Trends in Applied Intelligent Systems, 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA AIE 2010, Cordoba, Spain, June 1-4, 2010, Proceedings, Part I

Cover......Page 1
Lecture Notes in Artificial Intelligence 6096......Page 2
Trends in Applied Intelligent Systems, Part I......Page 3
ISBN-13 9783642130212......Page 4
Preface......Page 6
Conference Organization......Page 8
Table of Contents – Part I......Page 14
Table of Contents – Part II......Page 22
Table of Contents – Part III......Page 30
Problem Formulation......Page 38
Search Space: A Direct Representation......Page 39
Fitness Function......Page 40
Neighborhood and Search Strategy......Page 41
Diversification......Page 43
Experimentation Conditions......Page 44
Conclusions......Page 45
References......Page 46
Introduction......Page 48
Knowledge Gathering Process......Page 50
The Optimization Algorithm......Page 51
The New Transition State Layer......Page 53
Results......Page 55
References......Page 57
Introduction......Page 58
Objective: Quality and Quantity of Transfer Opportunities......Page 59
Notations......Page 60
Objective: Quality and Quantity of Transfer Opportunities......Page 61
Solution Approach......Page 62
Parameters of Tabu Search for the SbrT......Page 63
Numerical Results......Page 64
Discussion......Page 65
References......Page 67
Introduction......Page 68
Problem Description......Page 69
Notation and Terminology......Page 70
Iterative Deepening A*......Page 71
Lower Bounds......Page 72
Probe Heuristics......Page 73
Experiments and Analysis......Page 75
References......Page 77
Introduction......Page 78
Combining MADS and F-Race......Page 79
The Hybrid of F-Race and MADS......Page 80
Case Study: MADS/F-Race vs. MADS(fixed)......Page 81
Comparison of MADS/F-Race to Random MADS(fixed)......Page 82
The Leave-One-Out Cross-Validation......Page 83
Comparison of MADS/F-Race to Tuned MADS(fixed)......Page 84
Conclusions......Page 86
References......Page 87
Introduction......Page 88
Audio VAD......Page 89
Visual VAD......Page 90
Visual VAD......Page 91
Audio-Visual VAD......Page 92
HARK-Based Implementation Block......Page 94
Evaluation......Page 95
Conclusion......Page 96
References......Page 97
Introduction......Page 99
Multi-channel Noise Reduction System InceIcra2010......Page 101
MFT-Based Automatic Speech Recognition System......Page 102
MFM Generation......Page 103
MFT-ASR......Page 105
Results......Page 106
Summary and Outlook......Page 107
References......Page 108
Introduction......Page 109
Executor System: PLEXIL and Universal Executive......Page 111
High Tier: PDDL-Like Planner......Page 113
Middle Tier: PLEXIL Executor......Page 115
Low Tier: Ptinto Hardware Control......Page 116
References......Page 117
Introduction......Page 119
End-Effector Pose without Joint Clearance......Page 120
Joint-Clearance Errors......Page 121
The End-Effector Pose Error Modeling......Page 122
Definitions......Page 123
The Branch and Bound Algorithm......Page 124
Experiments......Page 126
References......Page 127
Introduction......Page 129
Architecture components......Page 131
Down-Up-Down Behavior Generation (DUD)......Page 132
Evaluation......Page 136
Conclusion......Page 137
References......Page 138
Introduction......Page 139
Essential Functions for Music Robots......Page 140
System Architecture......Page 141
Beat Tracking Based on Spectro-temporal Pattern Matching......Page 142
Theremin Pitch Control by Regression Parameter Estimation......Page 143
Experiment 1: The Influence of the Theremin on Beat Tracking......Page 145
Experiment 2: Theremin Ensemble with a Human Drummer......Page 146
References......Page 148
Introduction......Page 150
Measuring Interference Cost......Page 151
Adjusting the Channels......Page 152
Results and Discussion......Page 154
Adjusting the Links......Page 155
Second Path Reduction Algorithm......Page 156
Results and Discussion......Page 158
References......Page 159
Introduction......Page 160
The Case Study......Page 161
Off-Line Labeling......Page 162
On-Line Filtering......Page 164
