Knowledge-Based Intelligent Information and Engineering Systems: 9th International Conference, KES 2005, Melbourne, Australia, September 14-16, 2005,

✍ Scribed by Rajiv Khosla

• Publisher: Springer
• Year: 2005
• Tongue: English
• Leaves: 1008
• Series: Lecture Notes in Artificial Intelligence 3684
• Edition: 1
• Category: Library

✦ Synopsis

The four volume set LNAI 3681, LNAI 3682, LNAI 3683, and LNAI 3684 constitute the refereed proceedings of the 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2005, held in Melbourne, Australia in September 2005.

The 716 revised papers presented were carefully reviewed and selected from nearly 1400 submissions. The papers present a wealth of original research results from the field of intelligent information processing in the broadest sense; topics covered in the fourth volume are innovations in intelligent systems and their applications, data mining and soft computing applications, skill acquisition and ubiquitous human computer interaction, soft computing and their applications, agent-based workflows, knowledge sharing and reuse, multi-media authentication and watermarking applications, knowledge and engineering techniques for spatio-temporal applications, intelligent data analysis and applications, creativitiy support environment and its social applications, collective intelligence, computational methods for intelligent neuro-fuzzy applications, evolutionary and self-organizing sensors, actuators and processing hardware, knowledge based systems for e-business and e-learning, multi-agent systems and evolutionary computing, ubiquitous pattern recognition, neural networks for data mining, and knowledge-based technology in crime matching, modelling and prediction.

✦ Table of Contents

Title......Page 1
Preface......Page 4
KES 2005 Conference Organization......Page 6
International Program Committee......Page 8
Invited Session Chairs Committee......Page 10
IIHMSPWorkshop Organization Committee......Page 12
IIHMSPWorkshop Technical Committee......Page 14
KES 2005 Reviewers......Page 15
KES 2005 Keynote Speakers......Page 21
Table of Contents, Part IV......Page 22
Table of Contents, Part I......Page 32
Table of Contents, Part II......Page 47
Table of Contents, Part III......Page 62
1 Introduction......Page 77
2.1 Artificial Data Sets......Page 78
3 Results from Artificially Generated Data......Page 79
4 Results from Real World Data......Page 80
References......Page 82
1 Introduction......Page 84
2.1 Emergent Properties and Systems-Thinking......Page 85
2.2 The Idea......Page 86
3.1 Life of an Individual......Page 87
4 Example -- Symbolic Regression......Page 88
4.2 Autonomous EA......Page 89
References......Page 90
2 Speaker Verification......Page 91
4 Testing Voice Mimicry......Page 92
5.2 Results for the Non-professional Group......Page 93
References......Page 96
1 Introduction......Page 98
2 Wave-Based Bayesian Estimator......Page 99
3 Some Examples......Page 101
References......Page 103
1 Introduction......Page 105
2 Experimental Design......Page 106
3 Experiment Results......Page 107
4 Discussion......Page 109
References......Page 110
1 Introduction......Page 111
3 PSA Architecture......Page 112
4 Sample User Interactions......Page 114
5 Conclusion and Future Work......Page 116
References......Page 117
1 Introduction......Page 118
2 Conceptual Spaces Representation......Page 119
3.1 Concept Formation Using Clustering......Page 120
3.3 Concept Learning Using Classification......Page 121
Domain-Level Associations.......Page 122
References......Page 123
1 Introduction......Page 125
2.1 Assigning Slots......Page 126
2.2 Creation of Children......Page 127
3 Numerical Examples......Page 128
4 Summary and Conclusion......Page 131
References......Page 132
1 Introduction......Page 133
3.1 Implementation Details......Page 134
