motion control of SMA actutator wires. Department of Systems Engineering, Research School of Information Sciences and Engineering, The National University, 2003 Li F, Brandt RD, Saikalis G (2000) Self tuning of PID controllers by adaptive interaction. Proceedings of the American Control Conference, Chicago, IL Reynolds DR (2003) A nonlinear thermodynamic model of phase transitions in shape memory alloy wires. P.h.D. Thesis. Rice University, Houston, TX, May, 2003 Ghanem SAM (2010) Position control of shape memory alloy (SMA) wire using model and behavioral-based and non-model-based controllers. #2010, VDM Verlang Dr M€uller Ghanem SAM, Shibly H, Soeffker D (2010) Enhanced adaptive controller using combined MRAC and STC adaptive control approaches for the control of shape memory alloy wire. Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science 2010, WCECS 2010, 20–22 October, 2010, San Francisco, USA, vol 2187, pp 996–1001, Issue 1 Silva EPD (2007) Beam shape feedback control by means of a shape memory actuator. Materials and Design 28(5):1592–1596 Slontine JJ, Li WP (1991) Applied nonlinear control, pp 312–328, #1991 by Prentice Hall International Inc. Song G, Chaudhry V, Batur C (2003) Precision tracking control of shape memory alloy actuators using neural networks and a sliding-mode based robust controller. Institute of Physics Publishing, Smart Materials and Structures, vol 12, 2003 Yousef Al-Sweiti (2006) Modeling and control of an elastic ship-mounted crane using variablegain model-based controller. Ph.D. Thesis. University of Duisburg-Essen, Germany, 2006

Chapter 9

A Standalone System to Train and Evaluate Operators of Power Plants Jose´ Tavira-Mondrago´n, Rogelio Martı´nez-Ramı´rez, Fernando Jime´nez-Fraustro, Roni Orozco-Martı´nez, and Rafael Cruz-Cruz

9.1

Introduction

A wide list of the main benefits of using a variety of training simulators is shown in (International Atomic Energy Agency 1998), some of the most important are: the ability to train on malfunctions, transients and accidents; the reduction of risk to plant equipment and personnel; the ability to train personnel on actual plant events; a broader range of personnel receiving effective training; and individualized instruction or self-training being performed effectively on simulation devices designed with these capabilities in mind. Since more than 25 years, the simulation department of the Electric Research Institute (IIE) has designed, developed and started-up all the power plant simulators required for the Federal Commission of Electricity (CFE) to train its operators of thermal, geothermal and combined cycle power plants. These simulators have different scopes according to the training demands of the CFE, therefore in their training centers, the CFE have classroom simulators, full-scope replica and no-replica simulators, and with the aim of improving the onsite training programs, the CFE also has portable part task simulators. Currently, the training facilities of the CFE with the bigger number of simulators is The Ixtapantongo National Training Center (CENAC-I), which is devoted to train the operators of thermal power plants, the available simulators in this center, which have been developed by the IIE (Tavira-Mondrago´n et al. 2010a; Tavira-Mondrago´n et al. 2009; Zabre et al. 2009; Tavira-Mondrago´n et al. 2006; Tavira-Mondrago´n et al. 2005) are the following: 1. Two full-scope replica control board simulators, one for a 300 MW thermal power plant, and one for a 400 MW combined cycle power plant.

J. Tavira-Mondrago´n (*) Instituto de Investigaciones Ele´ctricas (IIE), Reforma 113 Col. Palmira, Cuernavaca, Mor., Mexico e-mail: [email protected] S.‐I. Ao et al. (eds.), Intelligent Automation and Systems Engineering, Lecture Notes in Electrical Engineering 103, DOI 10.1007/978-1-4614-0373-9_9, # Springer Science+Business Media, LLC 2011

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108

2. Five full-scope simulators with graphic operation interface for a 300 MW thermal power plant, for a 350 MW thermal power plant, for a 350 MW coal fired power plant, for a 150 MW gas turbine power plant, and for a 450 MW combined cycle power plant. 3. Two classroom simulators, one for a 300 MW thermal power plant, and one for a 350 MW thermal power plant. 4. Two partial-task portable simulators, one for turning and acceleration of the main turbine, and one for a 300 MW thermal power plant. The partial-task simulators are portables, so they are transported to the power plants, in this way, the operators can practice onsite, these simulators can be utilized with the assistance of an instructor or in a free-hands context, but in any case they do not have an automatic tutoring system or an evaluation system. The full-scope and classroom simulators are hosted in the CENAC-I facilities and require of an instructor to guide the training session. With these simulators, the CENAC-I prepare the operation personnel of the thermal power plants of the CFE; the training programs include retraining strategies, safe operation under malfunctions and familiarization with power plant systems. Another important issue is that the partial-task simulators are based on UNIX operating system, while the other simulators are based on personal computers with Windows XP operating system and with a very similar simulation environment. On the other hand, expert systems provide powerful and flexible tools to solve to a wide range of problems that often cannot be dealt by traditional methods; therefore, the use of expert systems has proliferated to many technological areas. In this way, although full-scope simulators are recognized worldwide as the only realistic method to provide real time and hands-on training of operators (International Atomic Energy Agency 2004), the inclusion of intelligent tutoring systems to train power plant operators has been a study matter to enhance the capabilities of a power plant simulator (Seifi and Seifi 2002), or as an intent to optimize operators training (Arjona Lo´pez et al. 2003; Tavira-Mondrago´n et al. 2010b). The main features of a portable simulation system to train and evaluate operators onsite without the guidance of a human instructor are presented. This system utilizes CLIPS as expert system and keeps the attributes of full-scope, detailed mathematical modeling and real-time functioning for each one of the two simulated processes (a 350 MW coal-fired power plant and a 450 MW combined cycle power plant). The software architecture and the most important aspects of the developed tools to design the training exercises are also shown; finally the results and conclusion achieved are presented.

9.2

The Power Plant Simulators

Because of the training demands onsite, the 350 MW Coal-Fired power plant Simulator (CFS) and the 450 MW Combined Cycle power plant Simulator (CCS) were selected among the available simulators at the CENAC-I. The CFS reproduces

9 A Standalone System to Train and Evaluate Operators of Power Plants

109

the behavior of a 350 MW dual fuel power plant, so it can operates with oil or coal as fuel, this unit has as main components: a forced circulation steam generator, a tandem compound turbogenerator, the feedwater system, and the auxiliary systems (e.g. lubrication oil, fuel, water services, electric grid, etc.). On the other hand, the CCS reproduces the behavior of a 450 MW combined cycle power plant, this unit has as main components: two gas turbines, two heat recovery steam generators, one tandem compound turbogenerator, the feedwater system, and the auxiliary systems (e.g. lubrication oil, fuel, water services, electric grid, etc.). Due to these simulators are of the full-scope type, their mathematical models must be able to reproduce, in a dynamic way, the behavior of the power plant in any feasible operation, this means, any steady state from cold iron up to full-load generation, and transients states as a part of operation itself or because of malfunctions, therefore, the simulators have detailed mathematical modeling based on physical laws, and to customize the models, to the actual power plant used as reference, each one of the equipments (tanks, valves, pumps, fans, heat exchangers, etc.), are characterized with design information and operation data. In this kind of modeling, the governing equations are formed by a group of algebraic and first-order differential equations. Once all the governing equations had been developed and programmed, each one of the models were tested and validated in an independent way, and their corresponding step-size integration methods were defined. After that, they were inserted into a modular sequential solver according to their causality. Additionally, each one of the simulators has its distributed control system; these systems include manual, semi-automatic and automatic operation modes, according to the specified requirements of the simulated areas. In brief, the control algorithms are organized in basic components with a very specific function (PID, Set/Reset, Dead Band, Limiters, etc.), and they are represented through a hierarchical components network. This network is stored as a diagram and consequently a group of diagrams constitutes a module. These modules have their own solver according to the order of precedence of the modules

9.3

Hardware and Software Architecture

Due to the simulation system will be continuously transported to power plants, a robust and portable computer platform was selected, for a safe and easy transportation. The computer selected is a personal computer with three LCD integrated monitors. This computer, based on Windows XP operating system, has a dual Xeon quad processor of 2.66 GHz, 4 GB memory and one 160 GB hard disk. The software architecture of the simulation system is shown in the Fig. 9.1, the CFS and the CCS used as base simulators for the new simulation system were adapted and integrated to this architecture. The description of the software modules is presented in the following sections.

J. Tavira-Mondrago´n et al.

110

Fig. 9.1 Software architecture

9.3.1

Simulator Modules

A detailed discussion of the simulator software is described in (Tavira-Mondrago´n et al. 2010a), therefore herein just a brief description of each module is presented. The control models and process mathematical models are discussed in Sect. 2.

9.3.1.1

Real Time Executive

The real-time executive module coordinates all simulation functions and its main parts are: (a) the mathematical model launcher; (b) the manager module for interactive process diagrams; (c) the manager module for the global area of mathematical models; (d) the manager module for the instructor console; (e) data base drivers.

9.3.1.2

Main Sequencer

It is in charge of sequencing, in real-time, all the functions which require a cyclic execution, these are: mathematical models, control models and other additional functions like historical trends.

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111

Operator Module

The operator module is in charge of the Human Machine Interface (HMI) of the trainee and manages the information flow with the executive system. The HMI consists of interactive process diagrams, which are Flash movies; the flash movies have static and dynamic parts. The static part is constituted by a drawing of a particular flow diagram, whereas the dynamic part is configured with graphic components stored in a library which are related to each one of the plant’s equipment, e.g., pumps, valves, motors, etc. These components have their own properties and they are established during the simulation.

9.3.1.4

Instructor Console Module

This interface is hidden to the trainee, but the instructors of the CENAC-I can access it during the stage of design, test and debug of the training exercises. This module consists of five parts: (a) a main module to carry out all the tasks related to the graphical interface of the instructor; (b) a module to retrieve the static information of the simulation session, e.g., malfunctions, internal parameters, etc.; (c) a module to store information in a data base using SQL; (d) a module to dynamically update the instructor console with the simulation information; (e) a module to communicate the instructor console with the real-time executive.

9.3.2

Evaluation Console Modules

These modules constitute the application software to train and evaluate the trainees.

9.3.2.1

Expert System

Expert systems use human expertise (the result of deliberated practice on standard tasks over many years) to answer questions, pose questions, solve problems, and assist humans in solving problems. They do so by using inferences similar to those a human expert would make, to produce a justified, sound response in a brief period of time. When questioned, they should be able to produce the rules and processes that show how they arrived at the solution (Olmstadt 2000). The use of an expert system provides many advantages over traditional programming. The system written in a conventional language would result in low performance and reliability with very limited flexibility when compared to the same task performed in a declarative expert system. Additionally expert systems typically allow for the incorporation of explanation facilities. With an explanation facility, the user (trainee) may “ask” the system why it is asking a particular question or how it has reached a particular conclusion. This is a capability that is

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not possible using conventional programming techniques. Additionally, with the structure of the system being declarative in nature, knowledge may be continually added to the system there by extending its capabilities (Chan 1996). The expert system incorporated to the simulation environment is the C Language Integrated Production System (CLIPS), which is a widely used tool to build expert system by government, industry and academia. The expert system is responsible of tracking the status of the simulation to determine the group of rules that should be fired. Due to its inference engine, and according to the configuration of the simulation exercise, the expert system is able to modify the simulation process, because it can insert malfunctions, modify values of process and control variables, and change the status of the simulation without the intervention of a human instructor. The expert system is embedded within the real-time executive of the simulator. In order to provide a suitable interface to the expert system, to its inference engine were added external functions to couple it to the main sequencer of the simulators.

9.3.2.2

Exercises Editor

This editor is a graphic tool to configure the training exercises in the simulation system. This configuration has two parts: 1. Simulation scenario. In this part are defined aspects like: exercise objective, initial condition for the simulation (e.g. cold iron, full-load, etc.), trend graphics, interactive process diagrams, process variables to evaluate the simulation, and questions for the theoretical evaluation 2. Exercise sequence. This sequence is established through a graphical language, which is a data flow language where basic elements are blocks and lines, the blocks have a specific function, for instance, to send a message to the user or establish a waiting time for an action. Each block consists of a header and an action, in the header is the logical code to launch its action. The execution sequence of all the blocks, and the communication among them is carried out by means of lines, in this way, an active input line is the signal to start the block, and an active output line indicates the block has finished its function. Additionally, the blocks have another input line to abort the block activation if a preset condition is detected. The result of this edition is a directed acyclic graph, which is stored in XML format. Finally, with a specially developed translator, the XML file is transformed in a new file containing an ordered group of rules which are the base information for the expert system

9.3.2.3

Theoretical Evaluation

The conceptual support of the training exercises is supported by multimedia lessons, and a theoretical evaluation. The multimedia lessons consist of theoretical explanations related with the training exercise and they can be presented to the

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trainee through different multimedia formats. In the case of the theoretical evaluation, the questions related with the exercise objectives are captured in a database, and at the moment of the evaluation, a group of them are selected and displayed randomly. This module is also in charge of feedback to the trainee with the correct answers to his mistakes.

9.3.2.4

Trainee Interface

This interface allows trainee to access the training exercises in the simulation system, and it is displayed in the three monitors of the computer, in one of them the trainee has the functions to control the simulation and a window to show the messages from the expert system. For the operation of the simulated process, the trainee has two monitors with interactive process diagrams, which have the same look and functioning like the diagrams of the base simulators, in this way, the trainees do not need to learn a new operation interface.

9.4 9.4.1

Knowledge Acquisition and Representation Knowledge Acquisition

The C Language Integrated Production System (CLIPS) is an inference engine initially developed to facilitate the representation of knowledge to model human expertise (CLIPS). CLIPS provides a cohesive tool for handling a wide variety of knowledge with support for three different programming paradigms: rule-based, object-oriented and procedural. Rule-based programming allows knowledge to be represented as heuristics, or “rules of thumb,” which specify a set of actions to be performed for a given situation. A rule-based expert system is defined as one, which contains information obtained from a human expert, and represents that information in the form of rules. The rules can then be used to perform operations on data to inference in order to reach appropriate conclusion. These inferences are essentially a computer program that provides a methodology for reasoning about information in the rule base or knowledge base, and for formulating conclusions (Liao 2004). Object-oriented programming allows complex systems to be modeled as modular components, which can be easily reused to model other systems or to create new components. In the procedural approach, CLIPS can be called from a procedural language, perform its function, and then return control back to the calling program. Likewise, procedural code can be defined as external functions and called from CLIPS. When the external code completes execution, control returns to CLIPS. Knowledge acquisition is built by carrying out predefined simulation exercises with the “Instructor Console” mode of the simulation system; these tests are derived

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of the training procedures for the simulators and they are performed by qualified instructors of the CENAC-I. The training procedures are the same utilized during the simulation sessions guided by an instructor in the simulators of the CENAC-I, therefore, they include all the operation actions required from the trainee, and this helps to build a very complete knowledge base. These procedures include normal operation (startup, normalization and shutdown operations, for each one of the power plant systems); and abnormal operations (predefined malfunctions, like pumps trips, pipe breaks, etc.). In this way, with the record of the operation actions, the results observed and the instructor experience is obtained all the necessary information for the expert system.

9.4.2

Knowledge Representation

According to the design specifications of the evaluation system, and the knowledge acquisition method, the rule-based approach to represent the knowledge was selected. This approach is also called IF-THEN rules. Some of the benefits of IFTHEN rules are their modularity, and that each one defines a relatively small and independent piece of knowledge. New rules can be added and old ones deleted usually independently of other rules. With the aim of avoiding resolution conflict, the rules must have a predefined order. The exercises editor is a graphic tool where the instructor of the CENAC-I builds the training exercises for the simulation system, Fig. 9.2 is an example of a simulation exercise. These editor contains a group of blocks where each block represents a rule (or a group of rules), and each block is customized by its characteristic parameters. The available blocks in the current version of the editor are the following: 1. Malfunction. It inserts a malfunction (e.g. pump trip, fouling in heat exchangers, etc.). 2. Local Action. It modifies a local action of the simulated process (e.g. valves position). 3. External Parameter. It modifies an external parameter to the simulated process (e.g. ambient temperature). 4. Cancel Malfunction. It removes an active malfunction. 5. Snapshoot. It takes a snapshot of the simulator state, so the trainee can return to this point in any moment. 6. Message. It sends a message to the trainee. 7. Conditional Message. It sends a message to the trainee requesting an operative action to satisfy certain condition (e.g. the trainee needs opening the fuel valve to increase the steam pressure in the boiler). 8. Timer. It establishes a time period waiting for a trainee action. 9. True/False Question. It asks a true/false question to the trainee. 10. Multiple Choice Question. It asks a multiple option question to the trainee.

