E-Book, Englisch, Band 52, 514 Seiten
Castillo / Huang / Ao Intelligent Automation and Computer Engineering
1. Auflage 2010
ISBN: 978-90-481-3517-2
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, Band 52, 514 Seiten
Reihe: Lecture Notes in Electrical Engineering
ISBN: 978-90-481-3517-2
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark
A large international conference in Intelligent Automation and Computer Engineering was held in Hong Kong, March 18-20, 2009, under the auspices of the International MultiConference of Engineers and Computer Scientists (IMECS 2009). The IMECS is organized by the International Association of Engineers (IAENG). Intelligent Automation and Computer Engineering contains 37 revised and extended research articles written by prominent researchers participating in the conference. Topics covered include artificial intelligence, decision supporting systems, automated planning, automation systems, control engineering, systems identification, modelling and simulation, communication systems, signal processing, and industrial applications. Intelligent Automation and Computer Engineering offers the state of the art of tremendous advances in intelligent automation and computer engineering and also serves as an excellent reference text for researchers and graduate students, working on intelligent automation and computer engineering.
Autoren/Hrsg.
Weitere Infos & Material
1;Preface;6
2;Contents;7
3;1 A Linguistic CMAC vs. a Linguistic Decision Treefor Decision Making;11
3.1;1 Introduction;11
3.2;2 Label Semantics;12
3.2.1;2.1 Overview;12
3.2.2;2.2 Consonance Assumption;14
3.3;3 LCMAC Based on Label Semantics;15
3.3.1;3.1 Basic CMAC;15
3.3.2;3.2 Mapping with Linguistic Labels of Input Vectors;16
3.3.3;3.3 Response Mapping;17
3.3.3.1;3.3.1 Fine Grain Mapping;17
3.3.3.2;3.3.2 Overlapping Coarse Grain Mapping;18
3.4;4 The Convergence of the LCMAC;19
3.5;5 Comparing with an LDT;21
3.5.1;5.1 LDTs Based on Label Semantics;21
3.5.1.1;5.1.1 A Focal Element Linguistic Decision Tree;21
3.5.1.2;5.1.2 Dual-Edge LDTs;21
3.5.2;5.2 Functionally Equivalent to an LDT;22
3.5.3;5.3 Linguistic Interpretability;23
3.5.4;5.4 Different Training Processes;23
3.6;6 A Case Study;23
3.7;7 Conclusions;25
3.8;References;25
4;2 A Multiple Criteria Group Decision Making Modelwith Entropy Weight in an Intuitionistic Fuzzy Environment;27
4.1;1 Introduction;27
4.2;2 Preliminaries;28
4.2.1;2.1 Intuitionistic Fuzzy Sets;28
4.2.2;2.2 Entropy of IFS;29
4.3;3 Proposed Fuzzy TOPSIS Group Decision Making Model;30
4.4;4 Illustrative Example;33
4.5;5 Conclusion;35
4.6;References;35
5;3 Emergency HTN Planning;37
5.1;1 Introduction;37
5.2;2 Museum Guide Scenario;39
5.3;3 On-Line Planning in Dynagent;40
5.4;4 When Suspending an Action;40
5.5;5 Agent Algorithm;42
5.6;6 Stratified Multi-agent Interruption Planning;45
5.7;7 Experiments;47
5.8;8 Related Work;48
5.9;9 Conclusions;49
5.10;References;49
6;4 Adaptive Ant Colony Optimization with Cranky Ants;51
6.1;1 Introduction;51
6.2;2 Ant Colony Optimization;53
6.3;3 Adaptive Cranky Ant;54
6.4;4 Experiments and Discussions;56
6.4.1;4.1 Evaluation for Searching Performance;57
6.4.2;4.2 Evaluation for Processing Time;58
6.4.3;4.3 Evaluation for Adaptive Optimization;60
6.5;5 Conclusion;61
