Genetic algorithms in computer aided design

Design is a complex engineering activity, in which computers are more and more involved. The design task can often be seen as an optimization problem in which the parameters or the structure describing the best quality design are sought.Genetic algorithms constitute a class of search algorithms especially suited to solving complex optimization problems. In addition to parameter optimization, genetic algorithms are also suggested for solving problems in creative design, such as combining components in a novel, creative way.Genetic algorithms transpose the notions of evolution in Nature to computers and imitate natural evolution. Basically, they find solution(s) to a problem by maintaining a population of possible solutions according to the ‘survival of the fittest’ principle. We present here the main features of genetic algorithms and several ways in which they can solve difficult design problems. We briefly introduce the basic notions of genetic algorithms, namely, representation, genetic operators, fitness evaluation, and selection. We discuss several advanced genetic algorithms that have proved to be efficient in solving difficult design problems. We then give an overview of applications of genetic algorithms to different domains of engineering design.     

Encapsulation of geometric functions for ship structural CAD using a STEP database as native storage

It is difficult to support collaborative design with a conventional ship CAD system that manages design information using files. The file storage, however, can be replaced with a commercial database management system. This paper describes OpenDIS, which is an interface between the geometric modeling kernel and the DBMS. The main purpose of OpenDIS is to implement a CAD system that has the STEP database as the native storage. A prototype CAD system has been implemented using the OpenCascade geometric modeling kernel and ObjectStore, a commercial object-oriented DBMS. The STEP methodology is used for the database schema. This CAD system has been applied to the hull design process of a ship in order to verify the usefulness of the interface.     

Comparative analysis of different approaches to target differentiation and localization with sonar

This study compares the performances of different methods for the differentiation and localization of commonly encountered features in indoor environments. Differentiation of such features is of interest for intelligent systems in a variety of applications such as system control based on acoustic signal detection and identification, map building, navigation, obstacle avoidance, and target tracking. Different representations of amplitude and time-of-flight measurement patterns experimentally acquired from a real sonar system are processed. The approaches compared in this study include the target differentiation algorithm, Dempster-Shafer evidential reasoning, different kinds of voting schemes, statistical pattern recognition techniques (k-nearest neighbor classifier, kernel estimator, parameterized density estimator, linear discriminant analysis, and fuzzy c-means clustering algorithm), and artificial neural networks. The neural networks are trained with different input signal representations obtained using pre-processing techniques such as discrete ordinary and fractional Fourier, Hartley and wavelet transforms, and Kohonen's self-organizing feature map. The use of neural networks trained with the back-propagation algorithm, usually with fractional Fourier transform or wavelet pre-processing results in near perfect differentiation, around 85% correct range estimation and around 95% correct azimuth estimation, which would be satisfactory in a wide range of applications.     

Object representation and recognition in shape spaces

In this paper, we describe a shape space based approach for invariant object representation and recognition. In this approach, an object and all its similarity transformed versions are identified with a single point in a high-dimensional manifold called the shape space. Object recognition is achieved by measuring the geodesic distance between an observed object and a model in the shape space. This approach produced promising results in 2D object recognition experiments: it is invariant to similarity transformations and is relatively insensitive to noise and occlusion. Potentially, it can also be used for 3D object recognition.     

Object-based image labeling through learning by example and multi-level segmentation

We propose a method for automatic extraction and labeling of semantically meaningful image objects using “learning by example” and threshold-free multi-level image segmentation. The proposed method scans through images, each of which is pre-segmented into a hierarchical uniformity tree, to seek and label objects that are similar to an example object presented by the user. By representing images with stacks of multi-level segmentation maps, objects can be extracted in the segmentation map level with adequate detail. Experiments have shown that the proposed multi-level image segmentation results in significant reduction in computation complexity for object extraction and labeling (compared to a single fine-level segmentation) by avoiding unnecessary tests of combinations in finer levels. The multi-level segmentation-based approach also achieves better accuracy in detection and labeling of small objects.     