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Fig. 9.2 Simulation exercise

9.5

Implementation and Results

The purpose of the developed system (simulator-expert system) is to provide a standalone application where operative personnel of the power plants can practice and evaluate themselves without the supervision of a human instructor; this practice will be a complement of the regular training courses carried out at the CENAC-I, due to this, the training exercises are designed with a very specific objective and a duration among 20 and 60 min. Figure 9.3 shows in a simple way the proposed approach to develop and implement the training exercises. The design stage of the exercise is carried out by a qualified instructor of the CENAC-I. In this phase the instructor (based on operation procedures) develops a training exercise with a specific objective, for instance, boiler

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Fig. 9.3 Implementation of an exercise

warming from ambient temperature up to 120 C. The process of warming the boiler from ambient temperature up to nominal conditions (540 C) requires at least of 6 h, but instead of having very long exercises, which can discourage or bore to the trainees, the proposed exercise has a duration of 30 min, and the goal is to train and evaluate to the trainee in specific operative actions related with the startup of the oil system and the initial stage of boiler warming. Additional exercises can be prepared to practice the complete process of boiler pressurization up to nominal conditions. As a part of the simulation scenario, the instructor defines the initial condition of the simulation system, creates the questions for the theoretical evaluation, assigns the corresponding multimedia material for the theoretical lessons, and selects the process variables for the simulation evaluation, for each one of these variables, the instructor defines a weight over the final grade, and also defines a time penalization in the case of exceeding the expected time to carry out the exercise. Finally with the exercises editor, the instructor “transforms” the operation procedure in a group of rules to guide/track the trainee operation supported by the expert system. During a training session with the simulation system, the trainee has a graphical interface to perform by himself his practice. Figure 9.4 shows a partial view of this interface, its main parts are: 1. Trainee functions. These are the functions available for the trainee during a simulation session, and they are: (a) Selecting the simulator and the training exercise; (b) Running/stopping the simulation; (c) Selecting snapshots; (d) Reloading the

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Fig. 9.4 Partial view

interactive process diagrams; (e) Displaying theoretical information related with the exercise scope, and application of the theoretical evaluation: (g) Selecting a new user. 2. Trainee auxiliary window. This window displays the theoretical information of the exercise or it can be utilized to display a trend graph or an interactive process diagram during simulation. 3. Messages window. This window displays to trainee all the processed information by the expert system, for instance, if it is required a trainee action, the expert system displays a message, if the trainee carries out the right action, the message changes of color; if the trainee does not carry out the action, the expert system verifies the timer specified for the action, and it sends a warning message to continue the simulation or abort and initiate again the practice. All these options are fully configurable in the exercises editor. When some of the criteria specified for finishing the exercise are achieved, the simulation system calculates the grade and shows the result to the trainee; after that, the simulation system is ready to start the execution of a new exercise.

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The results of each one of the simulation exercises are stored in a database and they are available for the consult of the instructors of the CENAC-I. The main stored results are: trainee identification, grades of the theoretical test and the simulation practice and total elapsed time. Currently have been developed and tested two exercises for each one of the power plant simulated. For the coal-fired power plant simulator, the exercises are: boiler warming from ambient temperature up to 120 C, and malfunction detection of regenerative air heaters. In the case of the combined cycle power plant simulator, the exercises are startup of feed water system, and turning and synchronization of the steam turbine.

9.6

Conclusions

With the aim of having a portable simulator, in which power plant operators can practice onsite, without assistance of an instructor, was developed a simulation system which includes an expert system, a 350 MW coal-fired power plant simulator and a 450 MW combined cycle power plant simulator. These simulators were integrated in a common simulation environment, and they kept their detailed mathematical modeling, their full-scope and real-time features, and provide a suitable human machine interface to train operators of modern power plants. In this simulation environment were implanted an expert system and a group of functions to train and evaluate the trainees in theoretical and practical aspects of the simulated process. Due to the nature of the knowledge acquisition process, the ruled-based approach was selected for knowledge representation. To create the simulation exercises for the trainee, a graphic editor was developed to design and implant in the simulator such exercises, thanks to this editor, the tasks related with coding of rules was replaced by a process of selecting, dragging and customizing the required blocks, which simplifies enormously the creation of exercises. Each one of the simulation exercises can include theoretical material to study, evaluation of theoretical concepts, guide and supervision via an expert system, and the corresponding evaluation. All these options are fully configurable in the exercises editor. The final objective of these simulation exercises is to provide to operators on site with a tool to practice specific operation actions, as a complement of the regular training programs which they carry out at the CENAC-I. The whole system has been validated with the development and test of four simulation exercises for normal operation and malfunctions, and it is expected the simulation system will be sent to the power plants once a complete group of exercises is developed, according to the current training requirements of the CFE. Acknowledgment The authors would like to thank all the personnel of the IIE and CENAC-I who participated in the project development. The simulation environment is proprietary software of the IIE, and it was customized to the particular requirements of this project.

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References Arjona Lo´pez M, Hernandez FC, Gleason GE (2003) An intelligent tutoring system for turbine startup training of electrical power plant operators. Exp Syst with App 1:95–101 Chan PF (1996) An expert system for diagnosis of problems in reinforced concrete structures. Master of Applied Science Thesis. Royal Melbourne Institute of Technology, Australia CLIPS, A Tool for Building Expert Systems [Online]. Available: http://clipsrules.sourceforge.net International Atomic Energy Agency (1998) Selection, specification, design and use of various nuclear power plant training simulators, IAEA-TECDOC-995, pp 2–3 International Atomic Energy Agency (2004) Use of control room simulators for training of nuclear power plant personnel, IAEA-TECDOC-1411, p 7 Liao S (2004) Expert system methodologies and applications-a decade review from 1995 to 2004. Exp Syst with App 28:93–103 Olmstadt W, (2000) Cataloging expert systems: optimism and frustrated reality. J South Acad Sp Lib ISSN: 1525-321X Seifi H, Seifi A (2002) An intelligent tutoring system for a power plant simulator. Elect Power Syst Res 3:161–171 Tavira-Mondrago´n J, Parra-Go´mez I, Martı´nez-Ramı´rez R, Tellez-Pacheco J (2005) 350 MW fossil power plant multiple simulator for operators training. Proceedings of the Eighth IASTED International Conference 2005, 29–31 August, Oranjestad, Aruba Tavira-Mondrago´n J, Parra-Go´mez I, Melgar-Garcı´a J, Cruz-Cruz R, Tellez-Pacheco J (2006) A 300 MW fossil power plant part-task simulator. Proceedings of the 2006 summer simulation conference, July 30–August 3, Calgary, Canada Tavira-Mondrago´n J, Melgar-Garcı´a J, Garcı´a-Garcı´a J, Cruz-Cruz R (2009) Upgrade of a fullscope simulator for fossil-fuel power plants. Proceedings of the spring simulation conference 2009, SpringSim´09, 22–27 March, San Diego, USA Tavira-Mondrago´n J, Jime´nez-Fraustro L, Romero-Jime´nez G (2010a) A simulator for training operators of fossil-fuel power plants with an HMI based on a multi-window system. Int J of Comp Aided Eng and Tech 1:30–40 Tavira-Mondrago´n J, Martı´nez-Ramı´rez R, Jime´nez-Fraustro F, Orozco-Martı´nez R, Cruz-Cruz R (2010b) Power plants simulators with an expert system to train and evaluate operators. Proceedings of the World Congress on Engineering and Computer Science 2010, WCECS 2010, 20–22 October, San Francisco, USA Zabre E, Rolda´n-Villasana E, Romero-Jime´nez G, Cruz R (2009) Combined cycle power plant simulator for operator’s training. Proceedings of the World Congress on Engineering and Computer Science 2009, WCECS 2009, 20–22 October, San Francisco, USA

Chapter 10

Lube Oil Systems Models for Real Time Execution Used on a Training Power Plant Simulator Edgardo J. Rolda´n-Villasana, Yadira Mendoza-Alegrı´a, and Iva´n F. Galindo-Garcı´a

10.1

Introduction

The Mexican Electric Research Institute (IIE1, http://www.iie.org.mx) was established in 1975. Since then, it has been the R&D support of the Mexican Electric Utility Company (CFE) offering solutions and innovations in the technical area. The Simulation Department (GS) of the IIE is a technical group specialized in the development of training simulators. They design and implement tools and methodologies to sustain the simulators development and operation. The GS has developed several training simulators devoted to train power plant operators. To satisfy their training requirements, CFE has acquired simulators based on control panels, classroom simulators, portable simulators and, recently, simulators based on multi-window man machine interfaces (MMI) as control screens. The use of real time full scope simulators has proven to be effective and reliable for training operators. The operators can learn to operate the power plant more efficiently in transients or maneuvers such as lowering of the heat rate or reducing the power required by auxiliary equipment (Hoffman 1995). Even simulators without full scope are being successfully used (Fray and Divakaruni 1995). In 2007 CFE sought to have a combined cycle simulator (SCC) using the open architecture of the IIE products. It was decided the new simulator to be developed in two stages: the gas-turbine part in 2007 and the steam-heat recovery part in 2009 (Zabre et al. 2009). The description of the lube oil systems models of the combined cycle simulator (CCS) is presented in this chapter, based in a previous work (Mendoza-Alegrı´a and Rolda´n-Villasana Edgardo 2010).

1

Some acronyms are after the name or phrase spelling in Spanish

E.J. Rolda´n-Villasana (*) Departamento de Simulacio´n, Instituto de Investigaciones Ele´ctricas, 62440 Cuernavaca, Morelos, Mexico e-mail: [email protected] S.‐I. Ao et al. (eds.), Intelligent Automation and Systems Engineering, Lecture Notes in Electrical Engineering 103, DOI 10.1007/978-1-4614-0373-9_10, # Springer Science+Business Media, LLC 2011

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10.2

Description of the Simulator

10.2.1 Simulation Sessions Scope The simulator is controlled by an instructor from his own console. The operators train using control screens, which are replica of the real ones. The simulator allows defining simulation scenarios: an initial condition is established (cold start up, 100% of load, etc.) and an assignment is imposed to the operator. The instructor tracks the simulation session and, without the knowledge of the operator, introduces malfunctions to evaluate the operator´s response. A malfunction is a simulated failure of plant equipment, for example: heat exchangers tube ruptures, pumps trips, obstruction of filters, etc. The instructor has the option to define a delay, permanence time, severity, and evolution time of a malfunction. If the operator asks for an action that in the real plant is not made in the control room but is executed by the auxiliary operators in the field, the instructor may execute it on his console. The instructor can modify the external conditions: atmospheric pressure and temperature (dry and wet bulb), voltage and frequency of the external grid, fuel composition, etc. The “ANSI/ISA S77.20-1993 Fossil-Fuel Power Plant Simulators Functional Requirements” norm was adopted as a design specification, including the real time execution.

10.2.2 Hardware Four Personal Computers, interconnected through a fast Ethernet Local Area Network, constitute the simulator. Each PC has a Pentium D processor with 3.6 GHz, 2 GB of RAM, 160 GB HD, and Windows XP as operating system. Figure 10.1 shows a schematic of this architecture.

10.2.3 Software WindowsXP is the operating system. The simulator was programmed in MS Visual Studio 2005: Fortran Intel was used for the mathematical models, and Flash and VisSim for the gas turbine screens. The steam turbine screens were duplicated from the real plant control screens, and C# was the basis for the modules of the simulation environment called MAS (proprietary software of the IIE), where the instructor controls the simulation sessions (Fig. 10.2).

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Control Room Operation Station1 (OS1)

Operation Station2 (OS2)

Lan switch Maintenance node (MN)

Instructor Console (IC)

Fig. 10.1 Hardware architecture

Fig. 10.2 Main display of the instructor console (Partial view)

10.3

Modeling Methodology

10.3.1 Processes The processes models are a set of algebraic and differential equations obtained from the application of basic principles (energy, momentum and mass balances and well-known and proved relations). The IIE methodology may be summarized as: (a) Information of the process is obtained and classified, (b) The information is analyzed to create a conceptual model, (c) Reasonable simplifications to the system are made to get a simplified diagram, (d) Main assumptions are stated and justified, (e) The flows and pressures network (FPN) configuration is obtained, (f) Energy balances are programmed using generic models, (g) Equipment’s models out the limits of the hydraulic network are parameterized considering the generic models available in the simulator libraries or developing the appropriate models, (h) Local tests are performed and, if necessary, corrections to the models are

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made, (i) Integration between all the different systems and their controls are performed, ( j) Global tests are made with the proper adjustments, (k) Final acceptance tests are performed by the final user according to his own procedures.

10.3.2 Control The control models acquire and process the actions performed by the operator in the control room by manipulating the control screens. The control models are a reproduction of the actual logic of the plant.

10.4

Lube Oil Systems Description

The components of the gas turbine lubrication oil system (GTLOS) and the steam turbine lube oil system (STLOS) are: AC motor pumps, emergency DC motor pumps, lubrication oil tanks, electric oil heaters, lube oil coolers, oil mist eliminators (extractors), oil filters, and valves. Additionally, the GTLOS has a pressure control for the lubricated parts. The STLOS has oil ejectors, an oil conditioner, and a main pump mounted on the steam turbine shaft. The systems provide a continuous supply of filtered oil at the pressure and temperature required for lubrication. In normal operation conditions of the GTLOS, the oil is supplied from the lube oil tank to the bearings through the main pump; the backup pump operates when the main pump cannot keep operating requirements. An emergency DC pump is only used as an ultimate option to ensure that the gas turbine slows down safely when a loss of pressure in the oil system, indicated by the pressure in the header bearings, occurs. During start up and shut off periods, the steam turbine lube oil requirements are supplied by the auxiliary pump and, when the turbine is at rated speed, the main oil pump supplies oil to the bearings. For both systems the oil is taken from the main oil tank, passes through a water cooler, is directed to the bearings and finally flows back to the tank. The GTLOS is linked to the Gas Turbine Seal Oil System (GTSOS) through a valve that supplies seal oil to the generator when the seals pump fails.

10.5

Modeling of the Systems

A model is a mathematical representation of the behavior of the real system variables of the power plant. Models of oil systems, from the point of view of operation training, are not frequently reported in the literature because they belong

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to the companies that provide the simulators or training services, and therefore, it is proprietary information (Vieira et al. 2008). Nevertheless, some design and analysis simulation approaches for oil system may be found (L€uckmann et al. 2009; Flores et al. 2007; D’Souza and Childs 2002; Wasilczuk and Rotta 2008) (out of the training modeling approach). In the SCC, a model responds to the operator’s action in the same way that the real systems do (in tendency and time), at least as required by the ANSI norm. The IIE has adapted different public solution methods for both, linear and nonlinear equations (Newton–Raphson, Gaussian elimination and bisection partition search) and they are used depending on the structure of each particular model. The differential equations are numerically solved with one of various methods (e.g. Euler, trapezoidal rule with one or two corrections). Fundamental conservation principles were used for the modeling considering a lumped parameters approach and widely available and accepted empirical relationships. The independent variables are associated with the operator’s maneuvers (actions on valves, pumps, etc.) and with the control signals from the distributed control system (DCS). A generic model (GM) constitutes a standard tool with some built-in elements (in this case, routines), that represent an equipment or system and may facilitate its adaptation to a particular case. The GM, as developing tools, allows reducing the developing time of a simulator. The GS have developed a series of GM from single accessories as junction nodes or ejectors; complex equipments as air condensers, deaerators, or boilers; to numerical methods that have been listed elsewhere (Rolda´n-Villasana et al. 2009).

10.5.1 Flows and Pressures Modeling The flows and pressures generic model ( flupre) is derived from the momentum equation applied in each stream and the continuity equation for each node of a hydraulic network. The term of transient acceleration is neglected and the forces acting on the fluid are considered instantly balanced. A 1D model may be stated integrating the momentum equation along a stream: DP ¼ X Dt þ r g z

(10.1)

Here, P is the pressure, X the length, r the density, g the gravity acceleration, z the height, and t the viscous stress tensor that may be evaluated using empirical expressions for any kind of element. For example, for pumps it may be assumed that: 0

0

0

DP ¼ K1 w2 þ K2 w o þ K3 o2  r g z

(10.2)

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There, K’i are constants, w the mass flow, and o the angular speed of the pump. For non rotating elements the equation may be stated as: w2 ¼ K 0 r Apg ðDP þ rgzÞ

(10.3)

Ap is the aperture of the valves but it may also represent a variable resistance to the flow. Exponent g represents the characteristic behavior of a valve. Other components (such as turbines) may also be represented by Flupre. Thus, by applying the momentum equation on each piece of equipment and a mass balance on each node, a system of equations is stated where the flows and pressures are the unknown variables to be solved. Flupre may detect automatically the varying network topology and establish the proper set of equations.

10.5.2 Energy Balances An energy balance is made where heat exchangers exist and in nodes where a temperature or enthalpy is required to be displayed or to be used in further calculations. In the lube oil models, two types of heat exchangers were included: fluid-fluid exchangers and fluid-metals exchangers. The fluid (water and air) exchangers were modeled considering a heat flow: q ¼ U A DT

(10.4)

Here q is the heat flow, A the transfer area, T the temperature, and U the global heat transfer coefficient which depends on the fluid properties, the flow rates, and the construction parameters of the equipment. The characteristic temperature difference between hot and cold streams is the logarithmic mean temperature difference. So, for each fluid, an energy balance may be stated (considering the heat capacity Cp as a constant) to calculate the exit temperature:


q T o ¼ Ti  w Cp

 (10.5)

Subscripts o and i are for output and input, respectively. To avoid thermodynamic inconsistencies in the exchanger, e.g. if temperature crossing is detected, and the heat calculated by (10.4) is too high, the limiting heat, which depends on the cold and the hot streams, and the maximum heat permitted by the second law are identified. Heat is re-calculated arranging (10.5). Outlet temperatures are re-calculated. Clearly, an iterative procedure may be established in order to obtain a converged solution. The model for the metals cooled with oil was based on the assumption that turbine and generator metals are heated by friction and electric current. The temperature

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Fig. 10.3 Schematic metal temperature model

Tg qg qatm Tm

qf

Tf

from this generated heat Tg is represented by virtual temperatures (TI for the current and To for the speed) that are calculated in order to simulate these heating effects (Fig. 10.3). With this temperature Tg, the heat qg represents the transferred energy due the frictional forces and/or the generated electric current (Pasquantonio and Macchi 1976): qg ¼ qo þ qI ¼ ko ðTo  Tm Þ o0:4 þ kI ðTI  Tm ÞI2

(10.6)

So, the temperature of the compressor may be calculated by integration of next equation: dTm qg  qf  qatm ¼ dt m Cp

(10.7)

Being t the time, m the mass, the subscripts f and atm are for the fluid and atmosphere respectively. The heat flow is calculated with (10.4).