6.6;References;61
7;5 A Kind of Cascade Linguistic Attribute Hierarchiesfor the Two-Way Information Propagation and Its Optimisation;63
7.1;1 Introduction;63
7.2;2 Label Semantics;64
7.3;3 A Cascade Linguistic Attribute Hierarchy;66
7.3.1;3.1 Definition of a Cascade Hierarchy;66
7.3.2;3.2 Upwards Propagation of Information;67
7.3.3;3.3 Downwards Propagation of Information;67
7.4;4 GA in Wrapper to Optimise Cascade Hierarchies;68
7.4.1;4.1 Chromosomes and Reproduction;68
7.4.2;4.2 Evaluation and Termination Criteria;69
7.4.3;4.3 LID3 Algorithm for the Induction of an LDT;71
7.5;5 Experiments and Evaluation;72
7.5.1;5.1 On the Pima Diabetes Database;72
7.5.2;5.2 On the Wisconsin Breast Cancer Database;74
7.6;6 Conclusion;75
7.7;References;76
8;6 Simulation Optimization of Practical Concurrent Service Systems;77
8.1;1 Introduction;77
8.2;2 Petri Net Model of Service Systems;79
8.3;3 Formulation of the Optimization Problem;82
8.3.1;3.1 Objective Function;82
8.3.2;3.2 Management Constraints;82
8.3.3;3.3 Customer Satisfaction Constraints;83
8.4;4 Particle Swarm Optimization;84
8.5;5 Results of PSO Optimization;85
8.6;6 Conclusion;86
8.7;References;87
9;7 A New Improved Fuzzy Possibilistic C-Means Algorithm Based on Weight Degree;88
9.1;1 Introduction;88
9.2;2 Preliminary Theory;89
9.2.1;2.1 Fuzzy c-Means Clustering Algorithm;89
9.2.2;2.2 Possibilistic c-Means Clustering Algorithm;91
9.2.3;2.3 Fuzzy Possibilistic C-Means Clustering Algorithm;92
9.3;3 A Proposed Improved Fuzzy Possibilistic Clustering Algorithm;93
9.4;4 Experimental Results;95
9.4.1;4.1 Example 1 (Data Sets in );95
9.4.2;4.2 Example 2 (Data Sets in );99
9.5;5 Conclusion;100
9.6;References;100
10;8 Low Cost 3D Face Scanning Based on Landmarksand Photogrammetry;101
10.1;1 Introduction;102
10.2;2 Background;102
10.3;3 Proposed Approach;105
10.3.1;3.1 First Experimental Setup;106
10.3.2;3.2 Second Experimental Setup;108
10.4;4 Conclusions;112
10.5;References;113
11;9 3D Face Recognition and Compression;115
11.1;1 Introduction;115
11.2;2 Background;117
11.2.1;2.1 Discrete Wavelet Transform;118
11.2.2;2.2 Set Partitioning in Hierarchical Trees;118
11.2.3;2.3 Arithmetic Coding;120
11.2.4;2.4 Principal Component Analysis;120
11.2.5;2.5 Linear Discriminant Analysis;121
11.3;3 3D Face Compression and Recognition;122
11.4;4 Simulation and Results;123
11.5;5 Conclusion;127
11.6;References;128
12;10 Head Movement Quantification and Its Role in Facial Expression Study;129
12.1;1 Introduction;129
12.2;2 Head Motion During Facial Expression;130
12.3;3 Related Work;131
12.4;4 Quantifying Head Motion;134
12.4.1;4.1 Image Capturing;134
12.4.2;4.2 Feature Point Tracking;134
12.4.3;4.3 Establishing 3D Coordinate;136
12.4.4;4.4 Head Motion Quantification;137
12.5;5 Experiments;138
12.6;6 Results;139
12.7;7 Conclusions;141
12.8;References;141
13;11 Enhanced Audio-Visual Recognition System over Internet Protocol;144
13.1;1 Introduction;144
13.2;2 Background;146
13.2.1;2.1 Discrete Wavelet Transform;146
13.2.2;2.2 Multiband Feature Fusion Method;146
13.3;3 Radial Basis Function Neural Network;147
13.3.1;3.1 RBF Neural Network with Orthogonal Least Square;148
13.3.2;3.2 RBF Neural Network with Quadratic-Constant Orthogonal Least Square;149
13.4;4 AV Recognition System over IP;151
