• Bulletin of the Korean Mathematical Society
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• v.54 no.2
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• pp.667-677
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• 2017

Let $F{\subseteq}{\mathbb{P}}^3$ be a smooth surface of degree $3{\leq}d{\leq}9$ whose equation can be expressed as either the determinant of a $d{\times}d$ matrix of linear forms, or the pfaffian of a $(2d){\times}(2d)$ matrix of linear forms. In this paper we show that F supports families of dimension p of pairwise non-isomorphic, indecomposable, Ulrich bundles for arbitrary large p.

• Journal of applied mathematics & informatics
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• v.26 no.5_6
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• pp.933-943
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• 2008

Spectral analysis of a strictly stationary r-vector valued time series is considered under the assumption that some of the observations are missed due to some random failure. Statistical properties and asymptotic moments are derived. Asymptotic normality is discussed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.341-344
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• 2001

We propose a novel method to simplify the computational load of ILSP algorithm for CDMA environment. Since this method processes the block matrix by a vector sequentially, the complex matrix computation ran be avoided. The performance of the algorithm is verified by computer simulations.

• The Journal of the Acoustical Society of Korea
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• v.30 no.2
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• pp.80-85
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• 2011

Speaker change detection involves the identification of time indices of an audio stream, where the identity of the speaker changes. In this paper, we propose novel measures for the speaker change detection based on a graph-partitioning criterion over the pairwise distance matrix of feature-vector stream. Experiments on both synthetic and real-world data were performed and showed that the proposed approach yield promising results compared with the conventional statistical measures.

• Information and Communications Magazine
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• v.7 no.3
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• pp.27-34
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• 1990

The $(L,\psi)$ feature description on the binary boundary air craft image is introduced of classifying 3-D object (aircraft) identification. Three types for associative matrix memories are employed and tested for their classification performance. The fast association involved in these memories can be implemented using a parallel optical matrix-vector operation. Two associative memories are based on pseudoinverse solutions and the third one is interoduced as a paralell version of a nearest-neighbor classifier. Detailed simulation results for each associative processor are provided.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.11B no.4
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• pp.164-168
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• 2001

In this paper voltage source FEA for hysteresis motor considering magnetic hysteresis characteristics is presented. The Preisach model is used as a hysteresis model. System matrix whose unknown variables are vector potentials and currents is formulated for voltage source. The stiffness matrix is maintained constant by using M-iteration method. Therefore the calculation time and efforts are reduced with Choleski direct method. Current waveform can be calculated for arbitrary voltage vaveform considering hysteresis effects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2833-2850
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• 2014

This paper proposes a novel framework for traffic video classification based on chaotic features. First, each pixel intensity series in the video is modeled as a time series. Second, the chaos theory is employed to generate chaotic features. Each video is then represented by a feature vector matrix. Third, the mean shift clustering algorithm is used to cluster the feature vectors. Finally, the earth mover's distance (EMD) is employed to obtain a distance matrix by comparing the similarity based on the segmentation results. The distance matrix is transformed into a matching matrix, which is evaluated in the classification task. Experimental results show good traffic video classification performance, with robustness to environmental conditions, such as occlusions and variable lighting.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.795-802
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• 2001

The commutation and control in matrix converters is more complicated as in voltage source converters. Natural freewheeling paths do not exist and the theoretic absent storage elements result in a direct coupled system of load and line currents as well as voltages. The paper offers an overview about staggered commutation and control policies in matrix converters. Based on the knowledge about load current direction and the signs of the line to line input voltages different multi-step commutation policies were derived. This paper examines the application of that policies in the case of space vector modulation and direct control methods with the focus on the resulting effects to the reference output voltage deviation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.5
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• pp.1-11
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• 1994

In computer graphics since objects atre constructed by lines and curves, the high-speed curve generator is indispensible for computer aided design and simulatation. Since the functions of graphic generation can be represented as a series of matrix operations, in this paper, two kind of the high-speed Bezier curve generator that uses matrix equation and a recursive relation for Bezier polynomials are designed. And B-spline curve generator is designed using interdependence of B-spline blending functions. As the result of the comparison of designed curve generator and reference [5], [6] in the operation time and number of operators, the curve generator with 1-dimensional systolic array processor for matrix vector operation that uses matrix equation for Bezier curve is more effective.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.965-974
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• 2003

In this paper we propose an incremental classification of multi-class data set by LS-SVM. By encoding the output variable in the training data set appropriately, we obtain a new specific output vectors for the training data sets. Then, online LS-SVM is applied on each newly encoded output vectors. Proposed method will enable the computation cost to be reduced and the training to be performed incrementally. With the incremental formulation of an inverse matrix, the current information and new input data are used for building another new inverse matrix for the estimation of the optimal bias and lagrange multipliers. Computational difficulties of large scale matrix inversion can be avoided. Performance of proposed method are shown via numerical studies and compared with artificial neural network.