10.5.3 Capacitive Nodes Two kinds of capacitive nodes exist in the lube oil systems. The first one (thermal node) is part of the FPN whose pressure is calculated as explained before. In this case, because no phase change is simulated for the oil, temperature is calculated by integrating next equation (the state variable could be the enthalpy especially if a phase change is expected): dT ¼ dt

P

wi Ti  wo T qatm  m Cp m

(10.8)

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The mass of the lube oil in the tanks is obtained from a mass balance on the liquid phase: X dm X wo ¼ wi  dt

(10.9)

The volume of the liquid Vl is: Vl ¼

m r

(10.10)

The oil level is calculated with an appropriate correlation depending on the tank’s geometry. The second kind of capacitive node is one that is a boundary of the FPN. In this category are boilers, condensers, deaereators, and other equipment related with phenomena involving water/steam operations. The state variables depend on each particular case. This equipment was not used in the lube oil systems; only closed tanks with oil and air were modeled. The tank is considered to have two separated phases with heat transfer between them. The air pressure is calculated based on an ideal gas behavior: Pa ¼

ma R Ta Ma Va

(10.11)

Ma is the air molecular mass and R the ideal gas constant. Equations (10.8) and (10.9) apply also for the air phase. Humidity in the air is neglected. Extractors that maintain the vacuity in the tanks are formulated with (10.2).

10.6

Modeled Systems

In this section some particular considerations of each model are presented. Some simplifications are included in the generic models (e.g., density and heat capacity are constant). The control models associated with the systems were modeled in an independent way (the activation of the automatic actions and the alarms behavior were included in these models). Additional GM are employed to solve for the angular speed and current of the pump motors.

10.6.1 Gas Turbine Lube and Seal Oil Systems Model The GTLOS and GTSSO were initially simulated into a single module. For the tanks, the extractors were included. The bearing metals and the heat exchangers were considered. The dragging of the hydrogen into the oil was neglected.

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Mathematical instabilities appeared because the magnitude order of the flow rates is very different for the both oil networks. To avoid the numerical problems, two independent sets of equations were solved, one for each system. In Fig. 10.4 a schematic diagram of the networks is presented. The dotted lines represent the common flow rates calculated out of the flupre models. In Fig. 10.5 is presented the control screen of the GTLOS.

10.6.2 Steam Turbine Lube Oil Model The STLOS model includes pipelines, pumps, heat exchangers, rotor bearings and valves (Fig. 10.6). There were excluded the oil ejectors at the suction of the main pump resulting in taking suction directly from the main tank and including their pressure effects in the main oil pump operation curve. The operator monitoring and control screen is shown in Fig. 10.7.

10.7

Upling and Testing

An important task is to define the causal relation of the models, i.e. to specify for each model all the variables (inputs and outputs) that connect the mathematical models, the controls, the operator console, and the instructor console. The goal is to assure that all models are congruent. The variables are classified and added into a data base. This data base contains the variables declaration for all the simulator programs, parameters values, unit conversions, instruments ranges, remote functions, malfunctions, etc. Each model is tested, independently, off line without controls (open loop). In this stage some simplified controls are included to avoid problems of process instabilities; for example in a tank model it is necessary to control the level to avoid it becomes empty or shedding. The idea of the tests is to reproduce the design data at 100% of capacity. The input variables must have an initial value (initial condition). For the coupling process, the control models are integrated into the MAS without the process models and they are tested to verify its dynamics, including the control screens. The models are added individually according to a predefined order. An algorithm was developed to consider a sequence trying to minimize retarded information to avoid mathematical problems(Mendoza-Alegrı´a and Rolda´n Villasana 2008). For each model addition the initial condition is updated and some tests are performed to assure the coupling is successful. Adjustments are done to fix differences between the simulator and the real plant values. When the last model has been added and the 100% initial condition is ready, all the other initial conditions, down to the cold start initial condition, are obtained by operating the simulated plant. Full factory tests, developed by the final user, are applied to the integrated simulator. Then it is delivered to its final site with the customer.

Fig. 10.4 Gas turbine lube and seal oil systems flows and pressures network

130 E.J. Rolda´n-Villasana et al.

Lube Oil Systems Models for Real Time Execution. . .

Fig. 10.5 Gas turbine lube and seal oil systems control screen

10 131

132

Fig. 10.6 Schematic diagram steam turbine lube oil system

Fig. 10.7 Control screen of the steam turbine lube oil system

E.J. Rolda´n-Villasana et al.

10

Lube Oil Systems Models for Real Time Execution. . .

10.8

133

Results

As an example of the models scope, two scenarios are presented. Both were executed with the entire simulator coupled. No corrective action by the operator was made during the transients. The first one is related to the gas turbine. The customer provided plant data for an automatic start up procedure. The simulator results were compared with these data. In Fig. 10.8 a comparison between the expected temperatures and the simulator results is presented. The x axis is the sum of the gas turbine speed and the load of the gas turbine generator (note that the load is zero while the speed reaches its nominal speed of 377 rad/s). The selected variables were the temperatures of the control oil tank and the thrust bearing metal and oil (at the exit). The good agreement between the simulated results and the plant data is evident. The second transient consists in testing the steam turbine lube oil model under the actions reported in Table 10.1. The reported variables in Fig. 10.9 are the temperatures in the lube oil tank, at the exit of the cooler, of the metal of the third bearing, and the oil at the exit of the third bearing; and the aperture of the cooler control valve (simulated in the cooling water circuit system). From a qualitative point of view, the simulator results are in agreement with the expected behavior the plant would have if the same transients were applied on the real plant.

Temperature (°C )

100

80

60

40

20

0 0

200

Real Plant Brng Thrust

Simulator Brng Thrust

Real Plant Oil Brng Thrust

Simulator Oil Brng Thrust

Real Plant EHC Oil

Simulator EHC Oil

400

600

Speed (rad/s) + Charge (MW) Fig. 10.8 Gas turbine lube oil models results

800

1,000

134

E.J. Rolda´n-Villasana et al.

Table 10.1 Test events of the steam turbine oil models Time (s) Event 0 Simulation starts at 100% of load in steady sate with the lube oil cooler control valve stuck at a position of 33% (a malfunction) 30 The electric oil heater is turned on. The temperatures of the system (oil and metals) increase 830 The electric oil heater trips automatically when the oil temperature in the tank reaches 80 C. The temperatures in the tank and at the exit of the cooler stop augmenting The metal and oil temperatures at the exit of the bearing keep raising until an inflexion point is reached at 830 s 1,200 The steam unit trips due to high temperature in the turbine metals (the 3rd bearing metal temperature reaches 89 C). The metal and oil temperatures continue increasing because the turbine speed, although lowering, continues with a high value 1,290 The cooler control valve is released from its malfunction. The valve opens quickly trying to control the oil temperature (at 40 C). All the metal and oil temperatures descend

Fig. 10.9 Steam turbine lube oil models results

References Hoffman S (1995) A new era for fossil power plant simulators. EPRI J 20(5):20–27 Fray R, Divakaruni M (1995) Compact simulators can improve fossil plant operation. Power Eng 99(1):30–32 (ISSN 0032-5961, United States) Zabre E, Rolda´n-Villasana EJ, Romero-Jime´nez G, Cruz R (2009) Combined cycle power plant simulator for operator’s training. The World Congress on Engineering and Computer Science, San Francisco, pp 20–22 Mendoza-Alegrı´a Y, Rolda´n-Villasana EJ (2010) Oil systems modeling for an operators’ training combined cycle plant simulator. The world congress on engineering and computer science, ISBN: 978-988-17012-0-6, San Francisco, pp 20–22 Vieira L, Matt C, Guedes V, Cruz M, Castelloes F (2008) Optimization of the operation of a complex combined-cycle cogeneration plant using a professional process simulator.

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Lube Oil Systems Models for Real Time Execution. . .

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Proceedings of IMECE2008. ASME International mechanical engineering congress and exposition, October 31–November 6, 2008, Boston, pp 787–796 L€ uckmann JA, Vinicius AM, Barbosa RJ (2009) Analysis of oil pumping in a reciprocating compressor. Appl Therm Eng 29(14–15):3118–3123 Flores P, Ambrosio J, Claro JCP, Lankarani HM (2007) Study of the influence of the revolute joint model on the dynamic behavior of multibody mechanical systems modeling and simulation. Proceedings of the ASME 2007 International design engineering technical conferences & computers and information in engineering conference, Las Vegas, pp 367–379 D’Souza RJ, Childs DW (2002) A comparison of rotordynamic-coefficient predictions for annular honeycomb gas seals using three different friction-factor models. J Tribol 124–3:524–529 Wasilczuk Micha, Rotta Grzegorz (2008) Modeling lubricant flow between thrust-bearing pads. Tribol Int 41:908–913 Rolda´n-Villasana EJ, Cardoso Ma J, Mendoza-Alegrı´a Y (2009) Modeling methodology for operators training full scope simulators applied in models of a gas-turbine power plant. Memorias del 9o. Congreso Interamericano de Computacio´n Aplicada a la Industria de Procesos, 25 al 28 de agosto, Montevideo, pp 61–66 Pasquantonio FD, Macchi A (1976) Mathematical model and boundary conditions in stress analysis relating to steam turbine rotors, under transient operating conditions. Int J Numer Meth Eng 10(2):345–360 Rolda´n-Villasana EJ, Mendoza-Alegrı´a Y, Zorrilla-Arena S, Jorge J, Cardoso G, Jesu´s Ma, CruzCruz R (2008) Development of a gas turbine full scope simulator for operators’ training. European Modelling Symposium, Second UKSim European Symposium on Computer Modelling and Simulation, UK Simulation Society, 6–8 September, Liverpool, England, 2008

Chapter 11

Stochastic Analysis and Particle Filtering of the Volatility Hana Baili

11.1

Introduction

Let S ¼ ðSt Þt2IRþ be an IRþ -valued semimartingale based on a filtered probability space ðO; F ; ðF t Þt2IRþ ; IPÞ which is assumed to be continuous. The process S is interpreted to model the price of a stock. A basic problem arising in Mathematical Finance is to estimate the price volatility, i.e., the square of the parameter s in the following stochastic differential equation dSt ¼ mSt dt þ sSt dW t where W ¼ ðW t Þt2IRþ is a Wiener process. It turns out that the assumption of a constant volatility does not hold in practice. Even to the most casual observer of the market, it should be clear that volatility is a random function of time which we denote st2. Itoˆ’s formula for the return yt ¼ logðSt =S0 Þ yields
dyt ¼

m

 s2t dt þ st dW t 2

y0 ¼ 0

(11.1)

The main objective is to estimate in discrete real-time one particular sample path of the volatility process using one observed sample path of the return. As regards the drift m, it is constant but unknown. Under the so-called risk-neutral measure, the drift is a riskless rate which is well known; actually one finds that m does not cancel out, for instance, when calculating conditional expectations in a filtering problem. For this argument no change of measure is required, we work directly in the original measure IP, and m has to be estimated from the observed sample path of the return as well. H. Baili (*) E´cole Supe´rieure d’E´lectricite´, Department of Signal Processing and Electronic Systems, 3 rue Joliot Curie, Plateau de Moulon, 91192 Gif sur Yvette, France e-mail: [email protected] S.‐I. Ao et al. (eds.), Intelligent Automation and Systems Engineering, Lecture Notes in Electrical Engineering 103, DOI 10.1007/978-1-4614-0373-9_11, # Springer Science+Business Media, LLC 2011

137

138

11.2

H. Baili

A Model for the Stochastic Volatility

Let ðzt Þt2IRþ be an arbitrary IR-valued process; at the moment, this is not the unknown process st2 of instantaneous volatility. Let us assume prior information about the process zt: wide sense stationarity and a parametric model for its covariance function gðtÞ ¼ D expðajtjÞ

t 2 IR

for some constants D, a > 0. Then the spectral density of zt is given by the formula 1 GðoÞ ¼ 2p

ð gðtÞ expðjotÞdt ¼ IR

1 2D a 2p o2 þ a2

pffiffiffiffiffiffiffi where j ¼ 1. The spectral density G(o) may be rewritten as GðoÞ ¼ where Hð joÞ ¼

 2 1 Hð joÞ 2p  Fð joÞ 

o 2 IR

pffiffiffiffiffiffiffiffiffiffi 2D a and Fð joÞ ¼ jo þ a. Notice now that FðsÞ ¼

HðsÞ FðsÞ

s 2 IR

represents the transfer function of some temporally homogeneous linear filter; this filter is furthermore stable as the root of F(s) is in the left half-plane of the complex variable s. Recalling that 1/2p is the spectral density of a white noise with unit intensity, we come to the conclusion that zt  IE½zt  may be considered as the response of the filter whose transfer function is F(s), to a zero-mean white noise with unit intensity. The differential equation describing such a filter is _ þ auðtÞ ¼ uðtÞ

pffiffiffiffiffiffiffiffiffiffi 2D a wðtÞ

where w(t) and u(t) are respectively the input and the output of the filter. Setting m ¼ IE½zt  and zt  m ¼ uðtÞ, the process zt solves the following SDE dzt ¼ aðzt  mÞ dt þ

pffiffiffiffiffiffiffiffiffi 2Da dW t

Let the IR-valued process ð~zt Þt2IRþ solve the SDE d~zt ¼ a~zt dt þ

pffiffiffiffiffiffiffiffiffi 2Da dW t

t>0

11

Stochastic Analysis and Particle Filtering of the Volatility

139

with given initial condition ~z0 . ~zt is wide sense stationary with zero mean and correlation function zðtÞ~ zðt  tÞ ¼ D expðajtjÞ r ~z ðtÞ ¼ IE½~ Consider now the process j~zt j, written xt. Then xt is a solution of dxt ¼ axt dt þ

pffiffiffiffiffiffiffiffiffi 2Da dW t

t>0

(11.2)

with reflection on the boundary {0} of its state space IRþ . Starting from any fixed point strictly greater than zero, xt reaches this boundary by a predictable stopping time with finite expectation because of the negative sign of the drift. The initial condition x0 is a random variable with known distribution since x0 ¼ j~z0 j. It is worthwhile to note the ergodicity of the Markovian process xt, with stationary distribution density   2 x2 pðxÞ ¼ pffiffiffiffiffiffiffiffiffi exp  2D 2pD

x 2 IRþ

(see pages 55–57 of Skorokhod 1989). This is beyond our expectation in view of the required wide sense stationarity. It follows that for each x 2 IRþ , for any bounded continuous function f on IRþ ð lim IEx ½ f ðxt Þ ¼

t!1

f ðxÞpðxÞ dx IRþ

and in particular 2D lim IEx ½xt  ¼ pffiffiffiffiffiffiffiffiffi 2pD

t!1

Obviously, the second order moment for xt coincides with that of ~zt , namely   IE x2t ¼ IE ~z2t ¼ D Let us compute rx(t), the correlation function of xt, for t 6¼ 0 IE½ xðtÞxðt  tÞ ¼ IE½ j~ zðtÞ~ zðt  tÞj  ¼ IE½~ zðtÞ~ zðt  tÞIPfeven passage number by 0g  IE½~ zðtÞ~ zðt  tÞIPfodd passage number by 0g ¼ C expðajtjÞ for some constant 0 < C < D. Note the discontinuity of rx(t) at t ¼ 0. We shall freely call the process xt or equivalently the SDE (11.2) our stochastic volatility model. We just

140

H. Baili

~ since it is independent have to denote the Wiener process in (11.2) differently, say W, of the Wiener process in (11.1). It should be noted that this type of correlation function:  C expðajtjÞ t 6¼ 0 a>0 0>1 at different even values of the power m. It is clear that rmax gets lower with increasing values of m>2 in accordance with Eq. 26.27.

26

Soft Vector Quantization with Inverse Power-Function. . .

