• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.141-148
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• 2011

Support vector machines have been extensively studied and utilized in pattern recognition area for years. One of interesting applications of this technique is music/speech classification for a standardized codec such as 3GPP2 selectable mode vocoder. In this paper, we propose a novel approach that improves the speech/music classification of support vector machines. While conventional support vector machine optimization techniques apply during training phase, the proposed technique can be adopted in classification phase. In this regard, the proposed approach can be developed and employed in parallel with conventional optimizations, resulting in synergistic boost in classification performance. We first analyze the impact of kernel width parameter on the classifications made by support vector machines. From this analysis, we observe that we can fine-tune outputs of support vector machines with the kernel width parameter. To make the most of this capability, we identify strong correlation among neighboring input frames, and use this correlation information as a guide to adjusting kernel width parameter. According to the experimental results, the proposed algorithm is found to have potential for improving the performance of support vector machines.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.5B no.1
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• pp.1-5
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• 2005

The vector magnetic property is defined as the relationship between the magnetic field strength vector H and the magnetic flux density vector B. It is very important for the development of high efficiency and the high-density electric machines. The electrical steel sheet for the machine core shows the remarkable vector behavior by the high magnetic flux density level. In this paper, the magnetic characteristic analysis using E&S2 model is introduced as the useful technology for the design and development.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.75-88
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• 2004

The Support Vector Machine(SVM) algorithm has paid attention on maximizing the shortest distance between sample points and discrimination hyperplane. This paper suggests the total margin algorithm which considers the distance between all data points and the separating hyperplane. The method extends existing support vector machine algorithm. In addition, this newly proposed method improves the generalization error bound. Numerical experiments show that the total margin algorithm provides good performance, comparing with the previous methods.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.2
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• pp.165-170
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• 2013

This paper presents the iron loss prediction of rotating electric machines taking account of the vector hysteretic properties of electrical steel sheet. The E&S vector hysteresis model is adopted to describe the vector hysteretic properties of a non-oriented electrical steel sheet, and incorporated into finite element analysis (FEA) for magnetic field analysis and iron loss prediction. A permanent magnet synchronous generator is taken as a numerical model, and the analyzed magnetic field distribution and predicted iron loss by using the proposed method is compared with those from a conventional method which employs an empirical iron loss formula with FEA based on a non-linear B-H curve. Through the comparison the effectiveness of the presented method for the iron loss prediction of the rotating machine is verified.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.261-271
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• 2003

Support Vector Machines (SVMs) provide a powerful performance of the learning system. Generally, SVMs tend to make overfitting. For the purpose of overcoming this difficulty, the definition of soft margin has been introduced. In this case, it causes another difficulty to decide the weight for slack variables reflecting soft margin classifiers. Especially, the error of soft margin algorithm can be bounded by a target margin and some norms of the slack vector. In this paper, we formulate a new soft margin algorithm considering the bound of corruption by noise in data directly. Additionally, through a numerical example, we compare the proposed method with a conventional soft margin algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1889-1893
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• 2008

This paper explores the classification performance of applying to support vector machines (SVMs) for the image classification problems. In this study, we extract the color, texture and shape features of natural images and compare the performance of image classification using each individual feature and integrated features. The experiment results show that classification accuracy on the basis of color feature is better than that based on texture and shape features and the results of the integrating features also provides a better and more robust performance than individual feature. In additions, we show that the proposed classifier of SVM based approach outperforms BPNN to corporate the image classification problems.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.543-549
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• 2008

Inspired by using a robust loss function in the support vector machine regression to control training error and the idea of robust template matching with M-estimator, Chen (2004) applies M-estimator techniques to gaussian radial basis functions and form a new class of robust kernels for the support vector machines. We are specially interested in the shape of the Huber's M-estimator in this context and propose a way to find the shape parameter of the Huber's M-estimating function. For simplicity, only the two-class classification problem is considered.

• Journal of Industrial Convergence
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• v.16 no.4
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• pp.33-46
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• 2018

Personal credit scoring is an effective tool for banks to properly guide decision profitably on granting loans. Recently, many classification algorithms and models are used in personal credit scoring. Personal credit scoring technology is usually divided into statistical method and non-statistical method. Statistical method includes linear regression, discriminate analysis, logistic regression, and decision tree, etc. Non-statistical method includes linear programming, neural network, genetic algorithm and support vector machine, etc. But for the development of the credit scoring model, there is no consistent conclusion to be drawn regarding which method is the best. In this paper, we will compare the performance of the most common scoring techniques such as logistic regression, neural network, and support vector machines using personal credit data of the financial institution in China. Specifically, we build three models respectively, classify the customers and compare analysis results. According to the results, support vector machine has better performance than logistic regression and neural networks.

• Journal of international Conference on Electrical Machines and Systems
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• v.3 no.2
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• pp.116-120
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• 2014

3-phase Induction machines are being used in industry and dq transformation from 3 phase of a, b, c is commonly used to analyze these machines. The equivalent circuits of d and q axis are however generally cross coupled and not simple to analyze. In this study, an analysis method of 3ph induction motor considering current regulator using dq transformation and matrix vector is proposed and it can explain the 3ph induction motor physically. This model does not need the separating process of d and q components. With this technique, the model becomes simple, is easy to understand in physical, and can get the same results with those from the other dq models. These simulation results of the proposed model are compared with those of other models for the conformation of the proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.799-803
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• 2007

In this paper, we propose a new fuzzy membership function for FSVM(Fuzzy Support Vector Machines). We apply a fuzzy membership to each input point of SVM and reformulate SVM into fuzzy SVM (FSVM) such that different input points can make different contributions to the learning of decision surface. The proposed method enhances the SVM in reducing the effect of outliers and noises in data points. This paper compares classification and estimated performance of SVM, FSVM(1), and FSVM(2) model that are getting into the spotlight in time series prediction.