Natural resonance-based feature extraction with reduced aspect sensitivity for electromagnetic target classification

This paper presents a model-based electromagnetic feature extraction technique that makes use of time-frequency analysis to extract natural resonance-related target features from scattered signals. In this technique, the discrete auto-Wigner distribution of a given signal is processed to obtain a partitioned energy density vector with a significantly reduced sensitivity to aspect angle. Each partition of this vector contains, in the approximate sense, spectral distribution of the signal energy confined to a particular subinterval of time. Selection of sufficiently late-time partitions provides target features with a markedly increased target discrimination capacity. The potential of the suggested technique and the practical issues in its implementation are demonstrated by applying it to realistic target classification problems with very encouraging results.     

Ant colony search algorithms for optimal polygonal approximation of plane curves

This paper presents a new polygonal approximation method using ant colony search algorithm. The problem is represented by a directed graph such that the objective of the original problem becomes to find the shortest closed circuit on the graph under the problem-specific constraints. A number of artificial ants are distributed on the graph and communicate with one another through the pheromone trails which are a form of the long-term memory guiding the future exploration of the graph. The important properties of the proposed method are thoroughly investigated. The performance of the proposed method as compared to those of the genetic-based and the tabu search-based approaches is very promising.     

Linear transform for simultaneous diagonalization of covariance and perceptual metric matrix in image coding

Two types of redundancies are contained in images: statistical redundancy and psychovisual redundancy. Image representation techniques for image coding should remove both redundancies in order to obtain good results. In order to establish an appropriate representation, the standard approach to transform coding only considers the statistical redundancy, whereas the psychovisual factors are introduced after the selection of the representation as a simple scalar weighting in the transform domain.In this work, we take into account the psychovisual factors in the definition of the representation together with the statistical factors, by means of the perceptual metric and the covariance matrix, respectively. In general the ellipsoids described by these matrices are not aligned. Therefore, the optimal basis for image representation should simultaneously diagonalize both matrices. This approach to the basis selection problem has several advantages in the particular application of image coding. As the transform domain is Euclidean (by definition), the quantizer design is highly simplified and at the same time, the use of scalar quantizers is truly justified. The proposed representation is compared to covariance-based representations such as the DCT and the KLT or PCA using standard JPEG-like and Max-Lloyd quantizers.     

A new method for representing and matching shapes of natural objects

A new method for the representation and comparison of irregular two-dimensional shapes is presented. This method uses a polar transformation of the contour points about the geometric centre of the object. The distinctive vertices of the shape are extracted and used as comparative parameters to minimize the difference of contour distance from the centre. Experiments are performed, more than 39 000 comparisons of database shapes, provided by Sebastian et al. (ICCV (2001) 755), are made and the results are compared to those obtained therein. In addition, 450 comparisons of leaf shape are made and leaves of very similar shape are accurately distinguished. The method is shown to be invariant to translation, rotation and scaling and highly accurate in shape distinction. The method shows more tolerance to scale variation than that of Sebastian et al. (ICCV (2001) 755) and is less computationally intense.     

Spatially eigen-weighted Hausdorff distances for human face recognition

Hausdorff distance is an efficient measure of the similarity of two point sets. In this paper, we propose a new spatially weighted Hausdorff distance measure for human face recognition. The weighting function used in the Hausdorff distance measure is based on an eigenface, which has a large value at locations of importance facial features and can reflect the face structure more effectively. Two modified Hausdorff distances, namely, “spatially eigen-weighted Hausdorff distance” (SEWHD) and “spatially eigen-weighted ‘doubly’ Hausdorff distance” (SEW2HD) are proposed, which incorporate the information about the location of important facial features such as eyes, mouth, and face contour so that distances at those regions will be emphasized. Experimental results based on a combination of the ORL, MIT, and Yale face databases show that SEW2HD can achieve recognition rates of 83%, 90% and 92% for the first one, the first three and the first five likely matched faces, respectively, while the corresponding recognition rates of SEWHD are 80%, 83% and 88%, respectively.