3.3 Experimental Results......Page 135
3.4 Comparison Against Wrapper and Ranking Methods......Page 137
References......Page 138
1 Introduction......Page 140
2.1 Definition and Notation......Page 142
3.1 Vector Space Representation......Page 143
3.2 CG-Based Representation......Page 144
3.3 Retrieval Method......Page 145
4 Example......Page 146
5.2 Results and Discussions......Page 147
Acknowledgement......Page 148
References......Page 149
1 Introduction......Page 150
2 Acquisition of Spark Voltage Waveforms......Page 151
3 Neural Network Fault Detection in RN7YC Samples......Page 153
5 Conclusions......Page 154
References......Page 156
1 Introduction......Page 157
2 Permutation Distance......Page 158
4 Result of Numerical Experiments......Page 160
References......Page 163
1 Introduction......Page 165
2.3 Reduction......Page 166
4 Illustrative Examples......Page 167
5.2 SARS Data Experiments and Results......Page 168
References......Page 169
2 Associative Classification......Page 170
3 Rule Mining Constraints......Page 171
5 The AIS-AC Approach......Page 172
6 Performance Evaluation......Page 174
References......Page 176
1 Introduction......Page 177
2 Fuzzy Association Rule Mining......Page 178
3 An Example......Page 180
4 Experimental Results......Page 181
5 Conclusion......Page 182
References......Page 183
1.1 Brief Review of Motion Observation......Page 184
2 Related Research......Page 185
2.2 Industrial Engineering (I.E.) Motion Study......Page 186
3.1 Wearable Sensor System......Page 187
4 Manual Generation......Page 188
5 Conclusions......Page 189
References......Page 190
1 Introduction......Page 191
2.2 Software......Page 192
2.4 Frequency Analysis of Two Motions......Page 193
2.6 Performance Test......Page 194
4 Conclusions......Page 197
References......Page 199
1 Introduction......Page 200
2 Biological Input to Skilled Performance Research......Page 201
3 Skill-Based Behavior Models Inspired by Biological Studies......Page 202
References......Page 203
1 Introduction......Page 205
2 Calculation of Self-load Using Cylindrical Human Body Model......Page 206
4.1 Self-load Measurement......Page 208
5 Conclusion......Page 210
References......Page 211
1 Introduction......Page 212
2.2 Applications of Learning with an Instructor......Page 213
2.3 Problems in Learning Clustering Rules......Page 214
3.1 Clustering Process for Exceptions......Page 215
3.3 Algorithm for Each Processing Part in Three Layers......Page 216
References......Page 217
1 Introduction......Page 219
2 Related Work......Page 220
3 Closed-Region Discrimination and Topological Model......Page 221
3.1 CRD (Closed-Region Discrimination)......Page 222
4 Applications......Page 223
References......Page 224
1 Introduction......Page 226
2 System Overview......Page 227
4 Performance Testing......Page 229
5 Application Agents......Page 230
References......Page 231
1 Introduction......Page 233
2 The Proposed Algorithm......Page 234
2.1 Maximum Queuing Delay......Page 235
3 Simulation Results......Page 236
3.1 Utilization and Loss Ratio Comparison......Page 237
3.2 Mean Delay and Its Jitter Comparison......Page 238
References......Page 239
1 Introduction......Page 240
1.4 CGA Execution......Page 241
2 Design Approach......Page 242
3.1 Output of Simulation 1......Page 243
3.3 Graphical Status of Genetic Operations......Page 244
References......Page 245
1 Introduction......Page 246
3 Self-organising Maps......Page 248
4 Experimental Results......Page 249
References......Page 253
1 Introduction......Page 255
2.2 ACC (Adaptive Cooperative Coevolution)......Page 256
3.1 Species Merging......Page 257
4 Experiments......Page 258
5 Conclusion......Page 260
References......Page 261
1 Introduction......Page 262
2.2 Ends-in Contrast Stretching......Page 263
2.3 Retinex......Page 264
3.1 Proposed Method for Face Image Preprocessing......Page 265