13.5;5 Experiment and Result;152
13.5.1;5.1 AV Recognition System with RBF Neural Network;152
13.5.2;5.2 AV Recognition System over IP;154
13.6;6 Conclusion;155
13.7;References;156
14;12 Lossless Color Image Compression Using Tuned Degree-K Zerotree Wavelet Coding;157
14.1;1 Introduction;157
14.2;2 Degree-K Zerotree Coding;159
14.2.1;2.1 Embedded Zerotree Wavelet Coding;160
14.2.2;2.2 Set-Partitioning in Hierarchical Trees Coding;161
14.2.3;2.3 Set-Partitioning Coding with Degree-K Zerotree;161
14.3;3 Tuned Degree-K Zerotree Wavelet Coder;162
14.3.1;3.1 Tuning Table;164
14.3.2;3.2 Spatial Orientation Tree Structure;165
14.3.3;3.3 Memory Requirements;167
14.4;4 Performance Evaluation;168
14.5;5 Conclusion;168
14.6;References;169
15;13 Motion Estimation Algorithm Using One-Bit-Transform with Smoothing and Preprocessing Technique;170
15.1;1 Introduction;171
15.2;2 Multiplication-Free 1BT (MF-1BT);173
15.3;3 The Smoothing Technique;174
15.4;4 The Preprocessing Technique;176
15.5;5 Simulation Results and Discussions;177
15.5.1;5.1 Determination of ThresholdS and ThresholdP;178
15.5.2;5.2 Simulation Results;179
15.5.3;5.3 Reduction in Number of Search Operations;181
15.6;6 Conclusion;182
15.7;References;182
16;14 Configuration of Adaptive Models in Arithmetic Coding for Video Compression with 3DSPIHT;183
16.1;1 Introduction;184
16.2;2 The SPIHT Algorithm;184
16.3;3 3DSPIHT for Video Compression;187
16.4;4 Configuration of Adaptive Models;188
16.5;5 Simulation Results and Discussions;191
16.5.1;5.1 QCIF Video Sequences;191
16.5.2;5.2 SIF Video Sequences;192
16.5.3;5.3 Adaptation of the Adaptive Models;192
16.5.4;5.4 Memory Requirement;192
16.6;6 Conclusion;196
16.7;References;196
17;15 Ad Hoc In-Car Multimedia Framework;197
17.1;1 In-Vehicle Systems and Bluetooth;197
17.2;2 Pervasive In-Vehicle Multimedia;198
17.3;3 Ad Hoc Framework Architecture;199
17.3.1;3.1 System Overview;199
17.3.2;3.2 Framework Design Concepts;200
17.4;4 mCAR Architecture;201
17.5;5 Framework Interfaces;203
17.5.1;5.1 Event System;203
17.5.2;5.2 Decision Module;204
17.5.3;5.3 Multimedia Protocol;205
17.5.4;5.4 Application Model;205
17.6;6 Framework QoS Challenges;206
17.7;7 Summary;208
17.8;References;208
18;16 Reliable Routing Protocol for Wireless Sensor Network;209
18.1;1 Introduction;209
18.2;2 Application Descriptions;210
18.3;3 Related Work;210
18.4;4 Self-organizing Network Survivability Routing Protocol;212
18.4.1;4.1 Start-Up Phase;213
18.4.1.1;4.1.1 Tree Creation Process;214
18.4.1.2;4.1.2 Child Discovery Process;215
18.4.2;4.2 Synchronization Phase;215
18.4.3;4.3 Message Exchange Phase;215
18.5;5 Results;215
18.6;6 Conclusion and Future Directions;219
18.7;References;220
19;17 802.11 WLAN OWPT Measurement Algorithmsand Simulations for Indoor Localization;221
19.1;1 Introduction;221
19.2;2 Related Work;223
19.2.1;2.1 Round Trip Time (RTT);223
19.2.2;2.2 One Way Propagation Time (OWPT);225
19.3;3 OWPT Synchronization and Time Measurement Resolution Improvement Algorithms;226
19.3.1;3.1 Introduction;226
19.3.2;3.2 OWPT Measurement Time Resolution Improvement;226
19.3.3;3.3 OWPT Synchronization Between AP and MS;228
19.4;4 OWPT Algorithms Simulation;230
19.4.1;4.1 Simulation Model;230
19.4.2;4.2 Simulation Results and Evaluation;231
19.5;5 Conclusion;233