347

Table 26.1 Relative noise energy of soft VQ with large codebooks L¼1,024 M 2 4 6 8 m 1

lim ^a

lim rmax

L!1

L!1

0 1 L4

L 1 pffiffiffi  L 2 rffiffiffi 2 3 L  3 2 rffiffiffi 3 4 L  4 3

 16 L 2  18 L 3  1 2L m m2 1

  m2 2  L 2=m  m m2 1

L¼2,048

^ a

rmax

^a

rmax

0 0.177

1,024 16

0 0.149

2,048 22.63

0.354

5.333

0.315

6.720

0.482

3.224

0.442

3.834

1

1

1

1

Fig. 26.1 Quantization noise energy of the proposed soft VQ relative to that of hard VQ at different values of the power m

26.4

Experimental Setup and Results

This section presents the experimentation we conducted to attest the benefits we claimed – in the introduction above – of our proposed soft VQ for machine-learning systems that include a VQ module. For this challenge, we have chosen the task of Optical Character Recognition (OCR) for Arabic script (Attia et al. 2004) which is an instance of a broader family of cursive scripts including Persian, Urdu, Kurdish, etc. This is a 4-decade old tough pattern recognition problem as the connected characters in the script need to be both identified and segmented simultaneously. It is evident in the literature that the most

348

M. Attia et al.

effective methodology for dealing with such a problem is the HMM-based one (Attia et al. 2009; Bazzi et al. 1999; El-Mahallawy et al. 2008; Gouda et al. 2004; Kanungo et al. 1999; Khorsheed 2007; Mohamed and Gader 1996; Rashwan et al. 2007). Among the many recent attempts made on this problem, we have selected CleverPage# to experiment with.3 CleverPage# is an ASR4-like HMM5-based Arabic Omni Type-Written OCR system that is reported to achieve the highest accuracy in this regard (Attia et al. 2009; El-Mahallawy et al. 2008). The most characterizing innovation in this OCR system that puts CleverPage# ahead of its rivals is its lossless recognition features which are autonomously normalized horizontal differentials encoded in 16-component vectors sequence. Each features vector is computed to differentially encode the pixels pattern in each single-pixel width slice (i.e. frame) included within the right-to-left horizontally sliding window. Given a sequence of such features, one can retrieve the shape of the digitally scanned type-written script with only a scaling factor along with an inevitable limited digital distortion. Each single-pixel frame of a given Arabic word is assumed to have a limited maximum number of vertical dark segments (4 segments are found to be enough). Each of these segments is coded into four components differentially representing the topological and agglomerative properties of the segment as follows: 1st component: The segment’s center of gravity with respect to that of the previous frame. 2nd component: The segment’s length relative to that of the previous frame. 3rd and 4th components: The orders of the most bottom, and the top segments in the preceding frame which are in 8-connection with the current segment. Special negative codes are given in case of non-connected segments. Empty segments are padded by zeros. The dimensionality of this features vector is 16 ¼ 4 segments 4 components per segment. While half of these components are sharply discrete in nature, the others are analog, which made it quite hard to find Gaussian mixtures that properly represent that kind of hybrid data. So, discrete HMMs rather than GMMs have been resorted to as the recognition vehicle of this system (Attia et al. 2009; El-Mahallawy et al. 2008; Rashwan et al. 2007). This made CleverPage# an excellent candidate for our experimentation especially as its producer6 was generous enough to allow us set up two versions of this discrete HMM-based Arabic OCR system with L ¼ 2,048: one with hard-deciding

3 See http://www.RDI-eg.com/technologies/OCR.htm for a details on CleverPage#, and see also the last section of the web page http://www.RDI-eg.com/technologies/papers.htm for downloading references (Attia et al. 2004), (El-Mahallawy et al. 2008), and (Rashwan et al. 2007) in the references list at the end of this chapter. 4 Automatic Speech Recognition 5 Hidden Markov Models 6 RDI; www.RDI-eg.com

26

Soft Vector Quantization with Inverse Power-Function. . .

349

Table 26.2 Relative noise energy of soft VQ with large codebooks Error rate of assimilation test Error rate of generalization test WERA Hard VQ 3.08%

Soft VQ 3.71%

CERA Hard VQ 0.77%

Degradation due to soft VQ –20.45%

Soft VQ 0.93%

WERG Hard VQ 16.32%

Soft VQ 13.98%

CERG Hard VQ 4.08%

Soft VQ 3.50%

Enhancement due to hard VQ +14.34%

VQ, and the other with our proposed soft VQ with m ¼ 8. This is an empirical selection of the value of m that compromises between both minimal quantization noise energy of soft VQ relative to that of hard VQ, along with soft enough distributions of our soft VQ scheme. The training data of both setups covers ten different popular Arabic fonts with six sizes per each font. It contains 25 LASER-printed pages per each size of each font with all the pages scanned at 600dpi B&W along with the correct digital text content of each page (El-Mahallawy et al. 2008). Then we challenged both versions with two sets of test data: assimilation test data and generalization test data. The assimilation test data consists of 5 test pages for each of the 60 (font, size) pairs represented in the training phase. Of course the pages themselves do not belong to the training data. These assimilation test pages are produced in the same conditions as the training ones. This provides the favorable runtime conditions of least runtime variance from the training conditions. The generalization test data, on the other hand, are sample pages picked randomly from some Arabic paper-books that are scanned also at 600 dpi.7 Obviously, there is no control on the fonts or sizes used in these pages. The tilting distortion and the printing noise are also quite apparent. Of course, this provides the less favorable runtime conditions of more considerable variance from the training conditions. Table 26.2 below compares the measured word error rates of both versions with each of the two test data sets. In this table, CER stands for Character Error Rate while WER stands for Word Error Rate. Under the assumption of a single character error per word - which is valid in our case - the relation WER h  CER; CER > > > > > > > < FðQ Þ k FðQ Þ 1 2 FðQÞ ¼ > catch c in FðRÞ finally S > > > > > local x in FðRÞ > > > : skip

if Q ¼ next R or Q ¼ unless c next R if Q ¼ Q1 k Q2 if Q ¼ catch c in R finally S if Q ¼ local x; c in R Otherwise

To simplify the transitions, a congruence relation  is defined. Following (Saraswat 1993), we introduce the standard notions of contexts and behavioural equivalence. Informally, a context is a phrase (an expression) with a single hole, denoted by [  ], that can be plugged in with processes. Formally, processes context C is defined by the following syntax: C ::¼ ½

j

when c do C þ M

j

CkC

j

local x in C

j j j

unless c next C delay C for d !C

j j j

catch c in C finally C next C ?C

where M stands for summations. : Two processes P and Q are equivalent, notation P ¼ Q, if for any context C, : : P ¼ Q implies C½P ¼ C½Q. Let  be the smallest equivalence relation over processes satisfying: 1. P  Q if they only differ by a renaming of bound variables 2. P k skip  skip k P  P

386

3. 4. 5. 6. 7.

M. Gerardo and M. Sarria

PkQ  QkP next skip  skip local x in skip  skip local x y in P  local y x in P local x in next P  next (local x in P) We extend  to configurations by defining hP, c, ti  hQ, c, ti iff P  Q.

29.4

Application

In this section we illustrate the application of the rtcc calculus by modeling two real-time scenarios.

29.4.1 Railway Level Crossing The “railway level crossing” is a problem proposed by S. Schneider in Schneider (2000) thus: One road and one railway line cross each other, and as usual there is a gate which can be lowered to prevent cars crossing the railway. If the gate is raised, then cars can freely cross the track. Trains can cross the road regardless of whether the gate is up or down. There should never be a train and a car on the crossing at the same time.

This problem can be modeled with the following main processes: Car, Train, Gate. Process Train supply the stream of trains: def

Train ¼ next Train þ tellðnear ¼ trueÞ k next tellðout ¼ trueÞ k next2 Train A train moves constantly until it is approaching to the crossing; we model this by choosing non-determiniscally continue with the process Train the next time unit (meaning the train is far from the crossing) or posting the constraint near ¼ true into the store. In moment it is approaching it sends the signal (post the constraint) in order to get the gate start down. Once the train pass the crossing entirely (we assume it takes a time unit) it sends another signal in order to get the gate start up. Process Car supply the stream of cars: def

Car ¼ catch gate ¼ down in ð! tellðmove ¼ trueÞÞ finally tellðstop ¼ trueÞ k unless gate ¼ down next Car

29

Application of the Real-Time Concurrent Constraint Calculus

387

A car is moving until the gate is down. In this case it has to stop and wait until the gate is up again in order to move. To simplify the model we assume uniform linear motion (no acceleration) for both cars and trains. The following process models the gate: def

Gate ¼ ! when near ¼ true do delay tellgate ¼ downÞ for 20 k ! when out ¼ true do delay tellgate ¼ upÞ for 20 When the train gives a signal, Gate captures it and performs the action of liftingup or lifting-down the gate. We model the duration of this actions by delaying the signals given to the cars for 20 ticks. The reader may notice that there would be time units where neither near ¼ true nor out ¼ true cannot be inferred from the store. This is not a security issue since this means that train is not coming or passing so cars may cross the railway (since the gate ¼ down signal is not in the store). To model the whole system we simply launch the process Train k Car k Gate This real-time problem was previously modeled in Alpuente et al. (2006) using another real-time ccp-based calculus called tccp (de Boer et al. 2000).

29.4.2 Factor Oracle Interactive systems are those systems in which their components exchange elements. An improvisation situation involving more than one machine, for instance, can be seen as an interactive system where a machine performs some action, the others learn his method and features, organize them in a model, and generate and perform another action consistent with the one of the first machine. A factor oracle is a finite state acyclic automaton introduced in Allauzen et al. (1999) for string matching on fixed texts. If a word is a sequence w ¼ s1s2. . . sn of letters (belonging to an alphabet S), then the factor oracle is a data structure representing at least all the factors of w. A sequence p ∈ S * is a factor of w iff w can be written w ¼ qpr, with q, r ∈ S* . The factor oracle is built from a word w ¼ s1s2. . . sn by creating n + 1 nodes (one for every si with i ∈ [1. . n] plus the initial state), and inserting an arrow labelled with symbol si linking the node i with the node i + 1 (these are called factor links). Depending on the structure of the word, other arrows from state i to a state j (0  i < j  n) are added to the automaton constituting the remaining factors. Finally, backwards links (called suffix links) are added to the automaton from a state i to a state j (0  j < i  n) holding the possibility of traverse all the factors in

388

M. Gerardo and M. Sarria a a

0

a

1

b

2

b

3

b

4

a

5

a

6

a

b

Fig. 29.2 Factor oracle automaton for w ¼ abbbaa.

a single path (suffix links connect repeated patters of w). Figure 29.2 shows the factor oracle of the word abbbaa. The dot-lined arrows represents the suffix links. In Allauzen et al. (1999) an algorithm for constructing the automaton on-line was also presented. This allows to build a factor oracle by reading the letters of a word one by one from left to right. With the on-line construction algorithm many applications can be modeled. For example in a concurrent learning/improvisation situation it is possible to construct a machine improviser. We can think of this improviser as a system consisting in three phases running concurrently: learning, improvisation and performance. In the learning phase the on-line construction algorithm is used to build the factor oracle during the performance of a machine. A word can denote many structures, the simplest is, perhaps, a sequence of actions, one action per time. Then every action the machine performs is an input for the algorithm and, ultimately, a new state in the oracle. In the improvisation phase, the system has to make a choice of what path should be traversed in the current automaton. The automaton used must be stable, that is, all factor and suffix links until the last state added must be present. We assume that the choice is non-deterministic in order to make a good improvisation. Finally, in the performance phase the sequence of actions of the path choose is performed. This model in rtcc is built with processes for each phase. The learning phase is modeled with a process posting constraints which define the automaton. Variable si denotes the label of the factor link connecting a state i  1 to a new state i. Process Learni adds the new state i and build the factor and suffix links with this new state. def

Learni ¼ ! tellðdi1;si ¼ iÞ k BuildFOi ðSi1 Þ Variable dk;si denotes the state reached by following a factor link si from state k in the automaton. Variable Si denotes the state reached by following a suffix link from state i. The process BuildFOi adds all the factor links taking into account the state i, and also adds the suffix links from it.

29

Application of the Real-Time Concurrent Constraint Calculus

389

def

BuildFOi ðkÞ ¼ when k 0 do unless si 2 f k nextð! tellðsi 2 f k Þ k ! tellðdk;si ¼ iÞ k BuildFOi ðSk ÞÞ k when k ¼ 1 do ! tellðSi ¼ 0Þ k when k 0 ^ si 2 f k do ! tellðSi ¼ kÞ Variable fk denotes the set of labels of all currently existing factor links going from k. Note that the automaton is built from latter states to the first ones. This can be a problem because the system could be too slow and there is the possibility of not building the whole factor oracle (recall that the duration of a time unit is limited and in each time unit i the automaton is bigger than the one in time unit i  1, so the store is also bigger which, normally, increase the time spent for posting constraints). We can avoid this by taking into account the current tick and execute the process if there is time, but if time is up then the construction of the factor oracle must continue in the next time unit. def

BuildFOi ðkÞ ¼ when k 0 do unless si 2 f k nextð! tellðsi 2 f k Þ k ! tellðdk;si ¼ iÞ k delay tellðtimeup ¼ 1Þ for t k catch timeup ¼ 1 in BuildFOi ðSk Þ finally nextðBuildFOi ðkÞÞ k when k ¼ 1 do ! tellðSi ¼ 0Þ k when k 0 ^ si 2 f k do ! tellðSi ¼ kÞ Variable timeup is the signal that specifies the nearly end of the time unit; we assume that t is the maximum tick in the time unit allowing to post the constraint timeup ¼ 1 and querying it. A machine is modeled as a process performing some action p every time unit and giving the signal for building the automaton at state j (the number of the current action). def

Machinej ¼

X

ð! tellðsj ¼ pÞ k tellðgo ¼ jÞ k nextðMachinejþ1 ÞÞ

p2S

The following improvisation process Impro defines the improvisation phase from state k of the automaton by choosing non-deterministically whether to perform an action (denoting by outputting the symbol sk + 1) or to follow a suffix link Sk and then perform an action (also chosen non-deterministically and denoting with symbol s 2 f Sk ). When Sk ¼ 1 there is only one choice: to perform the action symbolized by sk + 1.

390

M. Gerardo and M. Sarria def

ImproðkÞ ¼ when Sk ¼ 1 do nextðtellðout ¼ skþ1 Þ k Improðk þ 1ÞÞ k ðwhen Sk 0 do ðnextðtellðout ¼ skþ1 Þ k Improðk þ 1ÞÞÞ þ when Sk 0 do X nextð when s 2 f Sk do ðtellðout ¼ sÞ k ImproðdSk ;s ÞÞÞÞ s2S

Since the learning and the improvisation processes can run concurrently, we must guarantee that the improvisation works with a subgraph completely built. For this we define a process that synchronize the two phases on Si. def

Synci ¼ when Si1 1 ^ go i do ðLearni k nextðSynciþ1 ÞÞ k unless Si1 1 ^ go i nextðSynci Þ Finally, the whole system is modeled by launching all processes and initializing the first state. The parameter of the process System represents the number of actions that must be performed before starting the improvisation phase. def

Systemn ¼ ! tellðS0 ¼ 1Þ k Machine1 k Sync1 k ! when go ¼ n do ImproðnÞ A model using factor oracles for musical interactive systems was proposed in Assayag and Dubnov (2004), and built using the ntcc calculus in Rueda et al. (2006). The model presented here is similar to that model (finally, rtcc is an extension of ntcc and it remains all constructs), except for the fact that the model in this section is more realistic in the sense that it always consider the execution time of processes.

29.5

Concluding Remarks

In this paper we described the operational semantics of the rtcc calculus. This calculus belongs to the ccp family and is an strict extension of the ntcc calculus. rtcc extends ntcc to allow modeling systems with real-time behaviour. In order to guarantee real-time behaviour the operational semantics has a more realistic notion of time than any other ccp -based formalism and includes a transition system with support for expressing amount of resources and time allowance. We also illustrated the potentiality of the rtcc calculus with two applications: a railway level crossing problem and the factor oracle. Previously in Perchy and Sarria (2009) we showed the musical expressiveness of the rtcc calculus by modeling musical dissonances.

