Abstract: This paper describes a part of the integrating work between assembly design and assembly process planning domains (APP). The work is based, in its first stage, on modelling assembly features to support APP. A multi-layer architecture, based on feature-based modelling, is proposed to establish a dynamic and adaptable link between product design using CAD tools and APP. The proposed approach is based on deriving “specific function” features from the “generic” assembly and form features extracted from the CAD tools. A hierarchal structure from “generic” to “specific” and from “high level geometrical entities” to “low level geometrical entities” is proposed in order to integrate geometrical and assembly data extracted from geometrical and assembly modelers to the required processes and resources in APP. The feature concept, feature-based modelling, and feature recognition techniques are reviewed.
Abstract: When it comes to last, it is regarded as the critical foundation of shoe design and development. Not only the last relates to the comfort of shoes wearing but also it aids the production of shoe styling and manufacturing. In order to enhance the efficiency and application of last development, a computer aided methodology for customized last form designs is proposed in this study. The reverse engineering is mainly applied to the process of scanning for the last form. Then the minimum energy is used for the revision of surface continuity, the surface of the last is reconstructed with the feature curves of the scanned last. When the surface of a last is reconstructed, based on the foundation of the proposed last form reconstruction module, the weighted arithmetic mean method is applied to the calculation on the shape morphing which differs from the grading for the control mesh of last, and the algorithm of subdivision is used to create the surface of last mesh, thus the feet-fitting 3D last form of different sizes is generated from its original form feature with functions remained. Finally, the practicability of the proposed methodology is verified through later case studies.
Abstract: In this paper a one-dimension Self Organizing Map
algorithm (SOM) to perform feature selection is presented. The
algorithm is based on a first classification of the input dataset on a
similarity space. From this classification for each class a set of
positive and negative features is computed. This set of features is
selected as result of the procedure. The procedure is evaluated on an
in-house dataset from a Knowledge Discovery from Text (KDT)
application and on a set of publicly available datasets used in
international feature selection competitions. These datasets come
from KDT applications, drug discovery as well as other applications.
The knowledge of the correct classification available for the training
and validation datasets is used to optimize the parameters for positive
and negative feature extractions. The process becomes feasible for
large and sparse datasets, as the ones obtained in KDT applications,
by using both compression techniques to store the similarity matrix
and speed up techniques of the Kohonen algorithm that take
advantage of the sparsity of the input matrix. These improvements
make it feasible, by using the grid, the application of the
methodology to massive datasets.
Abstract: This paper presents features that characterize power
quality disturbances from recorded voltage waveforms using wavelet
transform. The discrete wavelet transform has been used to detect
and analyze power quality disturbances. The disturbances of interest
include sag, swell, outage and transient. A power system network has
been simulated by Electromagnetic Transients Program. Voltage
waveforms at strategic points have been obtained for analysis, which
includes different power quality disturbances. Then wavelet has been
chosen to perform feature extraction. The outputs of the feature
extraction are the wavelet coefficients representing the power quality
disturbance signal. Wavelet coefficients at different levels reveal the
time localizing information about the variation of the signal.
Abstract: Feature and model selection are in the center of
attention of many researches because of their impact on classifiers-
performance. Both selections are usually performed separately but
recent developments suggest using a combined GA-SVM approach to
perform them simultaneously. This approach improves the
performance of the classifier identifying the best subset of variables
and the optimal parameters- values. Although GA-SVM is an
effective method it is computationally expensive, thus a rough
method can be considered. The paper investigates a joined approach
of Genetic Algorithm and kernel matrix criteria to perform
simultaneously feature and model selection for SVM classification
problem. The purpose of this research is to improve the classification
performance of SVM through an efficient approach, the Kernel
Matrix Genetic Algorithm method (KMGA).