19.6;References;233
20;18 Residual Energy Based Clustering for Energy Efficient Wireless Sensor Networks;234
20.1;1 Introduction;234
20.2;2 Wireless Sensor Network Model;236
20.3;3 Clustering Approach for WSN;236
20.4;4 The Proposed Centralized Method;238
20.5;5 The Proposed Distributed Method;240
20.6;6 Numerical Simulation;243
20.7;7 Conclusions;244
20.8;References;245
21;19 Fast Dissemination of Alarm Message Based on Multi-Channel Cut-through Rebroadcasting for Safe Driving;246
21.1;1 Introduction;246
21.2;2 Alarm Message Broadcasting;247
21.3;3 Related Works;248
21.4;4 Cut-Through Rebroadcasting for Alarm Message;249
21.4.1;4.1 The Characteristics and Assumptions for VANETs;250
21.4.2;4.2 Targets to be Achieved;250
21.4.3;4.3 Cut-Through Rebroadcasting;250
21.4.4;4.4 Waiting Time Calculation;252
21.5;5 Evaluation Results;253
21.5.1;5.1 Evaluation Scenario;253
21.5.2;5.2 Average Broadcasting Time;255
21.5.3;5.3 Number of Rebroadcasting Vehicles;255
21.6;6 Conclusion and Further Researches;257
21.7;References;258
22;20 Lightweight Clustering Scheme for Disaster Relief Wireless Sensor Networks;259
22.1;1 Introduction;259
22.2;2 Related Work;260
22.2.1;2.1 Sensor Node with Sleep Mode;261
22.2.2;2.2 Clustering-Based Protocol;261
22.3;3 Lightweight Clustering Scheme;262
22.3.1;3.1 Network and System Models;262
22.3.2;3.2 Cluster Head Election Algorithm;264
22.4;4 Performance Evaluation;265
22.4.1;4.1 Simulation Outline and Parameters;265
22.4.2;4.2 Simulation Results;268
22.5;5 Conclusion;269
22.6;References;270
23;21 On the Complexity of Some Map-Coloring Multi-player Games;272
23.1;1 Introduction;272
23.2;2 Three-Player Col;273
23.3;3 Three-Player Col Played on Trees Is NP-Complete;274
23.4;4 N-Player Snort;275
23.5;5 N-Player Snort Played on Bipartite Graphs Is PSPACE-Complete;276
23.6;References;278
24;22 Methods to Hide Quantum Information;280
24.1;1 Introduction;280
24.2;2 Quantum Data Hiding;281
24.2.1;2.1 Hiding the Information;282
24.2.2;2.2 Extracting the Information;285
24.2.3;2.3 Security Analysis;286
24.2.3.1;2.3.1 Intercepting the Encryption Key;286
24.2.3.2;2.3.2 Attack Against the Message;286
24.3;3 Quantum Digital Signature;287
24.3.1;3.1 Security Analysis;290
24.4;4 Conclusions;291
24.5;References;291
25;23 Analyses of UWB-IR in Statistical Models for MIMO Optimal Designs;292
25.1;1 Introduction;292
25.2;2 Statistical Models for MIMO UWB-IR;294
25.3;3 Channels Analyses with MIMO UWB-IR of Different Statistic Models;296
25.4;4 Simulations of MIMO UWB-IR of Different Channels with Statistic Models;298
25.5;5 Conclusions;304
25.6;References;306
26;24 Intruder Recognition Security System Using an Improved Recurrent Motion Image Framework;308
26.1;1 Introduction;308
26.2;2 Overview of the IRS System;309
26.3;3 Intruder Recognition Framework;310
26.3.1;3.1 Detection;311
26.3.2;3.2 Tracking;312
26.3.3;3.3 Classification;313
26.4;4 Control Center;317
26.5;5 Results;318
26.6;6 Conclusion;320
26.7;References;321
27;25 Automatic Recognition of Sign Language Images;322
27.1;1 Introduction;322
27.2;2 Related Work;323
27.3;3 Proposed Methodology;324
27.3.1;3.1 Canny Edge Detection;325
27.3.2;3.2 Clipping;327
27.3.3;3.3 Boundary Tracing;328
27.3.3.1;3.3.1 Identifying the Optimal y-Level;328
27.3.3.2;3.3.2 Identifying the Initial Trace Direction;328
