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Über dieses Buch

This book bridges the gap between Soft Computing techniques and their applications to complex engineering problems. In each chapter we endeavor to explain the basic ideas behind the proposed applications in an accessible format for readers who may not possess a background in some of the fields. Therefore, engineers or practitioners who are not familiar with Soft Computing methods will appreciate that the techniques discussed go beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas. At the same time, the book will show members of the Soft Computing community how engineering problems are now being solved and handled with the help of intelligent approaches.

Highlighting new applications and implementations of Soft Computing approaches in various engineering contexts, the book is divided into 12 chapters. Further, it has been structured so that each chapter can be read independently of the others.



Chapter 1. Introduction

This chapter gives a conceptual overview of the soft computing techniques and optimization approaches, describing their main characteristics.
Margarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas

Chapter 2. Motion Estimation Algorithm Using Block-Matching and Harmony Search Optimization

Motion estimation is one of the major problems in developing video coding applications. Motion estimation is one of the major problems in developing video coding applications. On the other hand, block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame can be modeled as a translation of pixels contained in the previous frame. During this procedure is obtained a motion vector by minimizing a certain matching metric that is produced between the current frame and the previous frame. However, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the best-matching block within a search space. Harmony search (HS) algorithm is a metaheuristic optimization method inspired by the music improvisation process, in which a musician polishes the pitches to obtain a better state of harmony. In this chapter, a BM algorithm that combines HS with a fitness approximation model is presented. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate. In order to minimize computational time, the approach incorporates a fitness calculation strategy to decide which motion vectors can be only estimated or actually evaluated. Guided by the values of such a fitness calculation strategy, the set of motion vectors is evolved through HS operators until the best possible motion vector is identified. The presented method has been compared to other BM algorithms in terms of velocity and coding quality and its experimental results demonstrate that the algorithm exhibits the best balance between coding efficiency and computational complexity.
Margarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas

Chapter 3. Gravitational Search Algorithm Applied to Parameter Identification for Induction Motors

Induction motors represent the main component in most of the industries. Induction motors represent the main component in most of the industries. They use the biggest energy percentages in industrial facilities. This consume depends on the operation conditions of the induction motor imposed by its internal parameters. In this approach, the parameter estimation process is transformed into a multidimensional optimization problem where the internal parameters of the induction motor are considered as decision variables.
Margarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas

Chapter 4. Color Segmentation Using LVQ Neural Networks

Color segmentation in digital images is a challenging task due to image capture conditions. Typical segmentation algorithms present several difficulties in this process because they do not tolerate variations in color hue corresponding to the same object.
Margarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas

Chapter 5. Global Optimization Using Opposition-Based Electromagnetism-Like Algorithm

Electromagnetism-like Optimization (EMO) is a global optimization algorithm which allows to solve complex multimodal optimization problems.
Margarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas

Chapter 6. Multi-threshold Segmentation Using Learning Automata

Multi-threshold selection for image segmentation is considered as a critical pre-processing step for image analysis, pattern recognition and computer vision. This chapter explores the use of the Learning Automata (LA) algorithm to compute the thresholding points for segmentation proposes. LA is a heuristic method which is able to solve complex optimization problems with interesting results in parameter estimation. Different to other optimization approaches, LA explores in the probability space providing appropriate convergence properties and robustness. In this chapter the segmentation task is considered as an optimization problem and the LA is used to generate the image multi-threshold points. In this approach, one 1-D histogram of a given image is approximated through a Gaussian mixture model whose parameters are calculated using the LA algorithm. Each Gaussian function approximating the histogram represents a pixel class and therefore a thresholding point. Experimental results show fast convergence of the method, avoiding the typical sensitivity to initial conditions.
Margarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas

Chapter 7. Real-Time Gaze Control Using Neurofuzzy Prediction System

Real-time gaze control is a complicated task because it considers different dynamics behaviors of the elements involved in the process. This chapter describes the use of the adaptive network-based fuzzy inference system (ANFIS) model to reduce the delay effects in gaze control. The approach considers the modelling of the target objet location to predict the future positions, in order to diminish its delay consequences. The approach has been tested in a vision system of a humanoid robot. The predictions presented in the experimental results show that the object tracking performance is better in terms of velocity and accuracy.
Margarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas

Chapter 8. Clonal Selection Algorithm Applied to Circle Detection

Automatic circle detection in digital images is considered an important and complex task for the computer vision community. Consequently, recently, a tremendous amount of research has been devoted to find an optimal circle detector.
Margarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas

Chapter 9. States of Matter Algorithm Applied to Pattern Detection

Pattern Detection (PD) plays an important role in several image processing applications such as feature tracking, object recognition, stereo matching and remote sensing. PD involves two critical aspects: similarity measurement and search strategy. The simplest available PD method finds the best possible coincidence between the images through an exhaustive computation of the Normalized cross-correlation (NCC) values (similarity measurement) for all elements of the source image (search strategy).
Margarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas

Chapter 10. Artificial Bee Colony Algorithm Applied to Multi-threshold Segmentation

Image segmentation is a very important task in Computer Vision community, due to its capabilities for further steps that lead to recognizing patterns in digital images. Thus, the process of thresholding selection has become an interesting area, in recent years this procedure has been investigated as an optimization problem. On the other Hand, ABC is a nature inspired algorithm based on the intelligent behaviour of honey-bees which has been successfully used to solve complex real life optimization problems.
Margarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas

Chapter 11. Learning Automata Applied to Planning Control

Planning Control uses information regarding a problem and its environment to decide whether one plan is better than other in order to reach a required control objective. An interesting alternative for planning control is model predictive control (MPC) and the receding horizon control. MPC is the planning approach that has recently found a wide acceptance for industrial applications.
Margarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas

Chapter 12. Fuzzy-Based System for Corner Detection

Corner detection is an important task in computer vision problems due to the complexity of determinate the shape of different regions within an image. Real-life image data are always inexact due to inherent uncertainties that may arise from the imaging capture process such as defocusing, illumination changes, noise, etc. Therefore, the localization and detection of corners has become a difficult task under research, in order to accomplish the detection process under such imperfect situations. On the other hand, Fuzzy systems are well known for their efficient handling capacities when they face uncertainness and incompleteness. Fuzzy systems use modelling concepts in the same way as a human do. Under this circumstances, corners could be modelled by means of linguistic rules. This chapter presents a corner detection algorithm which employs fuzzy reasoning. The robustness of the presented algorithm is compared to well-known conventional corner detectors and its performance is also tested over a number of benchmark images to illustrate the efficiency of the algorithm under uncertainty.
Margarita-Arimatea Díaz-Cortés, Erik Cuevas, Raúl Rojas
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