Huang / Wunsch / Levine Advanced Intelligent Computing Theories and Applications

With Aspects of Contemporary Intelligent Computing Techniques
1. Auflage 2008
ISBN: 978-3-540-85930-7
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 15, 580 Seiten

Reihe: Communications in Computer and Information Science

This book - in conjunction with the two volumes LNCS 5226 and LNAI 5227 - constitutes the refereed proceedings of the Fourth International Conference on Intelligent Computing, ICIC 2008, held in Shanghai, China in September 2008. The intelligent computing technology includes a range of techniques such as artificial intelligence, perceptual and pattern recognition, evolutionary and adaptive computing, informatics theories and applications, computational neuroscience and bioscience, soft computing, case based and constrained reasoning, agents, networking and computer supported co-operative working, human computer interface issues. ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications.

Autoren/Hrsg.

Huang, De-Shuang

Jo, Kang-Hyun

Wunsch, Donald C.

Levine, Daniel S.

Inhaltsverzeichnis

1;Preface;5
2;Organization;7
3;Table of Contents;15
4;Adaptive Routing Algorithm in Wireless Communication Networks Using Evolutionary Algorithm;22
4.1;Introduction;22
4.2;Statement of the Problem;23
4.3;Evolutionary Algorithm for Wireless Communication Networks;24
4.3.1;Representation;24
4.3.2;Initial and Fitness Function;24
4.3.3;Evolutionary Algorithms;24
4.4;Experimental Results;26
4.5;Conclusion;26
4.6;References;27
5;A New GA – Based and Graph Theory Supported Distribution System Planning;28
5.1;Introduction;28
5.2;Graph Theory and Minimum Spanning Tree;29
5.3;Description of Optimization;30
5.3.1;Mathematical Formulation of Optimization Problem;31
5.4;Simulation and Results;33
5.5;Conclusion;35
5.6;References;35
6;Sequencing Mixed-Model Assembly Lines with Limited Intermediate Buffers by a GA/SA-Based Algorithm;36
6.1;Introduction;36
6.2;Mathematical Models;37
6.2.1;Minimizing the Variation in Parts Usage;37
6.2.2;Minimizing the Makespan;37
6.3;Algorithms for Solving the Model;38
6.3.1;GA/SA-Based Algorithm Procedures;38
6.3.2;Implementation of the GA/SA-Based Algorithm;38
6.3.3;GA Algorithm;39
6.4;Case Studies and Discussions;39
6.5;Conclusions;42
6.6;References;43
7;Solving Vehicle Routing Problem Using Ant Colony and Genetic Algorithm;44
7.1;Introduction;44
7.2;Ant Colony Model;45
7.3;Vehicle Routing Problem;46
7.4;Ant Colony for VRP;47
7.5;Improved Ant Colony;48
7.6;Experimental Results;49
7.7;Conclusion;51
7.8;References;51
8;A Research on the Association of Pavement Surface Damages Using Data Mining;52
8.1;Introduction;52
8.2;Data Mining Application in Pavement Maintenance;53
8.3;Association Analysis;54
8.4;Case Study;56
8.4.1;The Application of Road Repairing Data;56
8.4.2;Applications of Road Damage Data;57
8.4.3;Discussion;58
8.5;Conclusion;58
8.6;References;59
9;An Integrated Method for GML Application Schema Match;60
9.1;Introduction;60
9.2;Related Work and Techniques;61
9.2.1;Schema Match;61
9.2.2;GML Application Schema Match;62
9.3;GML Application Schema Match;62
9.3.1;GML Application Schema Match;63
9.3.2;Linguistic-Based Element Match;64
9.3.3;Constraint-Based Element Match;65
9.3.4;Weighted Combination of Similarity;65
9.3.5;Structure-Level Match Based on Similarity Flooding;65
9.4;Power Management in DM-Sensors;66
9.5;Conclusion;67
9.6;References;67