27.3.3.3;3.3.3 Tracing with Appropriate Switch of Direction;329
27.3.3.4;3.3.4 Rejoining the Trace on Encountering Breaks;329
27.3.3.5;3.3.5 Finger Tip Detection;330
27.4;4 From Fingers to Signs;330
27.5;5 Implementation and Results;331
27.6;6 Conclusion and Future Work;332
27.7;References;332
28;26 Algorithm Using Expanded LZ Compression Schemefor Compressing Tree Structured Data;334
28.1;1 Introduction;335
28.2;2 Ordered Term Trees and Substitutions;337
28.3;3 LZ Compression Scheme for Tree Structured Data;338
28.4;4 Application of Our Lempel--Ziv Compression Scheme to XMill Compressor;342
28.5;5 Implementation and Experimental Results;343
28.6;6 Conclusion;346
28.7;References;347
29;27 Using Finite Automata Approach for Searching Approximate Seeds of Strings;348
29.1;1 Introduction;348
29.2;2 Preliminaries;349
29.3;3 Problem Formulation;350
29.4;4 Problem Solution;351
29.5;5 Time and Space Complexities;356
29.6;6 Experimental Results;360
29.7;7 Conclusion;361
29.8;References;361
30;28 Speech Recognizer with Dynamic Alternative Path Search and Its Performance Evaluation;362
30.1;1 Introduction;362
30.2;2 Overview of Constructing an FSA Language Model;363
30.2.1;2.1 Constructing an FSA Language Model;363
30.2.2;2.2 Basic Performance of a Generated FSA;366
30.2.2.1;2.2.1 Corpus and Speech Recognizer;366
30.2.2.2;2.2.2 Experiment for Closed Data;366
30.2.2.3;2.2.3 Experiment for Open Data;367
30.3;3 Dynamic Alternative Path Search;368
30.3.1;3.1 Dynamic Alternative Path Search;368
30.3.2;3.2 Experiment and Evaluation;369
30.3.3;3.3 Combining with a Post Filter;370
30.4;4 Conclusion;371
30.5;References;372
31;29 Text Mining Decision Elements from Meeting Transcripts;373
31.1;1 Introduction;373
31.2;2 The Corpus;376
31.3;3 The Ania Model;376
31.4;4 Text Analysis Theories Pertinent to the Analysis of Transcripts;377
31.5;5 Text Mining;378
31.5.1;5.1 Pre-processing;378
31.5.2;5.2 Transcript Analysis;379
31.5.2.1;5.2.1 Lexical Chaining;379
31.5.2.2;5.2.2 Segmentation;380
31.5.2.3;5.2.3 Extraction;380
31.5.2.4;5.2.4 Expected Applications;380
31.6;6 Evaluation;381
31.7;7 Conclusions;383
31.8;References;384
32;30 Double SVMSBagging: A Subsampling Approach to SVM Ensemble;387
32.1;1 Introduction;387
32.2;2 Support Vector Machine (SVM);389
32.2.1;2.1 Advantage of SVM Over Other Classifiers in Data Based Condition Diagnosis;390
32.2.2;2.2 Designing and Tuning of SVM in the Experiments;390
32.3;3 Double SVMSBagging: Double Subbagging with SVM;391
32.4;4 Data;393
32.5;5 Experimental Setup and Discussion of Results;393
32.5.1;5.1 Experiment to Get the Optimum SSR;394
32.5.2;5.2 Experiment with GIS Dataset;394
32.5.3;5.3 Experiment with the UCI Dataset;396
32.6;6 Conclusions;397
32.7;References;398
33;31 Clustering of Expressed Sequence Tag Using Globaland Local Features: A Performance Study;400
33.1;1 Introduction;400
33.2;2 Related Work;401
33.2.1;2.1 Methods Based on Word Frequencies;402
33.2.2;2.2 Methods Based on Information Theory;402
33.2.3;2.3 Methods Based on Compression Technique;403
33.2.4;2.4 Clustering Approaches in DNA Sequences;403
33.2.5;2.5 Recent Approaches in EST Clustering;404
33.3;3 Proposed Method;405
33.3.1;3.1 Dataset;406
33.3.2;3.2 Grammar-Based Sequence Distance;406