Conclusions and Related Work......Page 168
References......Page 169
Introduction......Page 170
Hybrid Automata and Safety Verification......Page 172
Robotic Setup and Software Agents......Page 173
Control Agents as Hybrid Automata......Page 174
Empirical Analysis......Page 176
Discussion and Related Works......Page 178
References......Page 179
Introduction......Page 180
Background......Page 181
Tutoring Environment Architecture......Page 182
Tutoring Module for Fault Diagnosis Training......Page 183
Tutoring Module for Restoration Training......Page 184
Modelling Power Systems Control Centre Operators According the New Reality......Page 187
Conclusions......Page 188
References......Page 189
Introduction......Page 191
A Multi-Agent System for Space Services......Page 192
On-Board Autonomy through Automated P&S Technology......Page 194
Spacecraft with On-Board Planning Capabilities......Page 195
Using Joscar for Onboard Replanning......Page 197
A Practical Case: Running the Demonstrator......Page 198
References......Page 200
Introduction......Page 201
The Problem......Page 202
Upper Bound $V^U_\alpha$......Page 206
Lower Bound $V ^L_α$......Page 208
Conclusion......Page 209
References......Page 210
Introduction......Page 212
Knowledge-Based Workflow Framework......Page 213
Estimate Processing Times......Page 215
Product Design......Page 216
Assign Material Cost......Page 217
Assign Manufacturing Cost......Page 218
Framework Evaluation......Page 219
References......Page 221
The Problem......Page 222
Financial Indicators......Page 223
Processing of Financial Indicators: Transformed Data......Page 225
The Decision Trees......Page 227
Validation of the Decision Trees......Page 229
Conclusions and Future Work......Page 230
References......Page 231
Industrial Motivation......Page 232
Lack of Technological Solutions......Page 233
From Legal Documents to Database Records......Page 234
Novelties in the Database Population Procedure......Page 237
Evidences......Page 239
Conclusions and Further Work......Page 240
References......Page 241
Introduction......Page 242
CO2RBFN for Time Series Forecasting......Page 243
Other Forecasting Methods......Page 246
Experimentation and Results......Page 247
Concluding Remarks......Page 249
References......Page 250
Processing Common Sense Knowledge......Page 252
Generator Module of ConceptNet......Page 253
Contexteller......Page 255
Steps to Create a Story......Page 256
Case Study......Page 257
Conclusions......Page 260
References......Page 261
Introduction......Page 262
The Adaptive QUESTOURnament System......Page 263
Genetic Algorithm to Estimate the Difficulty Level of the Challenges......Page 264
Design of the Genetic Algorithm......Page 265
The Fitness Function......Page 266
Experiments and Results......Page 267
Conclusions......Page 268
References......Page 269
Introduction......Page 272
Architecture Overview......Page 273
Methods......Page 274
Feature Extraction......Page 275
Experimental Results and Discussions......Page 277
References......Page 279
Introduction and Motivation......Page 281
Mapping E-Learning to a Planning and Scheduling Model......Page 282
Planning and Scheduling Learning Routes......Page 284
Planning Stage......Page 285
Experimental Results......Page 288
Conclusions......Page 289
References......Page 290
Introduction......Page 291
The Methodology......Page 292
E-Learning Factors......Page 293
Fuzzy ANP Application......Page 294
Real Cases......Page 297
References......Page 299
Introduction......Page 301
The Domain of Study......Page 302
Objectives of the Application......Page 303
The Static Knowledge of the Ontology......Page 304
The Dynamic Knowledge of the Ontology......Page 306
Outline of the Graphical Application......Page 308
Conclusions and Future Work......Page 309
References......Page 310
Introduction......Page 311
Related Work......Page 312
Semantic Similarity Measures Based on the Taxonomical Structure......Page 314
A New Measure to Compute the Semantic Similarity......Page 315
Evaluation......Page 316
Conclusions......Page 318