10;Application of Classification Methods for Forecasting Mid-Term Power Load Patterns;68
10.1;Introduction;68
10.2;Data Collection and Preprocessing;69
10.3;Generating Representative Load Profiles Using K-Means;71
10.4;Classification Methods for Forecasting Load Patterns;72
10.4.1;CMAR (Classification Based on Multiple Association Rules);72
10.4.2;CPAR (Classification Based on Predictive Association Rules);72
10.4.3;Support Vector Machine;72
10.4.4;C4.5 (Decision Tree);73
10.5;Experiments and Results;73
10.6;Conclusion;74
10.7;References;74
11;Design of Fuzzy Entropy for Non Convex Membership Function;76
11.1;Introduction;76
11.2;Fuzzy Entropy;77
11.2.1;Preliminary Results;77
11.2.2;Non Convex Membership Function;78
11.3;Fuzzy Entropy of Non Convex Membership Function;79
11.4;Conclusions;81
11.5;References;81
12;Higher-Accuracy for Identifying Frequent Items over Real-Time Packet Streams;82
12.1;Introduction;82
12.2;Related Work;83
12.3;Sliding Window Model;83
12.3.1;Semantics of Sliding Windows;84
12.4;Dynamic Synopsis;84
12.4.1;Equal Synopsis and Unequal Synopsis;85
12.4.2;Dynamic Synopsis;86
12.4.3;Function Definition;86
12.5;Experiment and Evaluation;87
12.5.1;Experimental Results;87
12.6;Conclusions;88
12.7;References;89
13;Privacy Preserving Sequential Pattern Mining in Data Stream;90
13.1;Introduction;90
13.2;Problems Definition;90
13.3;Methodology;91
13.3.1;Algorithm Outline;91
13.3.2;Customer Sequence Encryption;92
13.3.3;Server Compares Patterns;93
13.3.4;Customer Decrypts Support;95
13.4;Analysis of Privacy Preserving and Communication Price;95
13.5;Conclusion;96
13.6;References;96
14;A General $\it{k}$-Level Uncapacitated Facility Location Problem;97
14.1;Introduction;97
14.2;Formulation for the k-GLUFLP;98
14.3;Computational Complexity and Algorithm of 2-GLUFLNP;99
14.4;An Algorithm for the k-GLUFLP;103
14.5;Conclusion;104
14.6;References;104
15;Fourier Series Chaotic Neural Networks;105
15.1;Introduction;105
15.2;Fourier Series Chaotic Neural Network (FSCNN);106
15.3;Research on Single Neural Unit;107
15.4;Application to Continuous Function Optimization Problems;108
15.5;Application to TSP;109
15.5.1;Application to 10-City TSP;109
15.5.2;Application to 30-City TSP;110
15.6;Conclusions;112
15.7;References;112
16;Numerical Simulation and Experimental Study of Liquid-Solid Two-Phase Flow in Nozzle of DIA Jet;113
16.1;Introduction;113
16.2;Theoretical Analysis of Liquid-Solid Two-Phase Flow in the Nozzle;114
16.3;Building Model and Numerical Method of Liquid-Solid Two-Phase Flow in the Nozzle;115
16.3.1;Physical Model of Liquid-Solid Two-Phase Flow in the Nozzle;115
16.3.2;Mathematical Model of Liquid-Solid Two-Phase Flow in the Nozzle;116
16.3.3;Mesh Division, Boundary Conditions and Numerical Method;117
16.4;Results and Analyses of Numerical Simulation;118
16.5;Experimental Research;119
16.5.1;Basis of Experiments and Equipment;119
16.5.2;Design of Experimental Project;120
16.5.3;Results and Analyses of Experiments;120
16.6;Conclusions;120
16.7;References;121
17;Shape Matching Based on Ant Colony Optimization;122
17.1;Introduction;122
17.2;Related Work;122
17.3;Ant Colon Optimization for Skeleton Matching;123
17.3.1;Topological Similarity;123
17.3.2;Shape Similarity;124
17.3.3;Skeleton Similarity;125
17.3.4;Ant Colon Optimization;126
17.4;Experiments;127
17.5;Conclusions and Future Work;128
17.6;References;128
18;A Simulation Study on Fuzzy Markov Chains;130