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References Allauzen C, Crochemore M, Raffinot M (1999) Factor oracle: a new structure for pattern matching. In: Conference on current trends in theory and practice of informatics. Springer-Verlag, London, pp 295–310 Alpuente M, Gallardo MM, Pimentel E, Villanueva A (2006) Verifying real-time properties of tccp programs. J Univers Comput Sci 12(11):1551–1573 Assayag G, Dubnov S (2004) Using factor oracles for machine improvisation. Soft Comput 8 (9):604–610 Bre´mond-Gre´goire P, Lee I (1997) A process algebra of communicating shared resources with dense time and priorities. Theor Comput Sci 189(1–2):179–219 de Boer FS, Gabbrielli M, Meo MC (2000) A timed concurrent constraint language. Info Comput 161(1):45–83 de Boer FS, Pierro A Di, Palamidessi C (1995) Nondeterminism and infinite computations in constraint programming. In: Selected papers of the workshop on topology and completion in semantics. Theoretical Computer Science, vol 151. Chartres, Elsevier, pp 37–78 Hoare CAR (1985) Communicating sequential processes. In: Prentice-Hall international series in computer science. Prentice Hall, Englewood Cliffs Milner R (1980) A calculus of communicating systems. In: Lecture notes in computer science. Springer-Verlag, Berlin/New York Palamidessi C, Valencia F (2001) A temporal concurrent constraint programming calculus. In: Seventh international conference on principles and practice of constraint programming. Lecture notes in computer science, vol 2239. Springer-Verlang, London, pp 302–316 Perchy S, Sarria G (2009) Dissonances: brief description and its computational representation in the rtcc calculus. In: 6th sound and music computing conference (SMC2009), Porto, Portugal Rueda C, Assayag G, Dubnov S (2006) A concurrent constraints factor oracle model for music improvisation. In: XXXII conferencia Latinoamericana de informa´tica CLEI 2006, Santiago de Chile Saraswat VA (1993) Concurrent constraint programming. In: ACM doctoral dissertation award. MIT, Cambridge, MA Sarria G (2010) Improving the real-time concurrent constraint calculus with a delay declaration. In: Lecture notes in engineering and computer science: proceedings of the world congress on engineering and computer science WCECS 2010. San Francisco, California. Newswood Ltd, Hong Kong, pp 9–14 Sarria G, Rueda C (2008) Real-time concurrent constraint programming. In: 34th Latinamerican conference on informatics (CLEI2008), Santa Fe, Argentina Schneider S (2000) Concurrent and real-time systems: the CSP approach. Wiley, Chichester/ New York

Chapter 30

Teaching and Learning Routing Protocols Using Visual Educational Tools: The Case of EIGRP Jesu´s Expo´sito Marquina, Valentina Trujillo Di Biase, and Eric Gamess

30.1

Introduction

Visual learning is the use of graphics, images and animations to enable and enhance learning. It is a proven method in which ideas, concepts, data, and other information are associated with images and animations, resulting in an easier and more effective approach of transmitting skills. Students can understand theoretical concepts much easier if they can see them, or interact with them as in the real life. Visual learning uses methods that help students to open their minds and think graphically. For these reasons, the GUI (Graphical User Interface) is one of the most important parts of any didactic tool. It is the boundary between the application and users. It can be seen, it can be heard, and it can be touched. The piles of software code are invisible, hidden behind the screen, speaker, keyboard, and mouse. According to Galitz (2007), the goal of interface design is to make the working with a computer easy, productive, and enjoyable. These characteristics present in a teaching and learning application makes it extremely powerful and efficient to bring knowledge to users. As networking systems are becoming more complex, new curricula and learning tools are needed to help students to acquire solid skills about networking technology. In this paper, we discuss our teaching and learning experiences of routing protocols, specifically with EIGRP (Doyle and Carrol 2005; Pepelnjk 2000) (Enhanced Interior Gateway Routing Protocol), using visual educational applications, and we report how these applications can enhance, ease, and make the experience much more natural.

J.E. Marquina (*) Escuela de Computacio´n, Universidad Central de Venezuela, Los Chaguaramos 1040, Caracas, Venezuela e-mail: [email protected] S.‐I. Ao et al. (eds.), Intelligent Automation and Systems Engineering, Lecture Notes in Electrical Engineering 103, DOI 10.1007/978-1-4614-0373-9_30, # Springer Science+Business Media, LLC 2011

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To do so, we present some well-known tools (Packet Tracer, Dynamips/GNS3, and OPNET IT Guru) and a new application (Easy-EIGRP). We focus on those tools’ support to teach some advanced EIGRP concepts, such as: successors, feasible successors, composed metric, routing table, and topology table. The rest of the paper is organized as follows. In Sect. 30.2, we briefly introduce the Enhanced Interior Gateway Routing Protocol. Section 30.3 presents the CLI, a popular method to configure Cisco’s routers. Sections 30.4, 30.5, 30.6 and 30.7 present visual applications (Packet Tracer, Dynamips/GNS3, OPNET IT Guru, and Easy-EIGRP, respectively) for the teaching and learning of EIGRP. Conclusions and future work are discussed in Sect. 30.8.

30.2

Enhanced Interior Gateway Routing Protocol

As its name suggests, EIGRP is an enhanced version of IGRP (Interior Gateway Routing Protocol), an obsolete routing protocol that was developed by Cisco Systems. EIGRP (Doyle and Carrol 2005; Pepelnjk 2000) is an advanced distancevector protocol that implements some characteristics similar to those of link-state protocols. Some Cisco documentation refers to EIGRP as a hybrid protocol. EIGRP advertises its routing table to its neighbors as distance-vector protocols do, however it uses the Hello Protocol and forms neighbor relationships similar to link-state protocols. EIGRP sends partial updates when a metric or the network topology changes. It does not send full routing-table updates in periodic fashion as distancevector protocols do. EIGRP is a classless protocol that permits the use of VLSMs (Variable Length Subnet Masks) and supports CIDR (Classless Inter-Domain Routing) for a scalable allocation of IP addresses. EIGRP used five types of packets (Hello, Acknowledgment, Update, Query, and Reply) identified by the protocol number 88 in the IP header: • Hello: EIGRP sends hello packets in the neighbor discovery and recovery process. These packets are multicast to 224.0.0.10 and use unreliable delivery. • Acknowledgment (ACK): This packet acknowledges the reception of an update, query, or reply packet. It is a hello packet with no data. ACKs are unicast and use unreliable delivery. • Update: EIGRP uses update packets to propagate routing information about subnets. A router unicasts update packets to newly discovered neighbors; otherwise, it multicasts update packets to 224.0.0.10 when a link or metric changes. • Query: EIGRP sends query packets to find an alternate route to a subnet. Query packets can be unicast or multicast. • Reply: EIGRP sends reply packets to respond to query packets. Reply packets usually provide a feasible successor to the sender of the query. They are always unicast to the sender of the query packet. EIGRP uses a composite metric where bandwidth, delay, load, and reliability are weighted by scale values, also known as K-values (K1, K2, K3, K4, and K5).

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It is one of the most complex routing protocol’s metric, and it is seldom understood by students. K-values are integer values that can vary between 0 and 255. For K5 ¼ 0:   K2  bandwidth þ K3  delay metric ¼ 256  K1  bandwidth þ 256  load For K5 6¼ 0: 

K2  bandwidth metric ¼ 256  K1  bandwidth þ þ K3  delay 256  load 



K5 K4 þ reliability

DUAL (Diffusing Update Algorithm) is the algorithm used to obtain loopfreedom at every instant throughout a route computation. It allows all routers involved in a topology change to synchronize at the same time. Routers that are not affected by topology changes are not involved in the recomputation. The DUAL finite state machine tracks all subnets advertised by all neighbors. DUAL selects routes to be inserted into a routing table based on feasible successors. A successor is a neighboring router used for packet forwarding that has a least cost path to a destination that is guaranteed not to be part of a routing loop. When there are no feasible successors but there are neighbors advertising reachability to the affected destination, a recomputation must occur. This is the process where a new successor is determined. When a topology change occurs, DUAL will check for feasible successors. If there are feasible successors, it will use the best it finds in order to avoid any unnecessary recomputation.

30.3

Cisco Command-Line Interface

The CLI (Command-Line Interface) is the oldest user interaction style. It requires users to type commands into a terminal emulator where all results are shown in plain text. The command-line style is powerful, and it is the preferred methods used by expert administrators to configure Cisco’s devices. However, the CLI is not very suitable to teach routing concepts to novice students since (1) they will have to remember the commands, (2) the syntax of commands can be complex, and (3) it is not possible to visualize dynamic changes in real-time (e.g., the variation of the throughput or the discovery/recovery of neighbors). It is also very prone to typing errors that can lead to novice user frustration, resulting in an application with poor usability and pedagogy to teach and learn. It is obvious that the CLI is not focused on the teaching and learning of networking topics. It is just an interface to manage Cisco’s devices, therefore, it

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does not possess the necessary features to ease the teaching and learning of routing protocols like EIGRP. Important concepts, such as the finite state machine, are not present in the CLI to study and understand the operational details of the protocol. Warnings about new and lost adjacencies to neighbors are limited to a screen printing without animations, icons, maps and clouds, as in other visual applications.

30.4

Cisco Packet Tracer

Cisco Packet Tracer is a powerful didactic application that allows students to experiment with networking concepts through a virtual laboratory. It is freely distributed to CCNA (Deal 2008) (Cisco Certified Networking Associate) and CCNP (Edwards et al. 2005) (Cisco Certified Networking Professional) students as an integral part of the Cisco Networking Academy comprehensive learning experience. Packet Tracer provides simulation, visualization, authoring, assessment, and collaboration capabilities to facilitate the teaching and learning of complex networking technologies by visually simulating virtual networking environments (Frezzo et al. 2009; Janitor et al. 2010). With Packet Tracer, students can easily build their network topologies in a visual way by dragging, placing, connecting, and clustering virtual network devices such as hubs, switches, routers, workstations and servers. Once placed in the workspace, students can customize their virtual networking devices. For example, they are allowed to add additional cards (e.g., WIC-2 T, NM-1FE-TX, etc.) to modular routers such as a Cisco 2811. If a router does not allow users to add or remove extension cards while it is powered on, Packet Tracer will force the students to power off the router before performing the change, just to remind them that they can damage the router if they do not follow a strict procedure. To connect virtual networking devices, Packet Tracer offers a wide variety of connections, such as straight-through and cross-over UTP cables. If students do not use the correct connection, the experiment will not work properly and troubleshooting will be necessary. Network devices (switches or routers) can be configured by students just by double-clicking their icon and entering the commands (in the same way they will enter them in real devices) in the CLI tab of the window that will appear. At the moment of the writing of this paper, the last version of Packet Tracer (version 5.3) supports RIP, OSPF, EIGRP and BGP (limited to basic EBGP). Not all the EIGRP commands are implemented in this version, but most of the usual ones are. For example, users can verify which interfaces of a router are running EIGRP (show ip eigrp interfaces), or can see the important tables maintained by the protocol such as the neighbor table (show ip eigrp neighbors), the topology table (show ip eigrp topology), and the complete topology table (show ip eigrp topology all-links). Packet Tracer also supports the customization of the K-values (metric weights 0 K1 K2 K3 K4 K5) of the metric, but authentication and most of the EIGRP debugging commands are not implemented for now.

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Starting with the release of Packet Tracer 5.0, the Multiuser Capability (Smith and Bluck 2010) was introduced which allows students to cross-connect their Packet Tracer applications together and create one big topology. So now it is possible to create a challenging EIGRP scenario where each student is responsible for its own part of the topology, while they are trying to achieve a goal together – a working big EIGRP simulated network. This kind of practice will be almost impossible with real routers for reasons of cost. Packet Tracer has two operational modes: real-time and simulation mode. The real-time mode simulates a real environment, with the same speed of the simulated networks and protocols, in a similar way of real situations. In simulation mode, Packet Tracer displays the actual data exchange between devices. Each packet, or frame, that carries some data is displayed as a small envelop moving on links between devices. Users can set a filter to limit the study to a particular protocol such as EIGRP and can see all the relevant information of the EIGRP packets (Hello, Acknowledgment, Update, Query, and Reply) based on the different layers of the OSI model, similarly to using a packet analyzer (sniffer) in a real network.

30.5

Dynamips and GNS3

Dynamips1 is a free open-source emulator for Cisco Systems routers than runs on traditional PC with Windows, Linux or MacOS X. It can emulate Cisco 1700 series, 2600 series (2610 to 2650XM, and 2691), 3600 series (3620, 3640, and 3660), 3700 series (3725 and 3745), and 7200. In other words, Dynamips allows students to create virtual routers that run a real Cisco IOS (Internetwork Operating System) by using the PC resources. Since the IOS is a commercial product, students will have to legally get an IOS copy to use Dynamips. GNS32 is a front-end for Dynamips, that is, it is a graphical application that allows users to visually create their network topology based on Cisco Systems routers just by dragging and clicking as they do in Packet Tracer. Most of the WICs (WAN Interface Cards) and NMs (Network Modules) are emulated by Dynamips, so students can customized their virtual routers as needed. Since Dynamips runs true IOSs, it supports all the EIGRP commands that are implemented in the IOS. That is, it does not have the limitations of Packet Tracer, however users are restricted to smaller topologies due to the resources needed by each virtual router. In general, to run a network scenario with ten Cisco 3745 routers, a PC with an up-to-date processor and at least 4 GB of RAM is recommended. With Dynamips, virtual routers can interact with real routers allowing students to expand their testbed to a bigger topology by adding virtual routers. Similarly to Packet Tracer, it is possible to create a challenging EIGRP scenario based on virtual routers that run on different PCs, and where each student is responsible for its own part of the topology.

1 2

http://www.ipflow.utc.fr/index.php/Cisco_7200_Simulator http://www.gns3.net

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Another important feature for the training of students that offers Dynamips is the capture of network traffic. To do so, from GNS3, students just have to right click the link where they want to capture and choose the Capture item in the context menu. Immediately, Wireshark (Gamess and Veracoechea 2010) (a popular free packet analyzer) will appear and students will get a copy of all the EIGRP packets (Hello, Acknowledgment, Update, Query, and Reply) sent by routers in the link.

30.6

OPNET IT Guru

Network simulators are widely used by network administrators and researchers to plan, design, secure, analyze, test, debug, improve, and fine-tune networks. There are many network simulators around and Andrea Rizzoli3 and Sally Floyd4 are maintaining up-to-date lists of such tools. Some network simulation tools (ns-2, QualNet Developer and OPNET IT Guru) are very popular in the academic world, for their type of license and their abundant documentation (Gamess and Veracoechea 2010). ns-2 (Issariyakul and Hossain 2009) (Network Simulator 2) is an open source network simulator that is mainly used in the simulation of TCP variants and ad-hoc networking research. ns-2 does not support EIGRP and only offers a limited GUI (called nam) that allows users to start, stop, and step forward and backward the simulation. QualNet Developer and OPNET IT Guru are commercial network simulation tools developed by Scalable Network Technologies5 and OPNET Technologies,6 respectively. Both tools support EIGRP and have an excellent GUI. Their prices can be very high and vary depending on the extra modules required for the simulation. With the QUP (QualNet University Program), researchers and professors can acquire 1-seat research licenses and multi-seat teaching licenses of QualNet Developer at substantially reduced prices. There is a limited edition of OPNET IT Guru (called OPNET IT Guru Academic Edition) that can be downloaded free of charge by students and professors at OPNET Technologies6 website. Due to the free OPNET IT Guru Academic Edition, we will limit our study to this network simulation tool. Similarly to Packet Tracer and Dynamips/GNS3, OPNET IT Guru users can easily build their network topologies in a visual way by dragging, placing, connecting, and clustering virtual network devices. Almost all network settings and EIGRP commands are supported by OPNET IT Guru. That is, once the simulated network has been visually drawn, users can specify the IP address and subnet mask of each interface of routers. OPNET IT Guru also offers a method to auto-assign IP addresses and subnet masks. Then, users can create different EIGRP processes to manage

3

http://www.idsia.ch/~andrea/sim/simnet.html http://www.icir.org/models/simulators.html 5 http://www.scalable-networks.com 6 http://www.opnet.com 4

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several ASs (Autonomous Systems), and specify the EIGRP interfaces and the passive interfaces. Also, users can enable or disable auto-summarization, change the K-values (K1, K2, K3, K4, and K5) used in the computation of the metric, specify the variance, generate a default-route, redistribute other routing protocols (RIP, OSPF, ISIS, Static, Directly Connected, etc.) into EIGRP, establish input or output filters for route filtering, etc. OPNET IT Guru Academic Edition do not use the CLI to configure routers, instead users do it through menus and dialog boxes. Once the setting is finished, the simulation can be run and results (routing table, EIGRP topology table, etc.) are generated. To verify connectivity or to draw the path followed by packets between two devices, users can simulate ping commands or visualize the path used by packets in an easy-way. OPNET IT Guru Academic Edition is seldom used by CCNA or CCNP students, since the configuration is not done through the CLI. Moreover, users can not interact with the simulator during simulation time, so they must define all the simulated evens before running the simulation and study the effect of the events with the collected results at the end of the simulation. However, with OPNET IT Guru, users can make a deep study of the performance of the network under a specified load, which is not possible with Packet Tracer and Dynamips/GNS3.

30.7

Easy-EIGRP

Easy-EIGRP (Expo´sito et al. 2010) is an implementation of the EIGRP protocol developed in Java. It can be installed in a PC with several NICs (Network Interface Cards) to transform it in an EIGRP router. Even if it is a true, but limited implementation of the EIGRP protocol, its main goal is to be used as a didactic application in introductory and advanced network courses to support the teaching and learning of EIGRP. Unlike the previous studied tools (Packet Tracer, Dynamips/GNS3, and OPNET IT Guru), this application is totally focused on the teaching and learning of EIGRP through very intuitive and interactive GUIs, and gives students all the necessary feedback to know what actually happens at any given time, which results in a more effective and natural way to get or transmit the skills. To ease the teaching and learning process, Easy-EIGRP provides a set of five modules briefly described below.

30.7.1 EIGRP Settings This module offers users an interface for setting and configuring the network attributes of the PC as well as the environment variables of EIGRP. Figure 30.1 shows how the module is divided in three main sections: one that lists all the NICs detected in the PC, another section for setting network interfaces attributes (e.g., IP

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Fig. 30.1 Easy-EIGRP’s settings module

address, subnet mask, bandwidth, delay, etc.), and a third one for configuring router variables such as ASN (Autonomous System Number), Maximum Retransmission Allowed, K-values (K1, K2, K3, K4, and K5), etc. This module offers images, icons and warning messages that guide users in the settings, preventing them from typing incorrect values. It is a much easier and attractive way to specify the settings than the traditional CLI where users will have to memorize a large amount of commands to configure devices, and therefore, it is more adequate for novice students.

