• Journal of applied mathematics & informatics
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• v.25 no.1_2
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• pp.495-503
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• 2007

Symmetric interpolating refinable function vectors with compact support are of interest in several applications such as signal processing, image processing and computer graphics. It is known in [13] that orthogonal interpolating refinable function vectors can not be symmetric for multiplicity r = 2 and dilation d = 2. In this paper, we shall investigate symmetric interpolating refinable function vectors with compact support for multiplicity r = 2 and dilation d = 2 by omitting orthogonality. To illustrate our theorems and results in this paper, we shall also present some examples of symmetric interpolating refinable function vectors with compact support and high order of sum rules.

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.873-878
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• 2003

LS-SVM(Least-Squares Support Vector Machine) has been used as a promising method for regression as well as classification. Suykens et al.(2000) used only the magnitude of residuals to obtain SVs(Support Vectors). Suykens' method behaves well for homogeneous model. But in a heteroscedastic model, the method shows a poor behavior. The present paper proposes a new method to get SVs. The proposed method uses the variance of noise as well as the magnitude of residuals to obtain support vectors. Through the simulation study we justified excellence of our proposed method.

• Computational Structural Engineering
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• v.6 no.1
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• pp.99-105
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• 1993

A Stepped beam with immovable ends under forced vibrations with large amplitude is investigated by using the finite element method and the Ritz vectors. Unlike the Eigen vectors, the Ritz vectors are generated by a simple recurrence relation. Moreover the Ritz vectors yield much faster convergence with respect to the number of vectors used than the use of Eigen vectors. The computer program is developed for nonlinear analysis using Ritz vectors instead of Eigen vectors and numerical examples are analysed for deflections and natural frequencies of stepped beam under various support conditions. Results show that the proposed method is valid and efficient.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.4
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• pp.404-414
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• 2014

Yield prediction is important to manage semiconductor quality. Many researches with machine learning algorithms such as SVM (support vector machine) are conducted to predict yield precisely. However, yield prediction using SVM is hard because extremely imbalanced and big data are generated by final test procedure in semiconductor manufacturing process. Using SVM algorithm with imbalanced data sometimes cause unnecessary support vectors from major class because of unselected support vectors from minor class. So, decision boundary at target class can be overwhelmed by effect of observations in major class. For this reason, we propose a under-sampling method with minor class based SVM (MCSVM) which overcomes the limitations of ordinary SVM algorithm. MCSVM constructs the model that fixes some of data from minor class as support vectors, and they can be good samples representing the nature of target class. Several experimental studies with using the data sets from UCI and real manufacturing process represent that our proposed method performs better than existing sampling methods.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.285-292
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• 2008

Color histograms have been used as feature vectors to characterize the color features of given images, but they have a limitation in efficiency by generating high-dimensional feature vectors. In this paper, we present a method to reduce the dimension of the feature vectors by applying PCA (principal components analysis) to the color histogram of a given vehicle image. With SVM (support vector machine) method, the dimension-reduced feature vectors are used to recognize the colors of vehicles. After reducing the dimension of the feature vector by a factor of 32, the successful recognition rate is reduced only 1.42% compared to the case when we use original feature vectors. Moreover, the computation time for the color recognition is reduced by a factor of 31, so we could recognize the colors efficiently.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.1
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• pp.52-60
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• 2008

This research proposes a new strategy where documents are encoded into string vectors for text categorization and modified versions of SVM to be adaptable to string vectors. Traditionally, when the traditional version of SVM is used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text categorization, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and apply the modified version of SVM adaptable to string vectors for text categorization.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.490-493
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• 2002

In this paper, the old Fourier-Motzkin method (abbreviated as the old FH method from now on) is first modified to the form which can derive all minimal vectors as well as all minimal support vectors of nonnegative integer homogeneous solutions (i.e., T-invariants) for a matrix equation $Ax=b=0^{m{\times}1}$, $A\epsilonZ^{m{\times}n}$ and $b\epsilonZ^{m{\times}1}$, of a given Petri net, where the old FM method is a well-known and direct method that can obtain at least all minimal support solutions for $Ax=0^{m{\times}1}$ from the incidence matrix . $A\epsilonZ^{m{\times}n}$, Secondly, for $Ax=b\ne0^{m{\times}n}$ a new extended FM method is given; i.e., all nonnegative integer minimal vectors which contain all minimal support vectors of not only homogeneous but also inhomogeneous solutions are systematically obtained by applying the above modified FH method to the augmented incidence matrix $\tilde{A}$ =〔A,-b〕$\epsilon$ $Z^{m{\times}(n＋1)}$ s.t. $\tilde{A}\tilde{x}$ = 0^{m{\times}1}$ However, note that for this extended FM method we need some criteria to obtain a minimal vector as well as a minimal support vector from both of nonnegative integer homogeneous and inhomogeneous solutions for Ax=b. Then those criteria are also discussed and given in this paper.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.327-344
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• 2013

Beam string structure (BSS) is introduced as a new type of hybrid prestressed string structures. The composition and mechanics features of BSS are discussed. The main principles of wavelet packet transform (WPT), principal component analysis (PCA) and support vector machine (SVM) have been reviewed. WPT is applied to the structural response signals, and feature vectors are obtained by feature extraction and PCA. The feature vectors are used for training and classification as the inputs of the support vector machine. The method is used to a single one-way arched beam string structure for damage detection. The cable prestress loss and web members damage experiment for a beam string structure is carried through. Different prestressing forces are applied on the cable to simulate cable prestress loss, the prestressing forces are calculated by the frequencies which are solved by Fourier transform or wavelet transform under impulse excitation. Test results verify this method is accurate and convenient. The damage cases of web members on the beam are tested to validate the efficiency of the method presented in this study. Wavelet packet decomposition is applied to the structural response signals under ambient vibration, feature vectors are obtained by feature extraction method. The feature vectors are used for training and classification as the inputs of the support vector machine. The structural damage position and degree can be identified and classified, and the test result is highly accurate especially combined with principle component analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8C
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• pp.772-781
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• 2009

This paper proposes a novel algorithm for the efficient classification and retrieval of medical images. After color and edge features are extracted from medical images, these two feature vectors are then applied to a multi-class Support Vector Machine, to give membership vectors. Thereafter, the two membership vectors are combined into an ensemble feature vector. Also, to reduce the search time, Correlated Categories Vector is proposed for similarity matching. The experimental results show that the proposed system improves the retrieval performance when compared to other methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.2
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• pp.96-101
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• 2015

Support vector machine (SVM) has a strong theoretical foundation and also achieved excellent empirical success. It has been widely used in a variety of pattern recognition applications. Unfortunately, SVM also has the drawback that it is sensitive to outliers and its performance is degraded by their presence. In this paper, a new outlier detection method based on genetic algorithm (GA) is proposed for a robust SVM. The proposed method parallels the GA-based feature selection method and removes the outliers that would be considered as support vectors by the previous soft margin SVM. The proposed algorithm is applied to various data sets in the UCI repository to demonstrate its performance.