18.1;Introduction and Motivation;130
18.2;Basic Definitions for Fuzzy Markov Chains;130
18.2.1;General Discussion;132
18.3;Computation of the Fuzzy Stationary Distribution;133
18.4;Methodology of Simulation;134
18.5;Simulation Results;135
18.6;Concluding Remarks;136
18.7;References;137
19;A Tentative Approach to Minimal Reducts by Combining Several Algorithms;139
19.1;Introduction;139
19.2;Rough Sets Reduction Theory;140
19.3;A Discussion of Set Theory;141
19.4;Data Structure of Information System;141
19.5;Algorithm and Examples;142
19.6;Conclusion;144
19.7;References;145
20;Ameliorating GM (1, 1) Model Based on the Structure of the Area under Trapezium;146
20.1;Introduction;146
20.2;Modeling Mechanism of the Ameliorating GM (1, 1) Model;146
20.2.1;GM(1,1) Model;146
20.2.2;The Improved Structure of the Background Value;148
20.2.3;Calculate the Background Value;149
20.3;Example;150
20.4;Conclusion;152
20.5;References;152
21;Comparative Study with Fuzzy Entropy and Similarity Measure: One-to-One Correspondence;153
21.1;Introduction;153
21.2;Fuzzy Entropy and Similarity Measure Analysis;154
21.3;Entropy Derivation with Similarity Measure;156
21.3.1;Entropy Generation by Similarity;156
21.3.2;Relation of Similarity and Distance;157
21.4;Conclusions;158
21.5;References;159
22;Low Circle Fatigue Life Model Based on ANFIS;160
22.1;Introduction;160
22.2;Adaptive Network Based Fuzzy Inference Systems (ANFIS);160
22.3;Low Circle Fatigue Life Estimate Model Based on ANFIS;162
22.4;Discussions;164
22.5;References;164
23;New Structures of Intuitionistic Fuzzy Groups;166
23.1;Introduction;166
23.2;Preliminaries;167
23.3;Some Structures of I . Groups;169
23.4;Conclusions;173
23.5;References;173
24;An Illumination Independent Face Verification Based on Gabor Wavelet and Supported Vector Machine;174
24.1;Introduction;174
24.2;Algorithm Design;175
24.3;ATICR Illumination Normalization;176
24.4;Modeling for Support Vector Machine;177
24.5;Gabor Wavelets Selection;177
24.5.1;Gabor Feature;177
24.5.2;Gabor Feature Selection;177
24.5.3;Fusion Strategy;178
24.6;Experiments and Discussion;179
24.7;Conclusion;180
24.8;References;180
25;Hardware Deblocking Filter and Impact;182
25.1;Introduction;182
25.2;Video Error Origins;182
25.2.1;Wrong Macro Blocks Number Occurred;183
25.2.2;False Alarms;183
25.2.3;Incorrect Code Words;184
25.2.4;The Range of Quantization Coefficients;184
25.2.5;Unusual DC Coefficients;184
25.2.6;Coefficients Number;184
25.2.7;Wrong MB Blocks;184
25.2.8;Wrong Macro Blocks Number (in GOP);184
25.2.9;Illogical GOP Lengths;184
25.3;Deblocking Ability of GPU;184
25.4;Deblocking Impact;186
25.5;Conclusions;188
25.6;References;188
26;Medical Image Segmentation Using Anisotropic Filter, User Interaction and Fuzzy C-Mean (FCM);190
26.1;Introduction;190
26.2;Methodology;191
26.2.1;Noise Reduction;193
26.2.2;FCM;193
26.3;Implementation;195
26.4;Conclusion;196
26.5;References;196
27;Medical Image Segmentation Using Fuzzy C-Mean (FCM), Learning Vector Quantization (LVQ) and User Interaction;198
27.1;Introduction;198
27.2;Methodology;200
27.2.1;Noise Reduction;201
27.2.2;LVQ;201
27.2.3;FCM;202
27.3;Implementation;202
27.4;Conclusion;204
27.5;References;204
28;New Data Pre-processing on Assessing of Obstructive Sleep Apnea Syndrome: Line Based Normalization Method (LBNM);206
28.1;Introduction;206
28.2;Subjects;207
28.3;The Proposed Method;208
28.3.1;Line Based Normalization Method (LBNM) and Data Scaling Methods;208