3.2 Feature Description Using Gabor Wavelet......Page 266
4 Experimental Results......Page 267
5 Concluding Remarks......Page 269
References......Page 270
1 Introduction......Page 271
2.2 YCbCr Color Model......Page 272
4 Simulation Result and Analysis......Page 273
References......Page 276
2 Collaborative Filtering......Page 277
3 Experiments......Page 278
3.1 Algorithms......Page 279
3.2 Discussion......Page 281
4 Conclusions......Page 282
References......Page 283
1 Introduction......Page 284
2.3 Existing Architecture......Page 285
4 Mechanism 2: Using Interaction Protocols (IPs) to Model Workflows......Page 286
4.1 Architectural Support for Interaction Protocols......Page 287
5 Choosing Process Models......Page 288
6 Conclusion......Page 289
References......Page 290
1 Introduction......Page 291
3 Summary of the Work......Page 292
3.1 Architecture of the Agent Based Workflow System......Page 293
4 Conclusions......Page 294
References......Page 295
1 Introduction......Page 297
2 Information Models and Ontologies......Page 298
3 Multi-agent Systems......Page 299
4 Hypothesis and Research Platform......Page 300
5 The Shared Ontology......Page 301
References......Page 302
1 Motivation......Page 304
3 Experience Factory Integration......Page 305
4 Reuse on a Project Basis......Page 307
References......Page 310
2 The Structure of the Hybrid Approach......Page 311
3 The Procedure of the Hybrid Approach......Page 312
References......Page 317
1.1 Transferring Tacit Knowledge......Page 318
2 Active Software......Page 319
3.1 An Augmented Active Software Framework and Its Interrelations......Page 320
4 Active Software Supporting Software Implementation-- An Illustration......Page 321
4.2 How Could A-Bank Benefit from an Augmented Active Software?......Page 322
6 Perspectives......Page 323
References......Page 324
1 Introduction......Page 325
2 Related Works......Page 326
3 The LZWS-05 Algorithm......Page 327
4 The LZFGS-05 Algorithm......Page 328
5 Experiments......Page 329
6 Conclusions......Page 330
References......Page 331
1.1 Some Current Security Models......Page 333
1.3.2 Read / Write Clearance of Subjects......Page 334
2.1 Definitions......Page 335
2.2 Rules......Page 336
2.3 Theorems and Corollary......Page 337
References......Page 338
1 Introduction......Page 339
2 Proposed Localization Method......Page 341
3 Results and Discussion......Page 343
References......Page 347
1 Introduction......Page 348
2 Feature Extraction......Page 349
3 Impulse Response of FIR System Characterizing Seal Imprint......Page 351
4 Seal Imprint Verification Algorithm......Page 352
5 Experiments......Page 353
6 Conclusion......Page 355
References......Page 356
1 Introduction......Page 357
2 Review of Tseng-Jan's Scheme......Page 358
3 Cryptanalysis of Tseng-Jan's Scheme......Page 359
4 Proposed Scheme......Page 360
Message Recovery Phase:......Page 361
5 Security Analysis......Page 362
6 Conclusion......Page 363
References......Page 364
1 Introduction......Page 365
3 Analysis of Informative and Noise-Like Regions......Page 366
3.2 Criterion to Segment a Bit-Plane into Informative and Noise-Like Regions......Page 367
4 BPCS-Steganography......Page 369
5.2 General Property of BPCS-Steganography......Page 370
6.1 Confidential Data Storage and Communication......Page 372
6.2 Forgery Detection of Digital Certificate Document......Page 373
6.3 Media Database System Application......Page 374
References......Page 375
1 Introduction......Page 376
WM Embedding.......Page 377
WM Detection.......Page 378
3.1 Principle of the Proposed Method......Page 379
3.2 Process Flow......Page 380
BER of Region.......Page 381
4.2 Results......Page 382
References......Page 384
1 Introduction......Page 385
2.1 Harris Corner Detector......Page 386
2.3 Scale-Invariant Keypoint Extractor......Page 387