30.7.2 DUAL Finite State Machine This module allows users to view every single step executed by the complex EIGRP’s finite state machine. Since the sequence of events in the finite state machine can occur very fast, this module provides options to customize the reproduction of any previous DUAL process (including forwarding, backwarding, pausing and stopping the animation). It also has a log section and a reply-status table through which users can keep track of events. It is important to note that the previous tools (Packet Tracer, Dynamips/GNS3, and OPNET IT Guru) do not offer information of the DUAL finite state machine, so Easy-EIGRP is a strong candidate to teach advanced EIGRP concepts. In this module, users can pick a prefix and

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Fig. 30.2 Easy-EIGRP’s DUAL finite state machine module

interact with its finite state machine. They can witness an animation composed of state changes with their own natural messages, allowing the understanding of the internal processes which were carried out and generally represent a fairly abstract task for students. Figure 30.2 shows the layout of the module with its different sections and how they interact with each other. For example, the log section and the animation of the finite state machine are synchronized, that is, when changing from one state to another the respective message is highlighted in the log section to show students when, how and who answered the query sent.

30.7.3 Partial Network Map This component provides a graphic and live updated view of the network obtained from the knowledge gained from neighbors. It also has a log section which describes events that occur in the network and is particularly well suited for the teaching and learning of the EIGRP metric since it shows details of its computation using the information advertised by neighbors (see Fig. 30.3). The simple GUI in this module was designed to dramatically improve the learning curve by providing immediate feedback after any change in the network or configuration. For example, when Easy-EIGRP discovers a neighbor, a new

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Fig. 30.3 Easy-EIGRP’s partial network map

router icon will appear in the Partial Network Map with the learnt subnets. When Easy-EIGRP loses a neighbor, the corresponding router icon will disappear.

30.7.4 EIGRP Tables EIGRP handles four main tables (IP Routing Table, Neighbor Table, Topology Table and Complete Topology Table) that are fundamental for the functionality to the entire protocol. These tables are available in this module of Easy-EIGRP and are presented with the same format used by the Cisco CLI, to facilitate user migration. However, they are displayed with different colors to distinguish each of the entries and allow a more pleasant viewing. It is clear that for the visualization of these tables, users do not have to remember or enter any command; furthermore, they are updated automatically in real time with the loss or arrival of new neighbors, or any metric change.

30.7.5 Logger The main goal of this module is to collect and summarize all the information processed by Easy-EIGRP to develop student’s ability to predict the responses of the actual configuration and to learn much more quickly. This panel, like all the previous ones, is automatically updated in real time offering new users valuable information to quickly learn the protocol. Figure 30.4 displays how this complete and powerful debugging

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Fig. 30.4 Easy-EIGRP’s Logger module

panel can keeps track of every packet exchange for troubleshooting and learning purposes. Similar to a packet sniffer, users can see all the information of the EIGRP packets in a more detailed form, presented as a tree which can be expanded and collapsed. The same behavior is observed in the packet bytes panel that shows the data of the current packet (selected in the messages/packets panel) in a hexdump style. In addition, system debugging messages are also added by the module. The computation of the packet checksum can be seen as a simple task by network specialists, but it may be a nightmare for beginner students. In this module, users can get the step-by-step process to compute a packet checksum, just by double clicking on checksum’s field (for both, the IP and EIGRP headers).

30.7.6 Comparative Study After the implementation phase of Easy-EIGRP, an exploratory study was conducted to measure the level of acceptance of this tool in a group of 50 students with different knowledge in terms of networking concepts, ranging from no knowledge at all to advanced skills. The study was carried out in the School of Computer Science of our University (Universidad Central de Venezuela) and consisted in setting up some simple topologies with GNS3 (using the Cisco CLI) on one side,

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Fig. 30.5 Results of the study

and with Easy-EIGRP on the other side. After doing the experiments, the students filled a survey to assess certain features of the tools such as learning, usability, ease of configuration, application feedback, etc., in a range of 1–5 marks, where 1 mark was the lowest score acceptance and 5 marks the highest. After analyzing the survey, we obtained quite positive results (see Fig. 30.5) indicating that Easy-EIGRP is well accepted by students and greatly facilitates the teaching and learning of EIGRP. Additionally, we evaluated each of the five modules of Easy-EIGRP separately, and received a very positive feedback from students (grades ranged between 4 and 5 marks).

30.8

Conclusions and Future Work

Networks are becoming more and more important in today’s life. Then, it is an essential area in the training of computer science students. With the needs and trends of new networking technologies, routing protocols are using complex algorithms and concepts that are not easy to understand at first glance. To facilitate the teaching and learning process of EIGRP (a Cisco Systems routing protocol), visual tools can be used. In this paper, we presented three well-known tools (Packet Tracer, Dynamips/ GNS3, and OPNET IT Guru) and a new application (Easy-EIGRP) that can significantly help students to master EIGRP. All these tools allow users to do labs with several simulated routers in a single PC, that is, without the need of a real and expensive testbed. They have advanced GUIs to ease the settings, and animations to facilitate the understanding of ideas. Most of these tools enable a direct look inside of a “wire” that is interconnecting devices and carrying the PDUs of the protocols. So students can visually see and therefore more easily understand what is really going on in the network. Packet Tracer and Dynamips/GNS3 are focused to Cisco’s devices configuration,

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so CCNA and CCNP students are more likely to use them. With OPNET IT Guru, students can also learn how EIGRP works by doing simulations. These are no realtime interactions between users and the virtual devices, but OPNET IT Guru allows users to make a deep study of the performance of the network. Easy-EIGRP is an advanced solution for the teaching of EIGRP. It shows a significant quantity of information to students and assists them in the configuration and learning of EIGRP. With Easy-EIGRP, all the information are shown dynamically and it is well-suited for novice students to learn concepts such as the metric, successors, and feasible successors. Unlike the other applications, Easy-EIGRP does have a module for the EIGRP finite state machine, which makes it one of the strongest tools for the teaching and learning of EIGRP. For future work, we plan to further develop Easy-EIGRP to support IPv6 (Davies 2008; Deering and Hinden 1998), since IPv6 will become the predominant layer-3 protocols in tomorrow’s networks. We also plan to develop a didactic visual version of OSPF and BGP, and study how animations and graphics can also support the learning process of these two complex routing protocols.

References Davies J (Jan 2008) Understanding IPv6, 2nd ed. Microsoft Press Deal R (Apr 2008) Cisco certified network associate study guide, 3rd ed. McGraw-Hill Osborne Deering S, Hinden R (Dec 1998) Internet protocol, version 6 (IPv6) specification. RFC 2460 Doyle J, Carrol J (Oct 2005) Routing TCP/IP, vol I, 2nd ed. Cisco Press Edwards W, Jack T, Lammle T, Skandier T, Padjen R, Pfund A, Timm C (May 2005) CCNP: complete study guide. Sybex Expo´sito J, Trujillo V, Gamess E (2010) Using visual educational tools for the teaching and learning of EIGRP. Lecture notes in engineering and computer science: proceedings of the world congress on engineering and computer science 2010 (WCECS 2010), San Francisco, USA, 20–22 Oct 2010, pp 169–174 Frezzo D, Behrens J, Mislevy R, West P, DiCerbo K (Apr 2009) Psychometric and evidentiary approaches to simulation assessment in packet tracer software. The fifth international conference on networking and services (ICNS 2009), Valencia, Spain Galitz O (Jun 2007) The essential guide to user interface design: an introduction to GUI design principles and techniques, 3rd edn. Wiley, New York Gamess E, Veracoechea C (Jul 2010) A comparative analysis of network simulation tools. The 2010 international conference on modeling, simulation and visualization methods (MSV’10). Part of WORLDCOMP’10, Las Vegas, Neveda Issariyakul T, Hossain E (Nov 2009) Introduction to network simulator NS2. Springer, New York Janitor J, Jakab F, Kniewald K (Mar 2010) Visual learning tools for teaching/learning computer networks. The sixth international conference on networking and services (ICNS 2010), Cancun, Mexico Pepelnjk I (Jan 2000) EIGRP network design solutions: the definitive resource for EIGRP design, deployment, and operation. Cisco Press, Indianapolis, USA Smith A, Bluck C (Mar 2010) Multiuser collaborative practical learning using packet tracer. The sixth international conference on networking and services (ICNS 2010), Cancun, Mexico

Chapter 31

Relevance Features Selection for Intrusion Detection Adetunmbi Adebayo Olusola, Oladele S. Adeola and Oladuni Abosede Daramola

31.1

Introduction

As Internet keeps growing with an exponential pace, so also is cyber attacks by crackers exploiting flaws in Internet protocols, operating system and application software. Several protective measures such as firewall have been put in place to check the activities of intruders which could not guarantee the full protection of the system; hence, the need for a more dynamic mechanism like intrusion detection system (IDS) as a second line of defense. Intrusion detection is the process of monitoring events occurring in a computer system or network and analyzing them for signs of intrusions (Bace and Mell 2001). IDSs are simply classified as host-based or network-based. The former operates on information collected from within an individual computer system and the latter collect raw networks packets as the data source from the network and analyze for signs of intrusions. The two different detection techniques employed in IDS to search for attack patterns are Misuse and Anomaly. Misuse detection systems find known attack signatures in the monitored resources. Anomaly detection systems find attacks by detecting changes in the pattern of utilization or behaviour of the system. Majority of the IDSs currently in use are either rule-based or expert-system based whose strengths depend largely on the dexterity of the security personnel that develops them. The former can only detect known attack types and the latter is prone to generation of false positive alarms. This leads to the use of an intelligence technique known as data mining/machine learning technique as an alternative to expensive and strenuous human input. These techniques automatically learn from

A.A. Olusola (*) Department of Computer Science, Federal University of Technology, PMB 704, Akure Ondo State, Nigeria e-mail: [email protected] S.‐I. Ao et al. (eds.), Intelligent Automation and Systems Engineering, Lecture Notes in Electrical Engineering 103, DOI 10.1007/978-1-4614-0373-9_31, # Springer Science+Business Media, LLC 2011

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data or extract useful pattern from data as a reference for normal/attack traffic behaviour profile from existing data for subsequent classification of network traffic. Intelligent approach was first implemented in mining audit data for automated models for intrusion detection (MADAMID) using association rule (Lee et al. 1999). Several others machine-learning paradigms investigated for the design of IDS include: neural networks learn relationship between given input and output vectors to generalize them to extract new relationship between input and output (Ajith et al. 2005; Byunghae et al. 2005; Mukkamala et al. 2002), fuzzy generalize relationship between input and output vector based on degree of membership (Ajith et al. 2005; Susan and Rayford 2000), decision tree learns knowledge from a fixed collection of properties or attributes in a top down strategy from root node to leave node (Ajith et al. 2005; Pavel et al. 2005; Quinlan 1993), support vector machine simply creates Maximum-margin hyper planes during training with samples from two classes (Byung-Joo and Il-Kon 2005; Mukkamala et al. 2002; Zhang et al. 2004). Rough sets produce a set of compact rules made up of relevant features only suitable for misuse and anomalous detection (Adetunmbi et al. 2007, 2008; Sanjay et al. 2005; Zhang et al. 2004). Bayesian approaches are powerful tools for decision and reasoning under uncertain conditions employing probabilistic concept representations (Axelsson 1999). Prior to the use of machine learning algorithms raw network traffic must first be summarized into connection records containing a number of within-connection features such as service, duration, and so on. Identification of important features is one of the major factors determining the success of any learning algorithm on a given task. Feature selection in learning process leads to reduction in computational cost, over fitting, model size and leads to increase in accuracy. Previous works in feature selection for intrusion detection include the work of (Kayacik et al. 2006; Sung and Mukkamala 2003). In this paper, attempt was made to investigate the relevance of each feature in KDD 99 intrusion detection dataset to substantiate the performance of machine learning and degree of dependency is used to determine the most discriminating features for each class. Therefore, the relevance of the forty one (41) features with respect to dataset labels was investigated. This paper is organized as follows: in Sect. 31.2 description of the intrusion detection evaluation dataset is presented followed by brief description of rough set and discretization technique employed in Sect. 31.3. Section 31.4 presents the experimental setup and results followed by conclusion in Sect. 31.5.

31.2

Intrusion Detection Dataset

The KDD Cup 1999 dataset used for benchmarking intrusion detection problems is used in our experiment (Cup 1999). The dataset was a collection of simulated raw TCP dump data over a period of 9 weeks on a local area Network. The training data

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Table 31.1 Class labels and the number of samples that appears in “10% KDD” dataset Original number Number of samples after Attack of samples removing duplicated instances Class Back 2,203 994 DOS Land 21 19 DOS neptune 107,201 51,820 DOS Pod 264 206 DOS Smurf 280,790 641 DOS teardrop 979 918 DOS Satan 1,589 908 PROBE ipsweep 1,247 651 PROBE Nmap 231 158 PROBE portsweep 1,040 416 PROBE normal 97,277 87,831 NORMAL Guess_passwd 53 53 R2L ftp_write 8 8 R2L Imap 12 12 R2L Phf 4 4 R2L multihop 7 7 R2L warezmaster 20 20 R2L warezclient 1,020 1020 R2L Spy 2 2 R2L Buffer_overflow 30 30 U2R loadmodule 9 9 U2R Perl 3 3 U2R Rootkit 10 10 U2R

was processed to about five million connection records from 7 weeks of network traffic and 2 weeks of testing data yielded around two million connection records. The training data is made up of 22 different attacks out of the 39 present in the test data.The known attack types are those present in the training dataset while the novel attacks are the additional attacks in the test datasets not available in the training data sets. The attacks types are grouped into four categories: (1) DOS: Denial of service – e.g. syn flooding (2) Probing: Surveillance and other probing, e.g. port scanning (3) U2R: unauthorized access to local super user (root) privileges, e.g. buffer overflow attacks (4) R2L: unauthorized access from a remote machine, e.g. password guessing The training dataset consisted of 494,021 records among which 97,277 (19.69%) were normal, 391,458 (79.24%) DOS, 4,107 (0.83%) Probe, 1,126 (0.23%) R2L and 52 (0.01%) U2R connections. In each connection are 41 attributes describing different features of the connection and a label assigned to each either as an attack type or as normal. Table 31.1 shows the class labels and the number of samples that appears in “10% KDD” training dataset.

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Basic Concept of Rough Set

Rough Set is a useful mathematical tool to deal with imprecise and insufficient knowledge, reduce data set size, find hidden patterns and generate decision rules. Rough set theory contributes immensely to the concept of reducts. Reducts is the minimal subsets of attributes with most predictive outcome. Rough sets are very effective in removing redundant features from discrete data sets. Rough set concept is based on a pair of conventional sets called lower and upper approximations. The lower approximation is a description of objects which are known in certainty to belong to the subject of interest, while upper approximation is a description of objects which possibly belong to the subset (Komorowski et al. 1998). Definition 1: Let S ¼ hU; A; V; f i be an information system, where U is a universe containing a finite set of N objects fx1 ; x2 ; ::::xN g. A is a non-empty finite set of attributes S used in description of objects. V describes values of all attributes, that is, V ¼ a2A Va where Va forms a set of values of the a-th attribute. f : UxA ! V is the total decision function such that f ðx; aÞ 2 Va for every a 2 A and x 2 U. Information system is referred to as decision table (DT) if the attributes in S is divided into two disjoint sets called condition (C) and decision attributes (D) where A ¼ C [ D and C \ D ¼ f. DT ¼ hU; C [ D; V; f i

(31.1)

A subset of attributes B  A defines an equivalent relation (called Indiscernibility relation) on U, denoted as IND(B). INDðBÞ ¼ fðx; yÞ 2 UxUj f ðx; bÞ ¼ f ðy; bÞ8b 2 Bg:

(31.2)

The equivalent classes of B-indiscernibility relation are denoted by [x]B. where ½xB ¼ fy 2 Ujðx; yÞ 2 INDðBÞg Definition 2: Given B  A and X  U. X can be approximated using only the information contained within B by constructing the B lower and B-upper approximations of set X defined as: BX ¼ fx 2 Xj½xB  Xg BX ¼ fx 2 Xj½xB \ X 6¼ 0g

ð31:3Þ

Definition 3: Given attributes A ¼ C [ D and C \ D ¼ f. The positive region for a given set of condition attribute C in the relation to IND (D), POSC(D) can be defined as POSC ðDÞ ¼

[ x2D

CX

(31.4)

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where D* denotes the family of equivalence classes defined by the relation IND(D). POSC(D) contains all objects of U that can be classified correctly into the distinct classes defined by IND(D). Similarly, given attributes subsets B, Q  A, the positive region contains all objects of U that can be classified to blocks of partition U/Q using attribute B. B is defined as: POSB ðQÞ ¼

[

BX

(31.5)

x2Q

Definition 4: Given attributes B, Q  A, the degree of dependency of Q on B over U is defined as gB ðQÞ ¼

jPOSB ðQÞj jUj

(31.6)

The degree of dependency of an attribute dictates its significance in rough set theory.