28.3.2;C4.5 Decision Tree Classifier;209
28.3.3;Levenberg Marquart Artificial Neural Network (ANN);210
28.4;Empirical Results and Discussion;210
28.5;Conclusion;211
28.6;References;212
29;Recognition of Plant Leaves Using Support Vector Machine;213
29.1;Introduction;213
29.2;Extracting Leaf Features;214
29.2.1;Image Segmentation;214
29.2.2;Color Feature Extraction;215
29.2.3;Image Normalization;215
29.2.4;Texture Feature Extraction;216
29.3;Support Vector Machine (SVM);217
29.4;Experimental Results;218
29.5;Conclusions;219
29.6;References;220
30;Region Segmentation of Outdoor Scene Using Multiple Features and Context Information;221
30.1;Introduction;221
30.2;Multiple Features;222
30.3;Contextual Probability;223
30.4;Segmentation of Object Region;224
30.4.1;Segmentation of Sky and Cloud Region;224
30.4.2;Segmentation of Trees Region;224
30.4.3;Segmentation of Building Face Region;225
30.5;Experiment;227
30.6;Conclusion;227
30.7;References;228
31;Two-Dimensional Partial Least Squares and Its Application in Image Recognition;229
31.1;Introduction;229
31.2;Partial Least Squares;230
31.3;2DPLS;231
31.3.1;2DNIPLS;231
31.3.2;2DCOPLS;233
31.4;Experiments and Discussion;234
31.5;Conclusion;235
31.6;References;236
32;A Novel Method of Creating Models for Finite Element Analysis Based on CT Scanning Images;237
32.1;Introduction;237
32.2;Methods;238
32.2.1;Modeling for FE;238
32.2.2;Mesh Generation;239
32.2.3;FEA Validation;240
32.3;Results and Discussion;240
32.4;Conclusion;241
32.5;References;242
33;Accelerating Computation of DNA Sequence Alignment in Distributed Environment;243
33.1;Introduction;243
33.2;Methods for Sequence Computation;244
33.3;JavaParty for Parallel Computing;245
33.4;Multi-threads and Concurrent Computing;246
33.4.1;Multi-threads for Sequence Comparison;246
33.4.2;DNA Concurrent Computation;246
33.5;Results and Discussion;247
33.6;Conclusion;248
33.7;References;248
34;Predicting Protein Function by Genomic Data-Mining;250
34.1;Introduction;250
34.2;Methods;251
34.3;Results;252
34.4;Discussion;255
34.5;References;255
35;Tumor Classification Using Non-negative Matrix Factorization;257
35.1;Introduction;257
35.2;Methods;258
35.2.1;Non-negative Matrix Factorization;258
35.2.2;NMF Models for Gene Expression Data;259
35.3;Experimental Results;259
35.3.1;Datasets;260
35.3.2;Classification Results;260
35.3.3;Colon Cancer Data;261
35.3.4;Acute Leukemia Data Set;262
35.4;Conclusion;263
35.5;References;263
36;A Visual Humanoid Teleoperation Control for Approaching Target Object;265
36.1;Introduction;265
36.2;Overview of Teleoperation System;266
36.3;To Create Target Geometry in Maya;267
36.3.1;Virtual Robot;267
36.3.2;Virtual Furniture;267
36.3.3;Rendering/Animation of 3D Objects;267
36.3.4;The Dynamics Simulation;270
36.4;Controlling System for Virtual Scene;270
36.5;Conclusions;271
36.6;References;271
37;An Intelligent Monitor System for Gearbox Test;273
37.1;Introduction;273
37.2;System Architecture;274
37.2.1;System Requirement;274
37.2.2;System Structure;274
37.3;Work Principles;275
37.3.1;Full Automatic Test;275
37.3.2;System Communication;275
37.4;Software Structure;276
37.4.1;Shift Control Module;276
37.4.2;Data Processing Module;278
37.5;Conclusion;279
37.6;References;280
38;Development of Simulation Software for Coal-Fired Power Units Based on Matlab/Simulink;281
38.1;Introduction;281
38.2;Design of Simulation Algorithms Library;282