3 Watermark Synchronization Using Feature Extraction......Page 388
4 Evaluation Results......Page 389
References......Page 392
1 Introduction......Page 393
2.1 The Review of the Patchwork Algorithm......Page 394
3.1 Symmetric Modulo Operation......Page 395
3.2 Improved Patchwork Algorithm......Page 396
4 Experimental Results......Page 397
References......Page 399
1 Introduction......Page 400
2.1 Geographical Ontologies......Page 401
2.3 Spatial Data Warehouses and Spatial Data Mining......Page 402
4 Conclusions......Page 403
References......Page 404
1 Introduction......Page 405
2 Related Work......Page 406
3.1 Map Personalization......Page 407
3.3 Progressive Vector Transmission......Page 408
4 Evaluation......Page 409
References......Page 411
1 Introduction......Page 412
3 Features and Issues for Geospatial Clustering......Page 413
References......Page 417
1 Introduction......Page 419
2 Related Work......Page 420
3 Spatio-temporal Modeling for Moving Objects in Video Data......Page 421
4 Content- and Semantic-Based Retrieval Based on Moving Objects......Page 422
5 Experimental Results......Page 424
6 Conclusions......Page 426
References......Page 427
1 Introduction......Page 428
2 Advantages of Types and Static Type Checking......Page 429
3 A Unifying View of Calendars and Topologies......Page 430
4 Modelling Calendars as Types in CaTTS......Page 432
5 Topological Types......Page 433
References......Page 434
1 Introduction......Page 435
2 Structure of the Proposed Method......Page 436
3.2 Detection of Moving Objects......Page 437
3.3 Update of Reference Edge List......Page 438
4 Results......Page 439
References......Page 440
1 Introduction......Page 442
2.1 The General Framework......Page 444
2.3 Minimal and -Consistency......Page 445
3 Motion and Size Constraints on Topological Transitions......Page 446
4 Discussion and Further Work......Page 448
References......Page 449
1 Introduction......Page 450
2.1 Analysis Block......Page 451
2.2.2 Landform Classification......Page 453
3 Cartographic Knowledge Domain Implementation......Page 454
5 Conclusion......Page 455
References......Page 456
1 Introduction......Page 458
2 Fuzzy Neural Networks......Page 459
3 Electromagnetism-Like Mechanism......Page 460
4 Case Study......Page 461
References......Page 463
1 Introduction......Page 465
3.1 Association......Page 466
3.2 Consistency......Page 467
3.4.1 Wald-Wolfowitz Runs Test......Page 468
4.3 Wilcoxon Rank Sum Test......Page 469
4.4 Wald-Wolfowitz Runs Test......Page 470
References......Page 471
1 Introduction......Page 472
3 Fuzzy Cost Evaluation Model......Page 473
4 GRA Approach for Software Vender Selection......Page 475
5 Conclusions......Page 477
References......Page 478
2 Coarse-Level Requirement Analysis......Page 479
4 Coarse Selection......Page 480
5 Fine Selection......Page 482
6 Conclusions......Page 484
References......Page 485
2 Description......Page 486
3 Method......Page 487
4 Experimental Results......Page 490
References......Page 491
2 Etymology and Definitions......Page 493
3.2 Indicators and Measures: Toward the F-Measure......Page 494
Case of Categorization......Page 495
Case of Information Retrieval (IR)......Page 496
6 Conclusion......Page 497
References......Page 498
1 Introduction......Page 499
2.3 Handwriting in Computer Environments......Page 500
3 A Handwriting Tool to Support Creative Activities......Page 501
3.2 Architecture and Technical Challenges......Page 502
4 Related Works......Page 504
References......Page 505
1 Introduction......Page 506
2 Concept of Natural Storage......Page 507
3 Design of Appliances......Page 508
4.2 Natural Storage Viewer......Page 509
6 Related Work......Page 511
References......Page 512
1 Introduction......Page 513
2 Computerized Support for Meta-synthetic Support for Idea Generation......Page 514
2.2 Visualized Shared Memory for Group Argumentation......Page 515