References......Page 319
Introduction......Page 321
Class Distribution Estimation......Page 322
Estimation Of Priors Based on Posterior Probabilities......Page 323
Hellinger Distance to Estimate Proportions......Page 324
Neural Network Classifier......Page 327
Quantification of the Damaged Sperm Cells......Page 328
Conclusions and Future Work......Page 329
References......Page 330
Introduction......Page 331
Database......Page 332
Feature Selection......Page 333
Decision Trees......Page 336
Support Vector Machines......Page 338
Conclusions......Page 340
References......Page 341
Introduction......Page 343
Theoretical Foundation......Page 344
An Environment for Data Analysis......Page 345
Information Extraction Module......Page 347
Evaluation: A Case Study......Page 349
Experiments with Sentence Classification......Page 350
Conclusion and Future Work......Page 351
References......Page 352
Introduction......Page 354
Methods......Page 355
Results and Conclusion......Page 361
References......Page 362
Introduction......Page 364
Class Imbalance Problems......Page 365
Undersampling......Page 366
Evaluation Measures......Page 368
Datasets......Page 370
References......Page 372
Introduction......Page 374
Related Work......Page 375
On-Line Segmentation of Time-Series Data......Page 376
On-Line Clustering of Detected Segments......Page 379
Comparative Method......Page 380
Calculation Time......Page 381
Labeling of Segmented Gesture Primitives......Page 382
Conclusion......Page 383
References......Page 384
Introduction......Page 385
People Candidate Blobs Detection......Page 386
People Candidate Blobs Refinement......Page 388
Data and Results......Page 390
References......Page 393
Introduction......Page 395
Background......Page 396
Our Approach......Page 397
Building Classifiers......Page 398
Evaluation......Page 401
Discussion......Page 402
References......Page 403
Introduction......Page 405
System Overview......Page 406
Feature Extraction......Page 408
Model Classification......Page 410
Test on Shape......Page 411
Test on the Shadow Pattern......Page 412
Results......Page 413
References......Page 414
Introduction......Page 415
Data Description......Page 416
Methodology......Page 417
Model Extraction......Page 418
Results......Page 419
Comparison of Methods......Page 420
Model Precision......Page 421
Conclusions and Future Work......Page 422
References......Page 423
Introduction......Page 425
Curvelet Transform......Page 426
Curvelet Based Feature Extraction......Page 427
Datasets......Page 428
Results......Page 429
References......Page 432
Introduction......Page 434
A Case Study of Legal Aspects in User Identification: E-Passports......Page 435
Context Aware Applications: An Abstract Model......Page 436
A Regulatory Model for the Use of Context-Aware Applications......Page 439
References......Page 441
Introduction......Page 444
Mobile Ad-Hoc Networks in CSCL Environments......Page 445
Proposed Scenario......Page 447
Scenario Description......Page 448
Conclusion and Future Work......Page 451
References......Page 452
Introduction......Page 454
CASAS Dataset......Page 455
Multiple-Resident Activity Recognition......Page 456
Conditional Random Field (CRF)......Page 457
Conditional Random Field with Iterative Inference......Page 458
Conditional Random Field with Decomposition Inference......Page 459
Experiment 1: Tendency of Data Preprocessing......Page 461
Conclusion......Page 462
References......Page 463
Introduction......Page 464
Data Processing......Page 465
High Resolution Range Profiles (HRRPs)......Page 466
Inverse Synthetic Aperture Radar Image (2D-ISAR)......Page 468
Methodology......Page 469
Class ID through HRRPs and ANNs......Page 470
Type ID Classification through ISAR Images and the 1-NN Algorithm......Page 471
References......Page 472
Introduction......Page 474
In Real Life......Page 475
Recommendation Systems......Page 476
VARDs......Page 477
Different Kinds of VARDs......Page 478
TESLAR. a RS with a 2D VARD......Page 479
Privacy......Page 481
Submission of Recommendations......Page 482