31.3.1 Discretization Based on Entropy Entropy, a supervised splitting technique used to determine how informative a particular input attribute is about the output attribute for a subset, is calculated on the basis of the class label. It is characterized by finding the split with the maximal information gain (Jiawei and Micheline 2006). It is simply computed thus: Let D be a set of training data set defined by a set of attributes with their corresponding labels The Entropy for D is defined as: EntropyðDÞ ¼ 

m X

Pi log2 ðPi Þ

(31.8)

i¼1

where Pi is the probability of Ci in D, determined by dividing the number of tuples of Ci in D by |D|, the total number of tuples in D. Given a set of samples D, if D is partitioned into two intervals D1 and D2 using boundary T, the entropy after partitioning is EðD; TÞ ¼

jD 1 j j D2 j EntðD1 Þ þ EntðD2 Þ D D

(31.9)

where |Di| denotes cardinality. The boundaries T are chosen from the midpoints of the attributes values

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Information gain of the split, Gain(D,T) ¼ Entropy(D) – E(D,T). In selecting a spilt-point for attribute A, pick an attribute value that gives the minimum information required which is obtained when E(D,T) is minimal.. This process is performed recursively on an attribute the information requirement is less than a small threshold (0). EntðSÞ  EðT; SÞ > d

31.4

(31.10)

Experimental Setup and Results

The training set employed for this analysis is the “10% KDD” (kddcup_data_gz file) dataset. Since the degree of dependency is calculated for discrete features, continuous features are discretized based on entropy as discussed in the previous section. Prior to the discretization, redundant records from the dataset were removed since rough set does not require duplicate instances to classify and identify discriminating features. For this experiment a total of 145,738 records are used, detailed shown in Table 31.1. In this experiment, two approaches are adopted to detect how significant a feature is to a given class. The first approach is the computation of the degree of dependency for each class based on the available number of class instances in the data set. Thus, signifying how well the feature can discriminate the given class from other classes. Secondly, each class labels are mapped against others for each attribute. That is, generating a frequency table of a particular class label against others based on variations in each attribute and then a comparison made to generate the dependency ratio of predominant classes in order to detect all the relevant features distinguishing one class from another. Graphical analysis was also employed in the analysis in order to detect the relevant features for each class. The dependency ratio is simply computed thus Dependency ratio ¼

HVF OTH  TIN TON

(31.11)

where HVF is the highest number of instance variation for a class label in attribute f. TIN is the total number of instances of that class in the dataset OTH is the number of instances for other class labels based on a particular or a set of Variations. TON is the total number of instances of class labels in the data set constituting OTH

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Table 31.2 Attribute with the highest degree of dependency that distinctly distinguish some class labels from the training data set Degree of Other distinct Attack dependency Selected features Feature name features back 0.9708 5 source bytes 6 neptune 0.0179 3 Service 39 teardrop 0.9913 8 wrong fragment 25 satan 0.0319 30 diff srv rate 27,3 portsweep 0.0264 4 Flag 30,22,5 normal 0.0121 6 destination bytes 5,3,10,11,1 guess_passwd 0.0189 11 failed logins – imap 0.3333 26 srv error rate – warezmaster 0.7500 6 destination bytes – warezclient 0.2686 10 Hot 5,1

Fig. 31.1 Maximum Degree of dependency of each feature distinctly distinguishing an attack

31.4.1 Result Discussions Results are presented in terms of the class that achieved good levels of discrimination from others in the training set and the analysis of feature relevancy in the training set. Analyses are based on degree of dependency and binary discrimination for each class. That is, for each class, a dataset instance is considered as in-class, if it has the same label; out-class, if it has a different label. Degree of dependency is computed for class labels based on number of instances of that class available in the dataset. Table 31.2 shows the highest degree of dependency of class labels depending on a particular feature which clearly distinguished a particular class label in the training data set. Figure 31.1 shows the Degree of dependency of each feature that distinctly distinguished some class labels while Fig. 31.2a and b clearly display the patterns of these chosen attributes. From Figs. 31.1 and 31.2b, Feature 8 selected for teardrop has the highest degree of dependency of 0.9913 based on variations 2 of that attribute shows that attribute

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a

back vs others (attribute 5)

1.2

Frequency ratio

1 back.

0.8

Others.

0.6 0.4 0.2 0 Attribute Variation

b

Teardrop vs other attributes in 8

% Distribution

1.2 1.0 0.8

teardrop

0.6

others

0.4 0.2 0 0

1 Variations

2

Fig. 31.2 (a) Dependency of back attack and others on attribute 5. (b) Dependency of teardrop attack against other class labels on attribute 8

8 is sufficient to detect teardrop attack. In addition, Fig. 31.2b shows that the remaining 0.0097 can be detected based on variation 1. Another DOS attack type “back” with degree of dependence of 0.9708 on attribute 5 depicted in Fig. 31.2a. This also shows that the attribute is almost sufficient in detecting this attack type. Table 31.3 details the most relevant features selected for each class and their corresponding dependency ratio. Six out of the 23 classes chooses amount of data exchange (source and destination bytes) as the most discriminating features with DOS group having half of it. This is expected of denial of service and probe category of attacks where the nature of the attack involves very short or very long connections. Feature 7 which are related for land attack is selected as the most discriminating feature for land attack while for pod and teardrop feature 8 (wrong fragment) was selected as the most discriminating features for these attack types. Figure 31.3 shows the maximum dependency ratio for each feature as shown in Table 31.3 and it could be seen that dependency of land attack on feature 7 is almost 100%. In fact it is 100% because all the 19 records labeled land in the training dataset used variation 2 in feature 7 which was only used by land attack except for a record

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Table 31.3 The most relevant feature for each attack type and normal Most relevant Dependency Attack features Feature name Variations ratio Back 5 source bytes 66,64,60 0.9708 Land 7 Land 2 0.9999 Neptune 5 source bytes 0 0.9328 Pod 8 wrong fragment 1 0.9853 Smurf 5 source bytes 39 0.7731 Teardrop 8 wrong fragment 2 0.9913 Satan 30 diff srv rate 30 0.7648 dst host name src Ipsweep 36 port rate 13,14,15,17 0.8282 Nmap 5 source bytes 4 0.6448 portsweep 28 srv error rate 9 0.8057 Normal 29 same srv rate 28 0.8871 guess_passwd 11 failed login 1 0.9622 ftp_write 23 Count 1 0.7897 Imap 3 Service 60 0.9980 Phf 6 destination bytes 28 0.9976 Multihop 23 Count 1 0.7898 warezmaster 6 destination bytes 33 0.7500 warezclient 3 Service 13 0.6658 dst host srv Spy 39 serror rate 8 0.9997 buffer_overflow 3 Service 6 0.6965 dst host name loadmodule 36 srcport rate 29 0.6279 Perl 14 root shell 1 0.9994 Rootkit 24 srv count 1 0.7269

415

Class DOS DOS DOS DOS DOS DOS PROBE PROBE PROBE PROBE NORMAL R2L R2L R2L R2L R2L R2L R2L R2L U2R U2R U2R U2R

Fig. 31.3 Dependency ratio of each feature

labeled normal in the dataset. Also revealed is heavy dependence on feature “Service” (that is, feature 3) which shows that different services are exploited to perpetrate different types of attack. For instance, imap4, ftp_data and telnet are exploited to lunch imap, warezclient and buffer_overflow attack respectively.

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Class label Back Land neptune Pod Smurf teardrop Satan ipsweep Nmap portsweep normal guess_passwd ftp_write Imap Phf multihop warezmaster warezclient spy buffer_overflow loadmodule perl rootkit

Relevant features 5,6 7 3.4,5,23,26.29,30,31,32,34,36,37,38,39 8 2,3,5,6,12,25,29,30,32,36,37,39 8 27 36 5 28 3,6,12,23,25,26,29,30,33,34,35,36,37,38,39 11,6,3,4 9,23 3,39 6,10,14,5 23 6,1 3,24,26 39,1 3,24,14,6 36,24,3 14,16,18,5 24,23,3

Table 31.4 details the most discriminating class labels for each feature. Normal, Neptune and Smurf are the most discriminating classes for most of the features which consequently make their classification easier. Moreover, these three classes dominating the testing dataset and this account to high detection rate of machine learning algorithm on them. Figure 31.3 shows how important a particular feature is to detection of an attack and normal. For some class label a feature is sufficient to detect an attack type while some requires combination of two or more features. For features with few representatives in the dataset such as spy and rootkit, it is very difficult detecting a feature or features that can clearly differentiate them because of the dominance of some class labels like normal and Neptune. This difficulty is in classifying attacks limited to two major groups, user to root and remote to local. The involvement of each feature has been analyzed for classification. Features 20 and 21 make no contribution to the classification of either an attack or normal. Hence these two features (outbound command count for FTP session and hot login) have no relevance in intrusion detection. There are other features that makes little significance in the intrusion detection data set. From the dependency ratios calculated, these features include 13, 15, 17, 22 and 40 (number of compromised conditions, su attempted, number of file creation operations, is guest login, dst host rerror rate respectively).

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31.5

417

Conclusion

In this paper, selection of relevance features is carried out on KDD’99 intrusion detection evaluation dataset. Empirical results revealed that some features have no relevance in intrusion detection. These features include 20 and 21 (outbound command count for FTP session and hot login) while features 13, 15, 17, 22 and 40 (number of compromised conditions, su attempted, number of file creation operations, is guest login, dst host rerror rate respectively) are of little significant in the intrusion detection. In our future work, additional measures including sophisticated statistical tools will be employed.

References Adetunmbi AO, Alese BK, Ogundele OS, Falaki SO (2007) A data mining approach to network intrusion detection. J Comp Sci Appl 14(2):24–37 Adetunmbi AO, Adeola OS, Daramola OA (2010) Analysis of KDD’99 Intrusion detection dataset for selection of relevance features. Lecture notes in engineering and computer science: proceedings of the world congress on engineering and computer science 2010 (WCECS 2010), vol 1, San Francisco, USA, 20–22 Oct 2010, pp 162–168 Adetunmbi AO, Falaki SO, Adewale OS, Alese BK (2008) Intrusion detection based on rough set and k-nearest neighbour. Int J Comput ICT Res 2(1):60–66 Ajith A, Ravi J, Johnson T, Sang YH (2005) D-SCIDS: distributed soft computing intrusion detection system. J Network Comp Appl 28(1):1–19, Elsevier Axelsson S (1999) The base –rate fallacy and its implication for the difficulty of intrusion detection. In: Proceedings of the 6th ACM conference on computer and communication security, Singapore, pp 127–141 Bace R, Mell P (2001) Intrusion detection system, NIST special publications SP 800. November Byung-Joo K, Il-Kon K (2005) Machine Learning approach to real time intrusion detection system. In: Zhang S, Jarvis (eds) Lecture notes in artificial intelligence, vol 3809. Springer, Berlin, Heidelberg, pp 153–163 Byunghae C, kyung WP, Jaittyun S (2005) Neural networks techniques for host anomaly intrusion detection using fixed pattern transformation in ICCSA. LNCS 3481:254–263 Jiawei H, Micheline K (2006) Data mining concepts and techniques, 2nd ed. China Machine Press, Singapore, pp 296–303 Kayacik HG, Zincir-Heywood AN, Heywood ML (2006) Selecting features for intrusion detection: a feature analysis on KDD 99 intrusion detection datasets KDD Cup 1999 Data: Available: http://kdd.ics.uci.edu/databases/kddcup99/ Komorowski J, Pokowski L, Skowron A (1998) Rough sets: a tutorial. citeseer.ist.psu.edu/ komorowski98rough.html Lee W, Stolfo SJ, Mok K (1999) Data mining in work flow environments: experiments in intrusion detection. In: Proceedings of the 1999 conference on knowledge discovery and data mining, 15–18 Aug 1999, San Diego, CA Mukkamala S, Janoski G, Sung A (2002) Intrusion detection using neural networks and support vector machines. In: Proceedings of IEEE international joint conference on neural networks, pp 1702–1707

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Pavel L, Patrick D, Christia S, Konrad R (2005) Learning intrusion detection: supervised or unsupervised? International conference on image analysis and processing, (ICAP). Italie 2005(3617):50–57 Quinlan JL (1993) C4.5 program for machine learning, Morgan Kaufmam, USA Sanjay R, Gulati VP, Arun KP (2005) A fast host-based intrusion detection system using rough set theory in transactions on rough sets IV. LNCS 3700(2005):144–161 Susan MB, Rayford BV (2000) Intrusion detection via fuzzy data mining. Proceedings of the 12th annual Canadian Information Technology Security Symposium, Ottawa, Canada, Jun 19–23, pp 109–122 Sung AH, Mukkamala S (2003) Identifying important features for intrusion detection using support vector machines and neural networks. IEEE proceedings of the 2003 symposium on applications and the Internet Zhang L, Zhang G, YU L, Zhang J, Bai Y (2004) Intrusion detection using rough set classification. J Zhejiang Univ Sci 5(9):1076–1086

Chapter 32

A Visual Application for Teaching and Learning the Advanced Concepts of the Diffusing Update Algorithm for EIGRP Valentina Trujillo Di Biase, Jesu´s Expo´sito Marquina, and Eric Gamess

32.1

Introduction

Routing protocols can be categorized, based on the information they exchange and the way they compute their routing tables, in: distance vector protocols and link state protocols. In a distance vector protocol, each node knows the shortest distance from a neighbor node to every destination network; however it does not know all the nodes between its neighbor and the final destination. This type of protocols sends periodic updates which include every destination entry of its routing table along with the corresponding shortest distance to it. Distance vector protocols use the Bellman-Ford algorithm for shortest path computation. The main disadvantages of this algorithm are routing table loops and counting to infinity. Some of the most important distance vector protocols known are: RIPv1 (Hedrick 1998) (Routing Information Protocol v1), RIPv2 (Malkin 1998) (Routing Information Protocol v2), and IGRP (Doyle and Carrol 2005) (Interior Gateway Routing Protocol). On the other hand, in link state protocols, every node knows the whole network topology thanks to the update flooding that happens every time a topology change is detected. Based on these updates, each node must compute the shortest path to a specific destination. Link state protocols use some variant of Dijkstra’s algorithm for the shortest path computation, which ensures that the counting to infinity problem is not going to be present; however, an up-to-date version of the entire topology is needed by every node, which may constitute excessive storage and communication overhead in a large, dynamic network (Garcı´a-Lunes-Aceves 1993). OSPF (Coltun et al. 1999, Moy 1989) (Open Shortest Path First) and IS-IS (ISO 2002, Oran 1990) (Intermediate System to Intermediate System) are some of the most commonly used link state routing protocols.

V.T. Di Biase (*) Escuela de Computacio´n, Universidad Central de Venezuela, Los Chaguaramos, Caracas 1040, Venezuela e-mail: [email protected] S.‐I. Ao et al. (eds.), Intelligent Automation and Systems Engineering, Lecture Notes in Electrical Engineering 103, DOI 10.1007/978-1-4614-0373-9_32, # Springer Science+Business Media, LLC 2011

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EIGRP (Pepelnjk 2000) (Enhanced Interior Gateway Routing Protocol) is often categorized as a hybrid protocol since it includes concepts of both distance vector and link state protocols by advertising its routing table to its neighbors as distance vector protocols do, and by using the Hello Protocol as well as creating neighbor relationships, similarly to link state protocols. In addition, it sends partial updates when a metric or the network topology changes, instead of sending full routing table updates in periodic fashion as distance vector protocols do. EIGRP uses the DUAL (Diffusing Update Algorithm) algorithm which ensures that there will never be loops, not even temporary, as in the case of distance vector or link state protocols. This algorithm is a new and advanced approach of the distance vector algorithms which includes: loop-free warranty, arbitrary transmissions or delay processing operations, arbitrary positive link assumption and finite time calculations for finding the shortest path towards a destination on a topology change (Garcı´a-Lunes-Aceves 1993). DUAL uses diffusing computations with the aim of solving the shortest path problem and improving the performance and resource usage of the traditional algorithms. Easy-EIGRP (Expo´sito et al. 2010, Trujillo 2010) is a didactic implementation of EIGRP with an integrated graphical viewer for the DUAL finite state machine that allows users to understand, in an easier way, EIGRP’s complex processes (including local and diffusing computations). In this paper, we present our alternative way of teaching the advanced concepts of DUAL using Easy-EIGRP. The rest of the paper is organized as follows: In Sect. 32.2, the related work is viewed. DUAL is discussed in Sect. 32.3. Our approach of teaching the DUAL algorithm is presented and justified in Sect. 32.4 and finally, conclusions and future work are discussed in Sect. 32.5.

32.2

Related Work

Currently, there is a wide range of options when it comes to open source projects for TCP/IP based routing protocols. The main goal of these projects is to promote inventive solutions for network routing. Some of the most popular routing software suites are: Zebra (http://www.zebra.org.); Quagga (http://www.quagga.net.); XORP (Handley et al. 2002); BIRD (http://bird.network.cz.); Click (Kohler 2000); Vyatta (http://www.vyatta.org.), etc. It is noteworthy that none of the projects mentioned before offer support for EIGRP, furthermore, none of them (not even the Cisco Systems Command Line Interface) provide a graphical view of the DUAL finite state machine which is the main core of the protocol, and its understanding is essential for EIGRP specialists. For this reason we decided to develop our own didactic EIGRP routing solution which provides a fully graphical environment for the understanding of the protocol. In addition we included a complete and interactive GUI for the DUAL comprehension, stating in that way the innovative quality of Easy-EIGRP.