2.3 Information Support......Page 516
3 GAE Practice in Xiangshan Science Conference......Page 517
References......Page 518
1 Introduction......Page 520
2.2 Experimental Process......Page 521
2.4 Experimental System......Page 522
4.1 Effects of Change in Communication Channel......Page 524
5 Conclusion......Page 525
References......Page 526
1 Introduction......Page 527
2 Understanding People with Dementia......Page 528
4 Creating a Story People with Dementia Live......Page 529
6 Person-Centered Care as Creative Problem Solving......Page 531
7 Conclusion......Page 532
References......Page 533
1 Introduction......Page 534
2 Related Work......Page 535
4.2 Interface......Page 536
4.3 Group Modeller......Page 537
4.5 Domain and Collaboration Model......Page 538
References......Page 539
2.1 Self-report......Page 541
2.3 Psycho Physiological Indices......Page 542
3 Research Goals......Page 543
4 Pilot Study......Page 544
5 Research Outline......Page 545
References......Page 546
1 Introduction......Page 548
2.2 Three Foraging Models: Trail, Attraction and Desensitization......Page 549
2.2.1 Trail Model: The Simplest Model......Page 550
2.2.2 Attraction Model: Intensifying Recruitment......Page 551
2.2.3 Desensitization Model: Avoiding Deadlock......Page 552
3.2 Stability of Foraging Behavior......Page 553
References......Page 554
1 Introduction......Page 556
2 Fundamental Mechanism of Emerging a Shape Information......Page 557
3.3 Simulation Results......Page 558
4.1 Representative Values for Simulation Conditions......Page 559
4.2 Reconstruction from a Shape Information......Page 560
4.3 Characteristics of Shape Reconstruction......Page 561
References......Page 562
1 Introduction......Page 563
2.2 Collision Detection and Collision Rate......Page 564
3.1 Synch-Alliance with Node-Type......Page 565
3.3 Node-Type Selection......Page 566
3.4 Self-tuning of the Number of Node-Type......Page 567
Coupled Stochastic Phase Dynamics: Outline.......Page 568
5.2 Simulation Results......Page 569
References......Page 571
1 Introduction......Page 572
2 Feature Recognition......Page 573
3 Data Exchange Issues......Page 575
4 Methodology......Page 576
5 Conclusions......Page 577
References......Page 578
2 Programmable Pipelined Queue......Page 579
3 Pattern Matching Algorithm......Page 581
Traditional Paradigm......Page 582
5 Conclusions......Page 583
References......Page 584
1 Introduction......Page 585
3 Performance Analysis......Page 586
4 Experimental Results......Page 590
References......Page 593
1 Introduction......Page 594
2.1 Optimal Fuzzy Systems......Page 595
2.2 Near-Optimal Fuzzy Systems Using Polar Clustering......Page 596
3 Experiment Result......Page 598
4 Conclusion......Page 599
References......Page 600
1 Introduction......Page 601
2.1 Eigenspace Mapping......Page 602
3 Fuzzy Logic Control System......Page 604
4 Experimental Result......Page 605
References......Page 606
1 Introduction......Page 608
2 Local Feature Analysis......Page 609
3 Proposed Method......Page 610
4 Experiments and Results......Page 615
5 Conclusions......Page 616
References......Page 617
1 Introduction......Page 618
3 Proposed Cluster Validity for Training of Feature Extractor......Page 619
4 Training of Feature Extractor......Page 622
6 Conclusion......Page 623
References......Page 624
1 Introduction......Page 625
2 Problem Formulation......Page 626
3.1 Optimal Controller Design......Page 627
Brief Explanation of FLS and FBF.......Page 628
Self-structuring Algorithm.......Page 629
3.3 Adaptive Law and Stability Analysis......Page 630
4 Conclusions......Page 632
References......Page 633
1 ROI Extraction Model......Page 635
2.1 PIM (Picture Information Measure)......Page 636
2.2 The Extraction of Difference Block......Page 637
3.1 Barcode ROI Extraction with PIM......Page 638