References......Page 483
Introduction......Page 485
Other Natural Language Understanding Systems......Page 486
Effect of Recognition Quality and Alternatives......Page 488
Effect of Interpretation Quality and Alternatives......Page 491
Impact of Recognition Quality on Interpretation......Page 492
References......Page 493
Introduction......Page 495
Supervisory Control and Data Acquisition System (SCADA)......Page 497
Automatic Failure Detection......Page 498
Automatic Failure Detection Procedure......Page 501
References......Page 503
Introduction......Page 505
Related Work......Page 506
Application and Data Preprocessing......Page 507
FLM-Rules......Page 509
Rule Selection......Page 510
Matching FLM-Rules Premisses in Data Streams......Page 511
Experimental Evaluation......Page 512
Conclusion and Perspectives......Page 513
References......Page 514
Introduction......Page 515
Related Work......Page 516
Delays in Internet-Based Monitoring and Control Systems......Page 518
User Access......Page 519
System Architecture Overview......Page 520
Information Model and Access......Page 521
Implementation Strategy......Page 522
Performance......Page 523
Concluding Remarks and Future Works......Page 524
References......Page 525
Introduction......Page 526
General Description of Simul-EMI II......Page 528
EMI Phenomena Modelling......Page 529
Data Mining and Automatic Generation of Parametric Models......Page 531
Reconstruction of the PCB Equivalent Schematic Circuit......Page 532
Tests and Validation......Page 533
References......Page 534
Introduction......Page 536
Preliminaries......Page 537
Results......Page 540
Numerical Example......Page 544
References......Page 545
Data Sets......Page 546
Creating and Selecting Informative n-Grams......Page 548
Entropy of n-Gram Collections......Page 549
Feature Selection......Page 550
Support Vector Machine......Page 551
ROC Analysis and Results......Page 552
Conclusion......Page 554
References......Page 555
Introduction......Page 557
Inverting Problem Presentation......Page 559
Negotiation Using Negotiable Feature Models: A Real Scenario......Page 561
Negotiation Strategies......Page 562
Experimental Results......Page 565
Conclusions......Page 566
References......Page 567
Introduction......Page 568
Problem Definition......Page 569
Related Work......Page 570
Classifier Learning......Page 571
Detecting Concept Drift......Page 572
Experimental Study......Page 573
References......Page 576
Introduction......Page 578
Performance Evaluation in Class Imbalance Problems......Page 579
Resampling......Page 580
Experiments......Page 581
Results on Data Sets with Severe Imbalance......Page 582
Results on Data Sets with Low/Moderate Imbalance......Page 583
Conclusions and Further Extensions......Page 584
References......Page 585
Introduction......Page 587
The ITSA Algorithm......Page 588
Experimental Setting and Analysis of Results......Page 589
Experimental Results......Page 590
References......Page 595
Introduction......Page 597
Interestingness Measures......Page 598
From Interestingness Measures to Classification Datasets......Page 600
Entropic Information Ratio: A New Interestingness Measure......Page 601
Experimental Evaluation......Page 602
Cross Validation Results......Page 603
Conclusions......Page 604
References......Page 605
Introduction......Page 607
Aggregating One-Dependence Estimators (AODE)......Page 609
Equal-width discretization discretization.......Page 610
Analysis and Results......Page 611
Conclusions and Future Work......Page 615
References......Page 616
Introduction......Page 617
Incremental Wrapper Subset Selection (IWSS)......Page 619
Re-ranking Based Incremental Wrapper Subset Selection......Page 620
Test Suite......Page 622
Experiments Results......Page 623
Conclusions and Future Work......Page 625
References......Page 626
Introduction......Page 627
Information Extraction......Page 628
Tools and Techniques......Page 629
Text Preprocessing......Page 630
Part-of-Speech Tagging......Page 631
Chunking......Page 632
Template......Page 633
