• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.14-20
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• 2019

Currently, microarray gene expression data take advantage of the sufficient classification of cancers, which addresses the problems relating to cancer causes and treatment regimens. However, the sample size of gene expression data is often restricted, because the price of microarray technology on studies in humans is high. We propose enhancing the gene expression classification of support vector machines with generative adversarial networks (GAN-SVMs). A GAN that generates new data from original training datasets was implemented. The GAN was used in conjunction with nonlinear SVMs that efficiently classify gene expression data. Numerical test results on 20 low-sample-size and very high-dimensional microarray gene expression datasets from the Kent Ridge Biomedical and Array Expression repositories indicate that the model is more accurate than state-of-the-art classifying models.

• International Journal of Advanced Culture Technology
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• v.10 no.1
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• pp.42-46
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• 2022

To cultivate talented people with creative and convergent thinking skills to live in the era of the 4th industrial revolution, the national curriculum of Korea is gradually emphasizing convergence education and software education. To meet the demands of the times, this paper suggests subject-convergent teaching-learning materials for educating core principles of Support Vector Machines, especially targeting elementary learners. Based on analysis of the national curriculum, achievement standards of three subjects are integrated. After printable worksheets for traditional face-to-face classes had developed, they were transformed to online interactive worksheets for non-face-to-face classes. The teaching-learning materials are expected to promote the growth of the learners' academic motivation and knowledge.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.193-202
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• 2022

Semi-supervised learning has gained significant attention in recent applications. In this article, we provide a selective overview of popular semi-supervised methods and then propose a simple but effective algorithm for semi-supervised classification using support vector machines (SVM), one of the most popular binary classifiers in a machine learning community. The idea is simple as follows. First, we apply the dimension reduction to the unlabeled observations and cluster them to assign labels on the reduced space. SVM is then employed to the combined set of labeled and unlabeled observations to construct a classification rule. The use of SVM enables us to extend it to the nonlinear counterpart via kernel trick. Our numerical experiments under various scenarios demonstrate that the proposed method is promising in semi-supervised classification.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1446-1455
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• 2000

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.59-64
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• 2010

To improve system performance, we apply the concept of membership function to Variance Considered Machines (VCMs) which is a modified algorithm of Support Vector Machines (SVMs) proposed in our previous studies. Many classification algorithms separate nonlinear data well. However, existing algorithms have ignored the fact that probabilities of error are very high in the data-mixed area. Therefore, we make our algorithm ignore data which has high error probabilities and consider data importantly which has low error probabilities to generate system output according to the probabilities of error. To get membership function, we calculate sigmoid function from the dataset by considering means and variances. After computation, this membership function is applied to the VCMs.

• Journal of international Conference on Electrical Machines and Systems
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• v.1 no.2
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• pp.23-29
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• 2012

The control of stator flux, torque angle, excitation torque, reluctance torque and total torque of the direct torque control (DTC) for a permanent magnet synchronous motor (PMSM) are studied in this paper. Simplified expressions to represent the changes of these variables due to the application of a voltage vector are given. Finally, a voltage vector selection area and the implementation of a voltage vector selection strategy are proposed.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.143-151
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• 2004

High-speed permanent magnet machines are likely to be a key technology for electric drives and motion control systems for many applications, since they are conductive to high efficiency, high power density, small size and low weight. In high-speed machines, the permanent magnets are often contained within a retaining sleeve. However, the sleeve and the magnets are exposed to high order flux harmonics, which cause parasitic eddy current losses. Rotor losses of high-speed machines are of great importance especially in high-speed applications, because losses heat the rotor, which is often very compact construction and thereby difficult to cool. This causes a danger of demagnetization of the NdFeB permanent magnets. Therefore, special attention should be paid to the prediction of the rotor losses. This paper is concerned with the rotor losses in permanent magnet high-speed machines that are caused by permeance variation due to stator slotting. First, the flux harmonics are determined by double Fourier analysis of the normal flux density data over the rotor surface. And then, the rectilinear model was used to calculate rotor losses in permanent magnet machines. Finally, Poynting vector have been used to investigate the rotor eddy current losses of high-speed Permanent magnet machine.

• Journal of KIISE:Software and Applications
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• v.32 no.12
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• pp.1260-1270
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• 2005

Support Vector Machines (SVM) is well known for a representative learner as one of the kernel methods. SVM which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, SVM is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with a binary SVM. One-Per-Class (OPC) and All-Pairs have been applied to solve the face recognition problem, which is one of the multi-class problems, with SVM. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. In this paper, we introduce the output coding methods as an approach for extending binary SVM to multi-class SVM and propose new output coding schemes based on the Error-Correcting Output Codes (ECOC) which is a dominant theoretical foundation of the output coding methods. From the experiment on the face recognition, we give empirical results on the properties of output coding methods including our proposed ones.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.195-200
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• 2004

In this paper we propose effective speech recognizer through two recognition experiments. In general, SVM is classification method which classify two class set by finding voluntary nonlinear boundary in vector space and possesses high classification performance under few training data number. In this paper we compare recognition performance of HMM and SVM at training data number and investigate recognition performance of each feature parameter while changing feature space of MFCC using Independent Component Analysis(ICA) and Principal Component Analysis(PCA). As a result of experiment, recognition performance of SVM is better than 1:.um under few training data number, and feature parameter by ICA showed the highest recognition performance because of superior linear classification.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.981-984
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• 2010

In this paper, we used Support Vector Machines(SVMs) as the learning method, one of Artificial Neural Network, to segregated from the continuous speech into phonemes, an initial, medial, and final sound, and then, performed continuous speech recognition from it. A Decision boundary of phoneme is determined by algorithm with maximum frequency in a short interval. Speech recognition process is performed by Continuous Hidden Markov Model(CHMM), and we compared it with another phoneme segregated from the eye-measurement. From the simulation results, we confirmed that the method, SVMs, we proposed is more effective in an initial sound than Gaussian Mixture Models(GMMs).