3.2 ROI Extraction Using Diff......Page 639
4 Analysis......Page 640
References......Page 641
1 Introduction......Page 642
2.1 Type-2 Membership Functions......Page 643
2.3 Fuzzy Inference Engine......Page 644
3 Simulation Results......Page 645
4 Conclusions......Page 647
References......Page 648
1 Introduction......Page 649
2 Some Common Definitions of Emergence......Page 650
3 "Tools" to Detect Emergence......Page 651
3.1 Emergent Patterns in Multi-agent Systems: Macro-level......Page 652
4 Emergence and Predictability: The Computational Mechanics View......Page 653
6 Conclusions......Page 654
References......Page 655
1 Introduction......Page 657
2.2 Animal Counting......Page 658
3.1 Local Resolution......Page 659
4 Annealing Sensor Networks......Page 660
5 Discussion......Page 661
References......Page 662
2 Information Theoretic Measures of Dependance......Page 663
2.2 Differential Entropy Estimation......Page 664
3.1 System Description......Page 665
3.2 Defining Correlations in a Multi-agent System......Page 666
3.3 Results......Page 668
4 Conclusion and Future Work......Page 669
References......Page 670
1 Introduction......Page 671
3.1 Pre-calibration......Page 672
3.3 In Use Calibration of Gyros and Accelerometers......Page 673
4.1 Algorithm......Page 674
4.3 Orientation Correction Using Accelerometers and Magnetometers......Page 675
5.2 Results and Discussion......Page 676
References......Page 677
1 Problem of Broadcasting Large Data......Page 678
3 Organization of the Heap Network......Page 679
References......Page 681
1 Introduction......Page 682
2 Old Method Evaluation......Page 683
3 New Methods for Improving DT Learning Data......Page 684
3.2 Data Copying Method......Page 685
References......Page 686
1 Introduction......Page 688
2.2 Intention Association Classes......Page 689
3.2 Classification Technique by Using Weight of Rules......Page 691
6 Conclusions......Page 692
References......Page 693
2 High-Speed Full-Text Search Using Bi-gram Index......Page 695
3.1 Dynamic Full-Text Search Algorithm......Page 696
3.2 Speed-up Retrieval Algorithm......Page 697
4.1 Dynamic Full-Text Search System......Page 698
5 Conclusion......Page 700
References......Page 701
1 Introduction......Page 702
2.2 CCGA......Page 703
3.1 Agents......Page 704
3.3 Parameters......Page 705
4 Experiments......Page 706
5 Conclusion and Perspectives......Page 707
References......Page 708
1 Introduction......Page 709
2.2 Objective......Page 710
2.4 Previous Work......Page 711
Quantitative vs. Qualitative.......Page 712
Time.......Page 713
4 Discussion and Conclusion......Page 714
References......Page 715
1 Introduction......Page 716
2 Window-Based Methods......Page 717
4 Ant Systems and Algorithms......Page 718
5.1 The Ant System Shared Memory......Page 719
5.3 Addition Sequence Characteristics......Page 720
7 Conclusion......Page 721
References......Page 722
1 Introduction......Page 723
2.2 How Does Metaheuristic Agent Exploit Frequency Information?......Page 724
3.1 Avoiding Search Space Symmetry......Page 725
3.2 The Crossover Operator......Page 726
4.1 COSATS Versus Simulated Annealing and Tabu Search......Page 727
5 Conclusions......Page 728
References......Page 729
1 Introduction......Page 730
2.1 Packing Quality......Page 731
2.3 Ant Colony Optimization Algorithm......Page 732
3 Empirical Results......Page 734
4 Conclusions and Future Work......Page 735
References......Page 736
1 Introduction......Page 737
2 Music and Multi-aspects......Page 738
3 Program Overview......Page 739
3.3 Aspect Mode......Page 740
4.1 The .Energy. Aspect......Page 741
6 Conclusion......Page 742
References......Page 743
1.1 Related Work......Page 744
2.2 Extraction Function f......Page 745
2.4 Content-Based Function......Page 746
3.3 Results......Page 747
5 Conclusions......Page 749
References......Page 750
2 Scott-Montague Models for Modal Logic......Page 751
