Applied Soft Computing Technologies: The Challenge of Complexity

1. Front Matter

   Pages I-XXXIII

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2. Plenary Presentations

   1. Front Matter

      Pages I-XXXIII

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   2. Applying Fuzzy Sets to the SemanticWeb: The Problem of Retranslation

      • Ronald R. Yager

      Pages 3-18

   3. Granular Computing: An Overview

      • Witold Pedrycz

      Pages 19-34

3. Classification and Clustering

   1. Front Matter

      Pages I-XXXIII

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   2. Parallel Neuro Classifier for Weld Defect Classification

      • S.V. Barai, Piyush Agrawal

      Pages 37-54

   3. An Innovative Approach to Genetic Programming—based Clustering

      • I. De Falco, E. Tarantino, A. Della Cioppa, F. Fontanella

      Pages 55-64

   4. An Adaptive Fuzzy Min-Max Conflict-Resolving Classifier

      • Shing Chiang Tan, M.V.C. Rao, Chee Peng Lim

      Pages 65-76

   5. A Method to Enhance the ‘Possibilistic C-Means with Repulsion’ Algorithm based on Cluster Validity Index

      • Juan Wachs, Oren Shapira, Helman Stern

      Pages 77-87

4. Optimization

   1. Front Matter

      Pages I-XXXIII

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   2. Design Centering and Tolerancing with Utilization of Evolutionary Techniques

      • Lukasz Zielinski, Jerzy Rutkowski

      Pages 91-98

   3. Curve Fitting with NURBS using Simulated Annealing

      • M. Sarfraz, M. Riyazuddin

      Pages 99-112

   4. Multiobjective Adaptive Representation Evolutionary Algorithm (MAREA) - a new evolutionary algorithm for multiobjective optimization

      • Crina Groşan

      Pages 113-121

   5. Adapting Multi-Objective Meta-Heuristics for Graph Partitioning

      • R. Baños, C. Gil, M.G. Montoya, J. Ortega

      Pages 123-132

5. Diagnosis and Fault Tolerance

   1. Front Matter

      Pages I-XXXIII

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   2. Genetic Algorithms for Artificial Neural Net-based Condition Monitoring System Design for Rotating Mechanical Systems

      • Abhinav Saxena, Ashraf Saad

      Pages 135-149

   3. The Applications of Soft Computing in Embedded Medical Advisory Systems for Pervasive Health Monitoring

      • Bing-Nan LI, Ming-Chui Dong, Vai Mang I

      Pages 151-160

   4. Application of Fuzzy Inference Techniques to FMEA

      • Kai Meng Tay, Chee Peng Lim

      Pages 161-171

   5. Bayesian Networks Approach for a Fault Detection and Isolation Case Study

      • G. Nunnari, F. Cannavò, R. Vrânceanu

      Pages 173-183

6. Tracking and Surveillance

   1. Front Matter

      Pages I-XXXIII

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SVMs have emerged as very successful pattern recognition methods in recent years [16]. SVMs have yielded superior performance in various applications such as; text categorization [15], and face detection [12], content-based image retrieval [6], and learning image similarity [4]. The motivation here is detection of suspicious bags in a security situation. The usual method for bomb personnel is to blow up a suspicious bag, and any explosives contained therein. However, if the bag contains chemical, biological or radiological canisters, this may lead to disastrous results. Furthermore, the “bl- up” method also destroys important clues such as fingerprints, type of explosive, detonators and other signatures of importance for forensic analysis. Extraction of the bag contents using telerobotics avoids these problems [8]. In a telerobotic system, it is advantageous to automate bag classification which is coupled to robotic tactics such as shaking out of the bags contents. For cooperative hum- robot interaction, in situations when autonomous capabilities fail, a human may be called in to distinguish the bag type. Here we focus on the autonomous operation of such a telerobotic system. In the autonomous mode, bags are classified by type using SVMs for the purpose of identifying initial manipulator grasp points. One side of the gripper must slide under the bag, and because of slippage, the grasp may fail. Thus, it is of importance to realize the type of bag, and the location of the opening before the grasp point assessment is made.

• Signal
• algorithm
• algorithms
• bioinformatics
• classification
• complexity
• evolution
• evolutionary computation
• genetic algorithm
• kernel
• layout
• neural network
• optimization
• robot
• robotics

• School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea

  Ajith Abraham

• Department of Applied Mathematics Biometrics and Process Control, University Gent, Belgium

  Bernard de Baets

• Dept. Automation Technologies, Fraunhofer IPK Berlin, Berlin, Germany

  Mario Köppen, Bertram Nickolay

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