Computational Intelligence for Engineering Systems Emergent Applications

✍ Scribed by SpringerLink (Online service); Ferreira, Judite.; Madureira, Ana; Vale, Zita

• Publisher: Springer Netherlands
• Year: 2011
• Tongue: English
• Leaves: 202
• Series: International series on intelligent systems control and automation--science and engineering 46
• Edition: 1st ed. 2011
• Category: Library

✦ Synopsis

Computational Intelligence for Engineering Systems provides an overview and original analysis of new developments and advances in several areas of computational intelligence. Computational Intelligence have become the road-map for engineers to develop and analyze novel techniques to solve problems in basic sciences (such as physics, chemistry and biology) and engineering, environmental, life and social sciences. The contributions are written by international experts, who provide up-to-date aspects of the topics discussed and present recent, original insights into their own experience in these fields. The authors also include methods that apply to diverse fields such as manufacturing, tourism, power systems, computer science, robotics, chemistry, and biology. Topics include: Simulation and evolution of real and artificial life forms; Self-organization; Models of communication and social behaviors; Emergent collective behaviors and swarm intelligence; Adaptive, complex and biologically inspired systems; Power Systems ; Web-based Applications; Knowledge discovery; Intelligent Tutoring Systems ; Decision support Systems; Intelligent Tutoring Systems.;Preface -- Intention Recognition with Evolution Prospection and Causal Bayes Networks, Luís Moniz Pereira and Han The Anh -- Scheduling a Cutting and Treatment Stainless Steel Sheet Line with Self-Management Capabilities, Ana Madureira, Ivo Pereira, Nelson Sousa, Paulo Ávila and João Bastos -- A sensor classification strategy for robotic manipulators using multidimensional scaling technique, Miguel F. M. Lima and J. A. Tenreiro Machado -- Collective-Intelligence and Decision-Making, Paulo Trigo and Helder Coelho;Analysis of Crossover Operators for Cluster Geometry Optimization, Francisco B. Pereira and Jorge M. C. Marques -- A Support Vector Machine based Framework for Protein Membership Prediction, Lionel Morgado, Carlos Pereira, Paula Veríssimo and António Dourado -- Modeling and Control of a Dragonfly-Like Robot, Micael S. Couceiro, N. M. Fonseca Ferreira and J.A. Tenreiro Machado -- Emotion Based Control of Reasoning and Decision Making, Luis Morgado and Graça Gaspar -- A Generic Recommendation System based on Inference and Combination of OWL-DL Ontologies, Hélio Martins and Nuno Silva -- GIGADESSEA - Group Idea Generation, Argumentation, and Decision Support considering Social and Emotional Aspects, Goreti Marreiros, Ricardo Santos and Carlos Ramos -- Electricity Markets: Transmission Prices Methods, Judite Ferreira, Zita Vale and Hugo Morais -- Computational Intelligence Applications for Future Power Systems, Zita Vale, Ganesh K. Venayagamoorthy, Judite Ferreira and Hugo Morais -- Index.