38.2.1;System Compartmentalization;282
38.2.2;Programming with Matlab CMEX;282
38.2.3;The Packaging of Module;284
38.3;Simulation Algorithms Library of Power Unit;285
38.4;Simulation Research;286
38.5;Conclusion;287
38.6;References;287
39;Inconsistency Management;289
39.1;Introduction;289
39.2;Background;289
39.3;Skills Evaluation;292
39.4;System Description;292
39.5;Conclusion;295
39.6;References;295
40;Neural Network-Based Adaptive Optimal Controller– A Continuous-Time Formulation;297
40.1;Introduction;297
40.2;The Optimal Control Problem;299
40.3;The Policy Iteration Algorithm;300
40.4;Online Neural Network-Based Approximate Optimal Control Solution on an Actor-Critic Structure;302
40.5;Relation of the Proposed Algorithm with Reward-BasedLearning Mechanisms in the Mammal Brain;304
40.6;Conclusion;305
40.7;References;305
41;On Improved Performance Index Function with Enhanced Generalization Ability and Simulation Research;307
41.1;Introduction;307
41.2;Analysis of Relationship between Generalization Ability and Structure, Training Method and Performance Index Function;308
41.3;On Improved Performance Index Function;309
41.4;Simulation Researches;310
41.5;Conclusion;313
41.6;References;314
42;A Fault Diagnosis Approach for Rolling Bearings Based on EMD Method and Eigenvector Algorithm;315
42.1;Introduction;315
42.2;Empirical Mode Decomposition;316
42.3;EVA of Blind Equalization;317
42.4;Fault Diagnosis Approach Based on EMD and EVA;319
42.5;Results;319
42.5.1;Tests on Extracting Impulses;319
42.5.2;An Inner Race Fault of Rolling Bearing;319
42.5.3;An Outer Race Fault of Rolling Bearing;321
42.6;Conclusions;321
42.7;References;322
43;An Adaptive Fault-Tolerance Agent Running on Situation-Aware Environment;323
43.1;Introduction;323
43.2;The Context: Situation-Aware Middleware and Fault Tolerance;324
43.3;Adaptive Fault-Tolerance Agent (AFTA);325
43.3.1;The AFTA Architecture;325
43.3.2;The Algorithm of AFTA;326
43.4;Simulating AFTA;329
43.5;Conclusions;329
43.6;References;330
44;Dynamic Neural Network-Based Pulsed Plasma Thruster (PPT) Fault Detection and Isolation for Formation Flying of Satellites;331
44.1;Introduction;331
44.2;Formation Flying Satellites;332
44.3;Formation Flying Fault Detection and Isolation System;334
44.3.1;Design of Neural Network FDI Scheme;334
44.3.2;Simulations Results of the FDI Schemes;337
44.4;Integrated Fault Detection and Isolation Scheme;338
44.5;Conclusions;341
44.6;References;341
45;Model-Based Neural Network and Wavelet Packets Decomposition on Damage Detecting of Composites;343
45.1;Introduction;343
45.2;Computation Model;344
45.3;NN Model;345
45.4;Experiment and Result;346
45.4.1;Samples and Experimental Setup;346
45.4.2;Training Performance of NN and Its Test;347
45.4.3;Test of Specimens;347
45.5;Conclusion;348
45.6;References;348
46;A High Speed Mobile Courier Data Access System That Processes Database Queries in Real-Time;350
46.1;Introduction;350
46.2;Wireless Technologies;351
46.2.1;Wireless Systems in Combination;352
46.2.2;Mobile PDAs and Access Points (APs);352
46.2.3;Mobile Worldwide Interoperability for Microwave Access (WiMAX);353
46.3;Security Technologies;353
46.4;The MCDA System Design;355
46.4.1;Experimentation;355
46.4.2;Results;356
46.4.3;Discussion of Results;356
46.5;Conclusions;356
46.5.1;Further Work;357
46.6;References;357
47;A Scalable QoS-Aware VoD Resource Sharing Scheme for Next Generation Networks;358