33.3.3;3.3 Distance Based on Generalized Relative Entropy;407
33.3.4;3.4 Evaluation of Clustering Quality in EST;408
33.4;4 Results and Discussion;408
33.4.1;4.1 Initial Evaluation of Features via Visualization;408
33.4.2;4.2 Evaluation with Hierarchical Clustering Algorithm;409
33.4.3;4.3 Comparison with the wcd Clustering Algorithm;411
33.5;5 Conclusion;412
33.6;References;412
34;32 Research on Process Algebraic Analysis Tools for Electronic System Design;414
34.1;1 Introduction;415
34.2;2 PAT: Process Analysis Toolkit;417
34.2.1;2.1 A Pipeline Process;417
34.2.2;2.2 PAT Model of the Pipeline Process;418
34.2.3;2.3 Verification;419
34.2.3.1;2.3.1 Observation and Simulation;420
34.3;3 SHE Methodology;421
34.3.1;3.1 POOSL;421
34.3.2;3.2 Tools for SHE;421
34.3.3;3.3 TLM Model: A CPU and Memory System;422
34.3.4;3.4 Simulation;422
34.3.5;3.5 Formal Verification;423
34.3.6;3.6 Interoperability;423
34.4;4 Summary;424
34.5;References;424
35;33 Behavioural Hybrid Process Calculus for Modelling and Analysis of Hybrid and Electronic Systems;427
35.1;1 Introduction;427
35.2;2 Behavioural Hybrid Process Algebra;429
35.3;3 Bhave Toolset;431
35.3.1;3.1 Application of BHPC;432
35.4;4 Visualisation of Hybrid Evolutions: Message Sequence Plots and msp-svg;434
35.5;5 Application of BHPC for Electronic Systems;437
35.6;6 Summary;439
35.7;References;440
36;34 Structured Robust Control for a Pmdc Motor Speed Controller Using Swarm Optimization and Mixed Sensitivity Approach;442
36.1;1 Introduction;442
36.2;2 DC Motor Modeling and the Proposed Technique;444
36.2.1;2.1 PSO Based Fixed Structure Robust Control;444
36.2.1.1;2.1.1 Controller's Structure Selection;444
36.2.1.2;2.1.2 Cost Function in the Proposed Technique;445
36.3;3 Design Example;446
36.4;4 Conclusions;452
36.5;References;453
37;35 Agents for User-Profiling, Information Filtering, and Information Monitoring;454
37.1;1 Introduction;455
37.2;2 A Holistic IR System;456
37.3;3 User Profiling Agent;457
37.3.1;3.1 Capturing User Profile;457
37.3.2;3.2 Updating User Profile;459
37.3.3;3.3 Reordering URLs;460
37.4;4 Web Browsing Agent;461
37.5;5 Web Monitoring Agent;462
37.6;6 Experimentation and Evaluation;463
37.6.1;6.1 Evaluating the UPA;464
37.6.2;6.2 Evaluating the WBA;465
37.7;7 Proof-of-Concept Examples;467
37.8;8 Discussion and Conclusion;469
37.9;References;470
38;36 Form-Based Requirement Definitions of Applications for a Sustainable Society;472
38.1;1 Introduction;472
38.2;2 Web Application Development;474
38.2.1;2.1 Basic Approaches;474
38.2.2;2.2 A UI-Driven Approach;474
38.2.3;2.3 A Model-Driven Approach;476
38.2.4;2.4 A Data-Driven Approach;477
38.3;3 Typical Applications;478
38.4;4 Issues on End-User Computing;478
38.5;5 Abstract Forms and Transformation;479
38.5.1;5.1 Form Transformation in XSLT;479
38.5.2;5.2 Form Transformation by Mapping;480
38.5.3;5.3 A Visual Tool for FTFT;483
38.6;6 Conclusion;484
38.7;References;484
39;37 A Dynamic Nursing Workflow Management System: A Thailand Hospital Scenario;486
39.1;1 Introduction;486
39.2;2 The DPWFM Architecture;487
39.3;3 Background of Thailand Hospital;488
39.3.1;3.1 General Workflow in Thailand Hospital;490
39.3.2;3.2 Case Study;492
39.4;4 Proposed DPWFM;494
39.5;5 Conclusions and Further Study;498
39.6;References;498
40;Author Index;499