3 Measure-Based Models for Modal Logic......Page 752
4.1 Axioms Not Generally Satisfied by Plausibility Functions......Page 753
4.2 Conjunctive Plausibility Functions......Page 754
4.3 Disjunctive Plausibility Functions......Page 755
4.5 Pseudo-possibility Measures......Page 756
References......Page 757
1 Introduction......Page 758
3 Tracking by Bayesian networks......Page 759
3.2 Probability Updating......Page 760
4 Experiments......Page 761
5 Discussion......Page 762
References......Page 763
1 Introduction......Page 765
2.2 A Case That Discriminative or Garbage Is Clear......Page 766
3.1 Evaluation on the Basis of Selected Features......Page 767
4 Discussion......Page 769
References......Page 770
1 Introduction......Page 772
2.1 Bag-of-Words Model with Meta-information......Page 773
2.2 Subclass Method on Bag-of-Words Model......Page 774
3 Experiments......Page 775
4 Discussion and Conclusion......Page 776
References......Page 778
1 Introduction......Page 779
2 Pressure Sensors on a Chair......Page 780
3.1 Conditions......Page 781
3.2 Experimental Results......Page 782
4 Conclusion......Page 783
References......Page 784
2 Signboard Recognition System......Page 785
3 Extraction of the Signboard Area from Scene Image......Page 786
4 Revise Distortion of the Signboard......Page 788
6 Conclusions......Page 789
References......Page 791
1 Introduction......Page 792
2.1 Bottom-Up Clustering......Page 793
2.2 Top-Down Clustering......Page 794
2.4 Neural Network Model Selection......Page 795
3.1 Experiments Using Artificial Data Set......Page 796
3.2 Experiments Using Automobile Data Set......Page 797
References......Page 798
1 Introduction......Page 799
2.2 Clustering Coefficients......Page 800
4.1 Data Acquisition......Page 801
4.4 Performance Evaluation......Page 802
5 Conclusion......Page 804
References......Page 805
1 Introduction......Page 806
2.1 Profit Sharing......Page 807
2.2 Bayesian Networks and Mixture Model of Them......Page 808
3 Procedures for Recognizing Environmental Changes and Improving the Agent's Policy......Page 809
4 Computer Simulations......Page 810
5 Empirical Results and Discussions......Page 811
References......Page 813
1 Introduction......Page 814
2.2 Neural Rhythm Generator......Page 815
3.2 Musculo-skeletal System......Page 816
4.1 Optimization of Rhythmic Torque Controller......Page 817
4.2 Parallel Optimization Method......Page 818
5 Generation of Walking Movement......Page 819
References......Page 820
1 Introduction......Page 821
2.1 SBSOM......Page 822
2.2 Label Confidence......Page 823
3 Experiment......Page 824
3.1 Results and Evaluation......Page 825
4 Related Work......Page 826
References......Page 827
2 Background Related to Environmental Marketing Research......Page 828
4.1 Learning Green Consumer Cluster Groups......Page 829
4.3 Affective (Attitudes on Brands and Companies) Variables......Page 830
References......Page 832
1 Introduction......Page 834
2.2 Power-Law Network......Page 835
2.4 Our Methodology......Page 836
3.2 Algorithm......Page 837
4.1 Analysis......Page 838
References......Page 839
1 Introduction......Page 841
2 Outline of My-Page Generation System......Page 842
3.2 Compression Method for Vector Space......Page 843
4.1 Test Environment and Condition......Page 844
4.2 Evaluation of Experimental Result......Page 845
References......Page 846
2.1 Background......Page 848
3.1 Framework......Page 849
3.2 Model Components......Page 850
5.1 Small-Worldness......Page 852
6 Conclusion and Future Work......Page 853
References......Page 854
1 Introduction......Page 855
2.2 Game Model and Decision-Making Items to Be Learned......Page 856
2.3 Conditions for Successful Decision-Makings......Page 857
3.2 Verification of Reproducibility......Page 858
References......Page 860
1 Introduction......Page 861