Performance......Page 634
Conclusions and Future Work......Page 635
References......Page 636
Introduction......Page 638
Background and Related Work......Page 639
Evaluation in Imbalanced Classification......Page 640
Nested Generalized Exemplar Theory......Page 641
CHC Algorithm......Page 642
Selection of Generalized Examples Using the Evolutionary Model CHC......Page 643
Results and Analysis......Page 644
References......Page 646
Introduction......Page 648
Data Description......Page 650
Feature Selection Analysis......Page 651
Frequency Analysis......Page 652
References......Page 655
Introduction......Page 658
The FastXplain Algorithm......Page 659
Identification of Minimal Conflict Sets......Page 660
Algorithm......Page 662
Satisfaction Rate......Page 663
Number of Items......Page 664
Different Number of MCS......Page 665
Conclusions......Page 666
References......Page 667
Introduction......Page 668
Example: Recommender Knowledge Base......Page 670
Study: Understandability of Knowledge Representations......Page 672
Related Work......Page 676
References......Page 677
Introduction......Page 678
Working Example: Utility Constraints......Page 679
Constraint Set Adaptation as Optimization Problem......Page 682
Evaluations......Page 684
Related Work......Page 686
References......Page 687
Introduction......Page 688
Background......Page 689
System Description......Page 690
Simulation Trace......Page 693
References......Page 696
Introduction......Page 698
Existent Works about the Use of Ontologies on AmI Systems......Page 699
Ontology-Based Model Process for Home Automation Controlling......Page 700
OntoDomo Ontology......Page 701
DomoRules Tool......Page 702
IntelliDomo System Example Usage......Page 703
Conclusions and Future Lines......Page 705
References......Page 706
Introduction......Page 708
Vulnerability Description Standards......Page 709
The Proposed Ontology Structure......Page 710
Ontology Mapping......Page 712
Ontology Engagement for Security Evaluation......Page 713
Conclusion......Page 715
References......Page 716
Introduction......Page 718
Thermal Comfort and the Predicted Mean Vote......Page 719
Learning of Individual User Preferences......Page 720
Weighting of the Two Relevance Functions......Page 721
Identifying Suitable Field Studies......Page 722
Computing the Predicted Average Vote for Realistic Scenarios......Page 723
Experimental Results......Page 724
Conclusions......Page 726
References......Page 727
Introduction......Page 728
Model-Based Knowledge Discovery from Limited Customer Satisfaction Data......Page 729
Evaluating the Compatibility Construct......Page 732
The Decision Support Tool......Page 734
References......Page 736
Introduction......Page 738
Knowledge and Preferences Representation Language......Page 739
The WADEX Framework......Page 740
City Selection......Page 743
Application Domain: Travel Assistant Agent......Page 744
Conclusions......Page 746
References......Page 747
Introduction......Page 748
Basic Definitions......Page 750
An Algorithm for Distributed Diagnosis......Page 753
References......Page 756
Introduction......Page 758
Related Work......Page 759
Definitions and Problem Formulation......Page 760
Distributed Allocation Algorithm......Page 762
Algorithm Details......Page 763
Simulation Results......Page 765
References......Page 766
Introduction......Page 768
Problem Description......Page 769
Methodology Approach......Page 770
Case Study......Page 774
Conclusions......Page 776
References......Page 777
Introduction......Page 779
A Domain-Independent and Domain-Dependent Planner: New Heuristics......Page 781
H3: Dangerous Containers......Page 784
Evaluation......Page 786
Conclusions......Page 787
References......Page 788
Introduction......Page 789
Definitions......Page 791
Restricting Constraints......Page 792
Example......Page 793
A Tool for Finding Robust Solutions......Page 794
Evaluation......Page 795
Some Issues to Improve the Search of Robust Solutions......Page 796
Conclusions......Page 797
References......Page 798
Author Index......Page 800