✦ Table of Contents

2 Computational Intelligence Methods in Power Systems......Page 3
2.4 Module or Use......Page 5
2.7.1 Generalized Generation Distribution Factors......Page 7
Cover......Page 1
2 Methodologies for Transmission Cost Allocation......Page 2
Computational Intelligence for Engineering Systems......Page 4
Preface......Page 6
Intention Recognition with Evolution Prospection and Causal Bayes Networks......Page 10
1 Introduction......Page 11
2.1 Causal Bayes Networks......Page 13
3.4 Wide Area Monitoring and Control Systems (WAMCS)......Page 14
2.2 Intention recognition with Causal Bayesian Networks......Page 15
3.1 Preliminary......Page 17
3.1.3 Comparison of the Taxes Imputed to the Loads......Page 18
2.3 P-log......Page 19
2.5 Situation-sensitive CBNs......Page 22
2.6 Plan Generation......Page 23
3.1.2 Active Goals......Page 27
5 Conclusions and Future Work......Page 30
4.1 Elder Intention Recognition......Page 32
3.2 Reactive Power Management using a PSO Approach......Page 9
3 Case Study......Page 12
Contents......Page 8
References......Page 16
2.4 Recognizing Fox’s intentions - An Example......Page 20
2.7.1 Representation in the action language......Page 24
3.1 Preliminary......Page 26
3.1.4 A posteriori Preferences......Page 28
4 Intention Recognition and Evolution Prospection for Elder Care......Page 31
4.2 Evolution Prospection for Providing Suggestions......Page 36
5 Conclusions and Future Work......Page 39
References......Page 41
1 Introduction......Page 43
2 Nature Inspired Optimization Techniques......Page 44
3 Multi-Agent Systems......Page 45
4 Autonomic Computing......Page 46
5 AutoDynAgents System......Page 48
6.1 Description of the Production Process......Page 51
6.2 Scheduling Problem Description......Page 52
6.3 Simulation Plans and Computational Results......Page 53
References......Page 55
1 Introduction......Page 57
2 Experimental platform......Page 58
3.1 Multidimensional scaling......Page 60
3.2 The Correlation coefficient......Page 62
4.1 Analysis in the time domain......Page 63
4.2 Sensor classification......Page 65
5 Conclusion......Page 68
1 Introduction......Page 70
2 Multiple simultaneous goals and uncertain causality......Page 71
2.1 The preferences model and the causal effect pattern......Page 72
2.3 Results and prospects after this work......Page 74
3.1.1 The CvI collective and individual strata......Page 75
3.1.2 The CvI structure and dynamics......Page 76
3.2 The experimental scenario (ambulances and injured civilians)......Page 77
3.3 Results and prospects after this work......Page 78
4.1 TEMMAS agency design......Page 79
4.2 The experimental scenario (Iberian electricity market)......Page 80
4.3 Results and prospects after this work......Page 81
5.1 The experimental scenario (Fire-Brigade decision-making)......Page 82
5.2 Results and prospects after this work......Page 83
References......Page 84
1 Introduction......Page 86
2 Morse Potential......Page 87
2.1 Related Work......Page 88
3.1 Evolutionary Algorithm......Page 89
3.1.1 Representation and Genetic Operators......Page 90
4 Results and Discussion......Page 92
5 Conclusions......Page 97
A Support Vector Machine based Framework for Protein Membership Prediction......Page 99
1 Introduction......Page 100
2 SVMs with profile kernel......Page 101
3 System architecture......Page 103
3.1 The protein membership prediction algorithm......Page 104
3.2 Multi-agent implementation......Page 105
4.1 Learning efficiency......Page 108
4.2 Processing speed evaluation......Page 110
References......Page 111
1 Introduction......Page 113
2 State of the Art......Page 114
3 The Kinematics of the Dragonfly......Page 115
4 The Dynamics of the Dragonfly......Page 116
5 Dynamical Analysis......Page 117
6 Controller Performances......Page 124
7 Conclusion......Page 125
References......Page 126
1 Introduction......Page 128
2 Modeling Artificial Emotion......Page 129
2.1 The Flow Model of Emotion......Page 130
3.1 Internal Representational Structures......Page 132
3.2 Cognitive Space......Page 133
3.3 Modeling Emotional Dynamics......Page 134
4.1 Focusing Mechanisms......Page 135
4.1.1 Attention Focusing......Page 136
5.1 Emotional Memory......Page 137
5.2 Integrating Memory and Attention Mechanisms......Page 138
6 Discussion......Page 139
References......Page 140
1 Introduction......Page 143
2.1 Recommendation System......Page 144
2.2 OWL language and reasoning......Page 145
2.3 Reference architecture......Page 146
3.1 Sensor-based data......Page 147
3.2 Context categorization......Page 148
3.3 Recommendation......Page 150
3.4 Two-step generic recommendation......Page 151
4 Conclusions & future work......Page 153
References......Page 154
1 Introduction......Page 156
2.1 Idea Generation......Page 157
2.2 Argumentation......Page 158
2.4 Emotion......Page 159
3.1 Model......Page 160
3.2 Scenario......Page 161
4 Conclusions......Page 162
1 Introduction......Page 165
2 Methodologies for Transmission Cost Allocation......Page 166
2.1 Post-Stamp Method......Page 167
2.2 MW-Mile Method......Page 168
2.4 Module or Use......Page 169
2.5 Zero Counterflow......Page 170
2.7.1 Generalized Generation Distribution Factors......Page 171
2.7.2 Generalized Load Distribution Factors......Page 172
2.8.2 Bialek’s Tracing Methodology......Page 173
2.9 Locational Marginal Price......Page 174
2.9.1 Penalty Factors and Delivery Factors......Page 175
3 Case Study......Page 176
3.1 Results......Page 178
3.1.1 Comparison of the Taxes Imputed to the Transactions......Page 179
3.1.2 Comparison of the Taxes Imputed to the Generators......Page 180
3.1.3 Comparison of the Taxes Imputed to the Loads......Page 182
4 Conclusion......Page 183
References......Page 184
1 Power Systems – Present and Future......Page 185
2 Computational Intelligence Methods in Power Systems......Page 187
3.1 Ancillary Services Dispatch using a Genetic Algorithm Approach......Page 190
3.2 Reactive Power Management using a PSO Approach......Page 193
3.3 Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) Scheduling using PSO......Page 197
3.4 Wide Area Monitoring and Control Systems (WAMCS)......Page 198
4 Conclusions......Page 200