32

A Visual Application for Teaching and Learning. . .

32.3

421

Dual in Eigrp

DUAL is a convergence algorithm that replaces the Bellman-Ford algorithm used by other distance vector protocols. DUAL was proposed by E. W. Dijkstra and C. S. Scholten (1980). The main goal of the algorithm is to perform distributed shortest path routing while maintaining the network free from loops at every instant. The Diffusing Update Algorithm relies on protocols (such as the Hello Protocol and the Reliable Transport Protocol) and data structures (such as the neighbor table and the topology table) to provide consistent information, leading to optimum route selection. For DUAL to operate correctly, the following conditions must be met (Garcı´a-Lunes-Aceves 1993): • Within a finite time, a node detects the existence of a neighbor or the loss of connectivity with a neighbor. This is handled by the Neighbor Discovery/ Recovery mechanism implemented by EIGRP. • All messages transmitted over an operational link are received correctly and in the proper sequence within a finite time. The EIGRP’s RTP (Reliable Transport Protocol) has the responsibility to ensure that this condition is met. • All messages, changes in the cost of a link, link failures, and new-neighbor notifications are processed one at a time within a finite time and in the order in which they are detected. EIGRP uses the Hello Protocol to discover neighbors and to identify the local router to neighbors; when this happens, EIGRP will attempt to form an adjacency with that neighbor. Once the adjacency is established, the router will receive updates from its new neighbor which will contain all routes known by the sending router and the metric of those routes. For each neighbor, the router will calculate a distance based on the distance advertised by the neighbor and the cost of the link to it. The lowest distance to a specific destination is called Feasible Distance (FD) (Doyle and Carrol 2005). The Feasible Condition (FC) is a condition that is met when a neighbor’s advertised or reported distance (RD) to a destination is strictly lower that the router’s FD to that destination. Any neighbor that meets the FC would be labeled as a Feasible Successor (FS). The FS that provides the lowest distance to a destination is labeled as a Successor and is the next hop that the router sets in order to reach that destination. It is important to mention that there might be more than one Successor and that unequal cost balancing is also allowed by EIGRP. The FSs and the FC are the elements that ensure that loops will be avoided. Because FSs always have the shortest metric distance to a destination, a router will never choose a path that will lead back through itself (creating a loop), since such path would have a distance larger that the FD, therefore the FC would not be met. EIGRP works with a topology table, where all the known destinations are recorded. Each destination is registered along with its FD and the corresponding FSs. For each FS, its advertised distance and interface of connectivity will be recorded. The DUAL’s computations can be summarized into two processes (local computations and diffusing computations) as described below.

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32.3.1 Local Computations The local computations are carried out by an EIGRP router as long as it can resolve a change in the network topology without querying its neighbors, in other words, if the router can resolve a specific situation locally. For example, if an EIGRP router faces an increased metric from its Successor and the router has at least one additional FS for the same destination, the action is immediate: the new route is selected and updates are sent to all the neighbors to inform them about the change in the network topology. When performing local computations, the affected route will stay passive. It is important to mention that if an EIGRP router can resolve a topology variation with local computations, it does not mean its neighbors are going to have the same opportunity.

32.3.2 Diffusing Computations When an EIGRP router cannot find an alternate route (no alternate route exists, or the new best route still goes through the affected Successor), it starts a diffusing computation by asking all its neighbors about an alternate route. A diffusing computation is performed in a series of steps: 1. 2. 3. 4.

The affected route is marked active in the topology table. A reply-status table is created to track the replies expected from the neighbors. A query is sent to the neighbors. Responses are collected and stored in the topology table. At the same time, the corresponding entry in the reply-status table is updated. 5. The best response is selected in the topology table and the new best route is installed in the routing table. 6. If necessary, an update is sent to the neighbors to inform them of the changed network topology (Pepelnjk 2000). Every time a query is sent to a neighbor, an independent timer is started for this neighbor in order to guaranteed network convergence in a reasonable time, which constitutes one of EIGRP principles. EIGRP ensures one more time that any loop will be avoided thanks to the use of the route’s status flag. If, for example, a router receives a query from a neighbor which is performing a diffusing computation, and the query is about a route that has already being marked as active, the router will reply with its current best path and it will stop the query processing, avoiding the creation of a query loop and an upcoming package flooding. In most cases, after a diffusing computation is complete, the router that initiated the computation must distribute the obtained results. DUAL has a finite state machine (DUAL finite state machine) which controls all the possible states in which a router can be found while performing diffusing computations (if the router is performing local computations, the finite state machine

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Fig. 32.1 DUAL finite state machine

will be executing the IE1 event, leading to the r ¼ 0, O ¼ 1 state). Because there are multiple types of input events that can cause a route to change its state, some of which might occur while a route is active, DUAL defines multiple active states. A query origin flag (O) is used to indicate the current state. Figure 32.1 and Table 32.1 show the complete DUAL finite state machine (Doyle and Carrol 2005; Expo´sito et al. 2010). As discussed before, diffusing computation requires that a router receives the replies from all the neighbors it queried in order to select the new best route. However, there are extreme circumstances in which a neighbor might fail to respond a query. Any of those circumstances blocks the router originating the diffusing computation. To prevent these types of deadlock situations, EIGRP contains a built-in safety measure: a maximum amount of time a diffusing computation can take to execute. Whenever a diffusing computation takes longer than the timeout value, the diffusing computation is aborted; the adjacency with any nonresponding neighbors is cleared, and the computation proceeds as if these neighbors replied with an infinite metric. The route for which the computation is aborted is said to be SIA (Stuck In Active). It is clear that the processes describe before are not easy to understand. For teaching and learning purpose, a visual approach can dramatically support the users (e.g., students with only basic knowledge of networking) in their understanding of every single detail of this algorithm. That is the reason why we developed a graphical implementation of the DUAL algorithm which can be found in the DUAL Finite

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Table 32.1 Input events for the DUAL finite state machine Input event Description IE1 Any input event from which FC is satisfied or the destination is unreachable IE2 Query received from the Successor; FC not satisfied IE3 Input event other than a query from the Successor; FC not satisfied IE4 Input event other than a last reply or a query from the Successor Input event other than a last reply, a query from the Successor, or an increase in the IE5 distance to destination IE6 Input event other than a last reply IE7 Input event other than a last reply or an increase in the distance to destination IE8 Increase in the distance to destination IE9 Last reply received; FC not met with current FD IE10 Query received from Successor IE11 Last reply received; FC met with current FD IE12 Last reply received; set FD to infinity

State Machine module of Easy-EIGRP. The main purpose of this module is to provide an intuitive and interactive tool for teaching and learning how EIGRP really works.

32.4

Dual’s Implementation in Easy-Eigrp

Easy-EIGRP (Expo´sito et al. 2010, http://www.zebra.org.) is a didactic application which main goal is the teaching and learning of EIGRP. Easy-EIGRP provides five modules with the aim of allowing users an improved, efficient and easy way to understand the protocol; these modules are: (1) the EIGRP Manager, (2) the DUAL Finite State Machine module, (3) the Partial Network Map Viewer, (4) the EIGRP Tables Viewer and, (5) the Logger module. In this paper we will focus on the DUAL Finite State Machine module, although it is worth to mention that the Logger module plays an important role when it comes to debugging (refer to Expo´sito et al. 2010 for further details). The DUAL implementation is based in a set of Java classes (DUAL, NeighborDiscovery, RTP, etc.) which are represented in Fig. 32.2, along with their corresponding relationships. The NeighborDiscovery class is responsible for discovering neighbors and setting new adjacencies. It also manages EIGRP table’s inputs (represented by the RoutingTable, the TopologyTable and the NeighborTable classes) every time a topology change is detected based on the loss (which will be announced by the HoldTimerRTOThread) or discovery of a neighbor. The diagram also shows that the NeighborTable, the RoutingTable and the TopologyTable are composed of Neighbors, Routes and Destination objects respectively. In turn, Destination objects are formed of a set of FeasibleSuccessors and Distances.

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Fig. 32.2 Easy-EIGRP’s DUAL class diagram

The RTP (Realiable Transport Protocol) class is the one that will manage EIGRP’s packet exchange, guaranteeing the delivery and the ordering of these packets. This class relies on the ListenerThread’s operation, since this thread is the entity that will monitor the PC’s corresponding interfaces, passing the received packets to the RTP layer, where these packets will be processed. Finally, the DUAL class embodies the decision process for all route computation by tracking all routes advertised by all neighbors. Whenever a local or a diffusing computation happens, DUAL will notify the pertinent changes to the EIGRPTablesPanel that eventually will update the EIGRP’s tables. If a diffusing computation occurs, DUAL will indicate to change the state of a Destination on the TopologyTable and to assemble its corresponding replyStatusTable. Additionally, the HoldTimerRTOThread will keep track of the SIA timers in order to prevent possible deadlock situations discussed in Sect. 32.3. The interaction of the DUAL class can be represented as shown in Fig. 32.3. Its operation was designed following some basic rules defined in (Pepelnjk 2000): 1. Whenever a router chooses a new Successor, it informs all its other neighbors about the new RD (Reported Distance). 2. Every time a router selects a Successor, it sends a poison update to its Successor.

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Fig. 32.3 DUAL’s module interaction

3. A poison update is sent to all neighbors on the interface through which the Successor is reachable unless split-horizon is turned off, in which case, it is sent to only the Successor. Easy-EIGRP also implements a set of methods which are capable of determining and handling both local and diffusing computations which are discussed next. It is important to mention that, since Easy-EIGRP was developed using reverse engineering, all its processes, operations and rules are the same defined by EIGRP. Easy-EIGRP intends to emulate the EIGRP protocol. When Easy-EIGRP is not performing diffusing computations, each route is in the passive state, as stated by the DUAL’s rules of EIGRP. Easy-EIGRP will reassess its list of FSs for a route, when a change in the cost or state of a directly connected link is detected or due to the reception of an update, a query or a reply packet. In case Easy-EIGRP’s DUAL module is not able to find a possible Successor (different from the current one) for the affected destination, the application will begin a diffusing computation and it will change the route to the active state. Next, Easy-EIGRP will perform every single step stated in Sect. 32.3.2. EasyEIGRP begins a diffusing computation by sending queries to all its neighbors. Simultaneously, the application will create a data structure which represents EIGRP’s reply-status table; this table will contain an input (including the neighbor’s IP address, a time stamp for the query sent, a time stamp for the reply received, etc.) for each neighbor queried. Since every step processed on a diffusing computation corresponds to an input event in the DUAL finite state machine (see Fig. 32.1) and since there is no didactic tool that represents or explain this process, we developed a graphical view that allows users to witness the whole computation (see Fig. 32.4). Easy-EIGRP’s DUAL finite state machine module is composed of five sections: 1. The prefix list panel, located on the left upper corner, is responsible for listing and maintaining record of all the prefixes that were handled by the application at

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Fig. 32.4 Easy-EIGRP’s DUAL finite state machine viewer

some point. It is noteworthy that even if the prefix is lost, this panel will maintain the prefix record for playback. 2. The DUAL finite state machine panel, located on the right upper corner, allows users to view an image of the whole finite state machine used by EIGRP, which can be animated at any time the users require it. 3. In the middle left, users can find a logger which will provide information about every change registered by the finite state machine. Every time users select an event on the logger, the image of the finite state machine will be updated, showing users the current state of the machine at that point. In some cases, the finite state machine panel will show additional information on its right upper corner about the selected event, generating a correspondence between the event described by the logger and the events establish on Table 32.1. It is important to mention that each prefix owns an independent logger that, no matter what happens, will always keep past events or computations. 4. The reply-status table, located in the middle right is independent for each diffusing computation process and is unique for each prefix. The table is composed of 4 fields: SIA timer, IP address of the queried neighbor, reply status, and a field that represents the reply status graphically. The last field can show three types of images: a clock (indicating the router is waiting for a reply), a green check mark (indicating the router already received the expected reply) or a red X (indicating that the expected reply never arrived and that the

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DUAL Finite State Machine Module

5

1

Logger Information

2 1.5

Navegability

2.5

Controls Section

3

Animation

4 3.5

Events Reproduction

4.5

0.5 0

Fig. 32.5 Survey results

SIA timer limit has been reached). Each reply-status table’s row has a direct correspondence with an entry of the replyStatusTable (more specifically with a ReplyStatusNode) of a Destination object. 5. Finally, there is a media reproduction control panel in the bottom of the module. The main goal of this panel is to provide full flexibility of reproduction (forward, backward, pause, etc.) of past or current diffusing computations. Easy-EIGRP allows users to modify the delay of the reproducing events in order to allow a detailed analysis. This panel also let users specify a range of events on the logger for later reproduction. It is a fact that it is easier to understand abstract topics, such as the diffusing computation processes and general networking issues, with the help of images, colors and animations, that using traditional command line interfaces or shells. That is the reason why Easy-EIGRP implements these types of tools which provide a more natural way of observing and analyzing the behavior and the decision process of DUAL. Every single section of the DUAL module was designed and developed to improve and facilitate a detailed study of the algorithm, providing an excellent, interactive and friendly interface. To prove that the benefits of this graphical view are significant, we conducted a set of experimental laboratories where we grouped around 50 computer science students with different levels of networking knowledge. We created two types of laboratories. In the first laboratory, a computer with several network interface cards was connected to Cisco 2811 routers through Fast Ethernet connections. In the computer, students installed Easy-EIGRP and configured EIGRP in all the devices to see how routing information was propagated and to understand DUAL. The idea behind the second laboratory is to do a realistic EIGRP practice in a single computer. For that, we did a virtual appliance for Easy-EIGRP, and students created some virtual machines (by cloning the virtual appliance), connected them in a

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network, configured Easy-EIGRP in these virtual machines, and also studied how routing information was propagated and handled by DUAL. After the experiment ended, a survey was filled by them. The results are shown in Fig. 32.5, where we can see the high acceptance level of Easy-EIGRP’s DUAL module, particularly about characteristics such a impact of the events reproduction, animations features, utility of the controls section, navigability, etc.

32.5

Conclusions and Future Work

Easy-EIGRP is a limited implementation of the EIGRP protocol which can be used on both Windows and Linux for teaching and learning purposes. This application includes a powerful module which implements DUAL and offers a set of graphical interfaces for debugging and understanding the processes carried out by this complex algorithm. We had been using Easy-EIGRP to support the teaching and learning of DUAL with a group of networking students. The feedback that we received from these students was very positive. Most of them could easily understand EIGRP’s operation; in addition, they claimed that the DUAL Finite State Machine panel provided them a very positive support in order to analyze how the Diffusing Update Algorithm works. For future work, we plan to further develop Easy-EIGRP to support IPv6 (Internet Protocol version 6) (Davies 2008), since IPv6 will become the predominant layer-3 protocols in tomorrow’s networks. Since our main goal is to offer Cisco instructors an excellent didactic application for the teaching of EIGRP, we already contacted Cisco Systems for a possible distribution of Easy-EIGRP, and now we are looking forward for an answer. Acknowledgment This work was partially supported by the CNTI (Centro Nacional de Tecnologı´as de Informacio´n).

References The BIRD internet routing daemon. http://bird.network.cz. Coltun R, Ferguson D, Moy J (1999) OSPF for IPv6. RFC 2740 Davies J (2008) Understanding IPv6. 2nd edn. Microsoft Press, USA Dijkstra EW, Scholten CS (1980) Termination detection for diffusing computations. Inf Process Lett 11(1):1–4 Doyle J, Carrol J (2005) Routing TCP/IP, vol 1, 2nd edn. Cisco Press, Indianapolis, USA Expo´sito J, Trujillo V, Gamess E (2010) Easy-EIGRP: a didactic application for teaching and learning of the enhanced interior gateway routing protocol Garcı´a-Lunes-Aceves J(1993) Loop-free routing using diffusing computations. IEEE/ACM transactions on networking, 1(1):130-141 GNU Zebra. http://www.zebra.org.

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Handley M, Hodson O, Kohler E (2002) XORP: an open platform for network research. ACM SIGCOMM computer communication review, pp 53–57 Hedrick C (1998) Routing information protocol. STD 34, RFC 1058 ISO (2002) Intermediate system to intermediate system intra-domain routing exchange protocol for use in conjunction with the protocol for providing the connectionless-mode network service. ISO 8473, international standard 10589:2002, Second Edition Kohler E, Morris R, Chen B, Jannotti J, Frans Kaashoek M (2000) The click modular router. Laboratory for Computer Science, MIT Malkin G (1998) RIP Version 2. RFC 2453 Moy J (1989) The OSPF specification. RFC 1131 Oran D (1990) OSI IS-IS intra-domain routing protocol. RFC 1142 Pepelnjk I (2000) EIGRP network design solutions: the definitive resource for EIGRP design, deployment, and operation. Cisco Press, Indianapolis, USA Quagga Routing Suite. http://www.quagga.net. Trujillo V, Expo´sito J, Gamess E (2010) An alternative way of teaching the advanced concepts of the diffusing update algorithm for EIGRP. Lecture notes in engineering and computer science: proceedings of the world congress on engineering and computer science 2010, WCECS 2010, San Francisco, 20–22 Oct 2010, pp 107–112 Vyatta. http://www.vyatta.org.