2 Outline of Research......Page 862
3.1 Data Used for Analyses......Page 863
3.4 Analysis of Keywords' Influences......Page 864
3.5 Analysis by Combining Text Information with Numerical Information......Page 865
4 Conclusion......Page 866
References......Page 867
1 Introduction......Page 868
3 Knowledge Modeling Related to Risks by Applying HHM Method......Page 869
4 Application Result of HHM Method for Risk Assessment in Security Field......Page 870
6 Finally......Page 873
References......Page 874
2 Evaluation of Existing and Proposed Method......Page 875
3.1 Development of PCSW Domain Model......Page 876
3.2 Creating Requirement Specifications Using Domain Model......Page 878
4.2 Evaluation......Page 880
References......Page 881
1 Introduction......Page 882
3 Crime Emergency Event Semantic Model CE2M......Page 883
3.1 Crime Event Ontology Model CE2M......Page 884
3.2.2 IEvent Level......Page 886
3.3 The Characteristic Features of CE2M......Page 887
References......Page 888
1 Introduction......Page 889
2.2 Brokerage and Spatial Density......Page 891
2.3 Temporal and Frequency Analysis of Links......Page 893
3 Conclusions and Future Directions......Page 896
References......Page 897
1.1 User Requirements for Decision Support for Criminal Investigation......Page 899
2.2 Identifying Relationships......Page 901
3.1 Initial Versions......Page 902
3.2 Extending the FLINTS System......Page 903
4 Conclusion......Page 904
References......Page 905
1 Introduction......Page 906
2.1 Language......Page 907
3.1 Annotated Arguments......Page 908
3.2 Attack Relation......Page 909
4 A Pluralistic or Multicultural Argument Example......Page 910
5 Implementation of LMA......Page 911
References......Page 914
1 Introduction......Page 915
2.1 The Analytical Framework of the Research......Page 916
3.1 Status Quo of Trust......Page 919
3.2 Relationships Among Some Variables......Page 921
4 Conclusion......Page 922
References......Page 923
2.1 Multivariable Process......Page 924
3.1 Evaluation Method for Tuning of PID Controller Based on Gain Margin/Phase Margin and Clonal Selection Algorithm......Page 925
4 Simulation Results and Discussions......Page 927
References......Page 928
1 Introduction......Page 930
3.1 AVR System......Page 931
3.2 GA-BF for AVR System......Page 932
4 Conclusion......Page 933
References......Page 934
1 Introduction......Page 936
2 A Fault Diagnosis System for Induction Motors......Page 937
3 Experiment and Analysis......Page 939
4 Concluding Comments......Page 940
References......Page 941
1 Introduction......Page 942
2 Classification......Page 943
3 Methodology......Page 944
4 Results and Discussion......Page 946
References......Page 949
1 Introduction......Page 951
2.2.1 Eye Detection......Page 952
2.3.2 Normalization......Page 953
2.4.2 Face Verification Using Template Matching......Page 954
3 Experimental Results......Page 955
References......Page 956
1 Introduction......Page 957
3 Watermark Embedding......Page 958
4 Watermark Extraction......Page 960
5 Experimental Results......Page 962
6 Conclusion......Page 963
References......Page 964
1 Introduction......Page 965
2.2 Spatial Scalability and Inter-layer Prediction......Page 967
2.3 SNR Scalability......Page 968
3.1 Temporal Scalability......Page 969
4 Conclusion......Page 970
References......Page 971
2 EFALCN......Page 972
2.2 Semantic Interpretation of EFALCN......Page 973
2.3 Reasoning Problems in EFALCN......Page 974
3 Reasoning Properties of EFALCN......Page 975
4 Related Work......Page 977
References......Page 978
1 Introduction......Page 979
2 Background......Page 980
3 Automated Operator Selection......Page 981
4 Experimental Results......Page 982
References......Page 985
1 Introduction......Page 986
2 Cellular Automata and Cryptography......Page 987
3 Weak Keys......Page 988
References......Page 990
Author Index......Page 992