47.1;Introduction;358
47.2;Architecture of Scalable Qos-Aware Vod Resource Sharing Scheme;359
47.2.1;Fuzzy Cache Relay Node Selection Module;360
47.2.2;Estimation of Request Node’s Suitability to Serve as a Cache Relay Node;361
47.2.3;Prediction of Mobile Entities’ Stability;361
47.3;Simulation Results and Analysis;363
47.3.1;Simulation Result;363
47.4;Conclusions;365
47.5;References;365
48;Brain Mechanisms for Making, Breaking, and Changing Rules;366
48.1;The Need for Rules;366
48.2;Brain Regions;367
48.3;Network Theory;368
48.4;Probability Versus Frequency;369
48.5;Discussion;375
48.6;References;375
49;Implementation of a Landscape Lighting System to Display Images;377
49.1;Introduction;377
49.2;ImageDisplayMethod;377
49.3;Hardware Design;378
49.4;Software;378
49.4.1;PC Program;378
49.4.2;MASTER Program;379
49.4.3;SLAVE Program;380
49.5;TestandResults;381
49.5.1;PC Program;381
49.5.2;MASTER Control;381
49.5.3;SLAVE Control;382
49.5.4;MODULE;382
49.5.5;Test Results;383
49.6;Conclusion;383
49.7;References;384
50;Probability-Based Coverage Algorithm for 3DWireless Sensor Networks;385
50.1;Introduction;385
50.2;Sensor Threshold Model (STM);386
50.3;PKCCA Description;387
50.4;Simulation Results;388
50.5;Conclusions;391
50.6;References;392
51;Simulating an Adaptive Fault Tolerance for Situation-Aware Ubiquitous Computing;393
51.1;Introduction;393
51.2;Related Works;394
51.3;Adaptive Fault-Tolerance: Our Proposed Approach;395
51.3.1;RCSM;395
51.3.2;The Adaptive Fault Tolerance Architecture;396
51.4;Simulating AFT;398
51.5;Conclusions;399
51.6;References;399
52;A Hybrid CARV Architecture for Pervasive Computing Environments;401
52.1;Introduction;401
52.2;QOS Layered Model;402
52.2.1;RCSM(Reconfigurable Context-Sensitive Middleware);402
52.2.2;QOS Layered Model for Multimedia Distance Education System;404
52.2.3;Web Based Multimedia Distance Education System;405
52.2.4;Hybrid Software Architecture for Concurrency Control and URL Synchronization;406
52.3;Simulation Results;407
52.4;Conclusions;408
52.5;References;408
53;Image and Its Semantic Role in Search Problem;409
53.1;Introduction;409
53.1.1;Motivation;410
53.2;Existing Technologies for Image Searching;411
53.2.1;Existing Systems’ Review;411
53.2.2;Limitations of Existing Approaches;412
53.3;Proposed Strategy;412
53.3.1;The Search Problem Redefined;413
53.3.2;Nth Dimensional Image Structure;413
53.3.3;Semantic Role of Image in Search Problem;415
53.3.4;Towards Intelligent Image Retrieval;415
53.4;Experiments with JPEG File Format;416
53.5;Future Directions;417
53.6;References;418
54;Color Image Watermarking Scheme Based on Efficient Preprocessing and Support Vector Machines;419
54.1;Introduction;419
54.2;SVM;420
54.3;The Proposed PPSVMW Method;420
54.3.1;Representation of Images;420
54.3.2;Tsai’s SVM-Based Color Image Watermarking Method;421
54.3.3;Watermark Embedding;421
54.3.4;Watermark Extraction;422
54.4;Experimental Results;423
54.5;Conclusion;426
54.6;References;426
55;Multiple Ranker Method in Document Retrieval;428
55.1;Introduction;428
55.2;Problem for Learning to Rank in Document Retrieval;429
55.3;Multiple Ranker Method;430
55.3.1;Framework;430
55.3.2;Constructing Training Subsets;431
55.3.3;Ensemble of Rankers;431
55.4;Experiment;432
55.4.1;Data Collection;432
55.4.2;Experiment with OHSUMED Data;433
55.4.3;Experiment with .Gov Data;433
55.4.4;Discussions;434
55.5;Conclusion;434
55.6;References;435
56;An Elimination Method of Light Spot Based on Iris Image Fusion;436
56.1;Introduction;436
56.2;Iris Image Preprocessing;437
56.2.1;Iris Localization;437
56.2.2;Image Normalization;438
56.3;Image Registration and Fusion;438
56.3.1;Image Registration;438
56.3.2;Location of the Light Spots;440
56.3.3;Image Fusion;440
56.4;Experimental Results and Analysis;441
56.5;Conclusion;442
56.6;References;442
57;An Improved Model of Producing Saliency Map for Visual Attention System;444
57.1;Introduction;444
57.2;Model;445
57.2.1;Center-Surround Differences (On Center Difference and off Center Difference);446
57.2.2;Extraction of Early Visual Features;447
57.2.3;Combine the Feature Maps Finally into Saliency Map;448
57.3;Experiment Results and Discussion;450
57.4;References;451
58;Multiple Classification of Plant Leaves Based on Gabor Transform and LBP Operator;453
58.1;Introduction;453
58.2;Texture Features Extraction Using Gabor Transform;454
58.2.1;Gabor Wavelet Transform;454
58.2.2;Gabor Filter Dictionary Design;455
58.3;Local Binary Patterns;455
58.3.1;Gray Scale Invariant Local Binary Pattern;455
58.3.2;Rotation Invariant Local Binary Pattern;456
58.3.3;Uniform Local Binary Pattern;457
58.4;Experiments;457
58.5;Conclusions;459
58.6;References;459
59;Research on License Plate Detection Based on Wavelet;461
59.1;Introduction;461
59.2;Algorithm for License Plate Detection;462
59.2.1;Image Preprocessing;462
59.2.2;Wavelet Decomposition and De-noising;462
59.2.3;Gradient Images;464
59.2.4;Window Traversing and License Plate Image Segmentation;465
59.3;Experimental Results and Conclusion;466
59.4;References;466
60;Stereo Correspondence Using Moment Invariants;468
60.1;Introduction;468
60.2;Stereo Matching Approaches;469
60.3;Moment Invariant Based Stereo Matching;471
60.3.1;Moment Invariants;471
60.4;Experimental Results;472
60.5;Summary;473
60.6;References;474
61;The Application of the Snake Model in Carcinoma Cell Image Segment;476
61.1;Introduction;476
61.2;Theoretical Analysis;477
61.2.1;Basic Snake Model;477
61.2.2;Improved Snake Model;478
61.2.3;Characteristics of Esophageal Cancer Cell;479
61.3;Analysis of the Experimental Results;480
61.3.1;Comparison and Evaluation of Performance;480
61.3.2;Comparison of Several Snake Methods;482
61.4;Conclusion and Prospect;482
61.5;Innovations;482
61.6;References;482
62;Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems;484
62.1;Introduction;484
62.2;Literature Survey;485
62.3;Development of a CBR-Based Fuzzy Decision Tree;485
62.3.1;The Selection of Different Data Bases in UCI Dataset Library;486
62.3.2;A Case Based Weighted-Clustering Method;486
62.3.3;A Fuzzy Decision Tree Classification Model;487
62.3.4;The Judgment of Output Value;488
62.4;Experimental Results;488
62.4.1;Experimental Process;489
62.4.2;Method Comparisons;489
62.5;Conclusions;489
62.6;References;490
63;Multivariate Polynomials Estimation Based on GradientBoost in Multimodal Biometrics;492
63.1;Introduction;492
63.2;Multivariate Polynomials;493
63.3;GradientBoost;494
63.3.1;Single boost;495
63.3.2;Biased boost;495
63.3.3;FM boost;495
63.4;Normalization and Weighting;495
63.5;Experimental Results;496
63.6;Conclusions;498
63.7;References;498
64;An Introduction to Volterra Series and Its Application on Mechanical Systems;499
64.1;Introduction;499
64.2;Representation;499
64.2.1;First Order Volterra Systems;500
64.2.2;Second Order Volterra Systems;501
64.2.3;Determination of Kernels;503
64.3;Simulation;503
64.3.1;Volterra System to Simple Pendulum;504
64.3.2;Volterra System to a Non Linear Spring;505
64.4;Results;506
64.5;Conclusion;506
64.6;References;507
65;Skin Detection from Different Color Spaces for Model-Based Face Detection;508
65.1;Introduction;508
65.2;Color Space Transform;509
65.3;Skin Segmentation;510
65.4;Knowledge-Based Face Modeling and Detection;511
65.5;Results and Discussions;512
65.6;Conclusions;514
65.7;References;514
66;Applying Frequent Episode Algorithm to Masquerade Detection;516
66.1;Introduction;516
66.2;Related Work;517
66.3;Formula of Effectiveness;517
66.4;Frequent Episode;518
66.5;Experiment;519
66.5.1;Dataset;519
66.5.2;Experiment Results;519
66.6;Conclusions;520
66.7;References;520
67;An Agent-Based Intelligent CAD Platform for Collaborative Design;522
67.1;Introduction;522
67.2;Related Works;523
67.3;The Basic Principle and Key Method;524
67.3.1;Basic Principle;524
67.3.2;The Multi-agent System Development Based on JADE Platform;524
67.4;Implementation of Co-cad Platform;525
67.4.1;Communication Model;525
67.4.2;Design and Integration of Multimedia System;526
67.5;Case Study;527
67.6;Conclusion;528
67.7;References;529
68;Design of a Reliable QoS Requirement Based on RCSM by Using MASQ Architecture;530
68.1;Introduction;530
68.2;Related Works;531
68.3;The IPM_RQOS Model;531
68.3.1;RCSM;531
68.3.2;RCSM-Optional Component and Other Services;533
68.3.3;IPM_RQOS Model;533
68.4;Simulation Results and Conclusion;535
68.5;References;536
69;Minimization of the Disagreements in Clustering Aggregation;538
69.1;Introduction;538
69.2;The Classification of XML Documents;539
69.3;Description of Our Approach;540
69.3.1;Illustrative Example;540
69.3.2;Definitions;541
69.4;Clust-Agregat Algorithm;542
69.5;Complexity of Clust-Agregat Algorithm;543
69.6;Conclusion;544
69.7;References;544
70;Prediction of Network Traffic Using Multiscale-Bilinear Recurrent Neural Network with Adaptive Learning;546
70.1;Introduction;546
70.2;Multiresolution Wavelet Analysis;547
70.3;Multiscale-BLRNN with an Adaptive Learning;549
70.4;Experiments and Results;550
70.5;Conclusions;552
70.6;References;552
71;Replay Attacks on Han et al.’s Chaotic Map Based Key Agreement Protocol Using Nonce;554
71.1;Introduction;554
71.2;Review of Han et al.’s Nonce Based Protocol;555
71.3;Replay Attacks on Han et al.’s Nonce Based Protocol;556
71.3.1;Replay Attack 1;556
71.3.2;Replay Attack 2;558
71.3.3;Replay Attack 3;559
71.4;Conclusions;561
71.5;References;561
72;The Short-Time Multifractal Formalism: Definition and Implement;562
72.1;Introduction;562
72.2;Short-Time Singularity Exponent and Hausdorff Spectrum;563
72.2.1;Singularity Analysis of Windowed Signal;563
72.2.2;Dimension Based Short-Time Spectra;564
72.2.3;Grain Based Short-Time Spectra;565
72.3;Partition Function and Short-Time Legendre Spectrum;565
72.4;Implement of the Short-Time Legendre Spectrum: Short-Time Multifractal Spectra Based on WTMM Method;567
72.4.1;Wavelet Coefficients and Holder Regularity;567
72.4.2;Wavelet Coefficient Based Multifractal Formalism;567
72.5;Experimental Results and Discussion;568
72.6;References;569
73;Modified Filled Function Method for Resolving Nonlinear Integer Programming Problem;570
73.1;Introduction;570
73.2;A Modified Filled Function and Its Properties;571
73.3;Modified Filled Function Algorithm and Numerical Results;574
73.4;Conclusions;577
73.5;References;577
74;Author Index;578