• Journal of Power Electronics
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• v.6 no.1
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• pp.67-72
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• 2006

This paper introduces two different types of AC current control methods for an indirect vector controlled induction motor using a matrix converter. The proposed methods combine the advantages of matrix converters with the advantages of the indirect vector AC current control methods. The first proposed method explains the basic idea of the hysteresis current control method for matrix converters and shows its capability and stability in comparison to the conventional method usually used for VSI. With the aid of the special configuration of the matrix converter, we also propose another current method which is modified from the first one in order to reduce both current ripple and torque ripple. Simulation results have verified the feasibility and the effectiveness of the proposed methods.

• The Korean Journal of Applied Statistics
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• v.17 no.1
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• pp.119-134
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• 2004

Generally to estimate the priority vector in AHP, an eigen-vector method or a log-arithmic least square method is applied to pairwise comparison matrix itself. In this paper an estimating method is suggested, which is applied to pairwise comparison matrix adjusted by using the eigen-decomposition of skew-symmetric matrix. We also show theoretical background, meanings, and several advantages of this method by example. This method may be useful in case that pairwise comparison matrix is quite inconsistent.

• Journal of applied mathematics & informatics
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• v.7 no.2
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• pp.439-451
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• 2000

To compute derivatives using matrix vector multiplication method, new algorithms were introduced in [1.2]n By these algorithms, we reduced roundoff error in computing derivative using Chebyshev collocation methods (CCM). In this paper, some applications of these algorithms ar presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.9 no.3
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• pp.127-131
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• 1984

The IOVMM(Incoherent Optical Vector Matrix Multiplier) is constructed, which can process much information very fast by incogerent light source, and its experimental results are compared with the theoretical values. The input vector and matirx elements are limited to the positive number in this paper. The input vector is made by the LED array and the matrix is encoded on the film by the area modulation method. The result of the vector-matrix multiplication is detected by the photodiode array through the lens system. The analog multiplexer is used for looking at output signal on one channel.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.189-194
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• 1989

The sparse vector method is more efficient than conventional sparse matrix method when solving sparse system. This paper considers the structural relation between factorized L and inverse of L and presents a new ordering algorithm for sparse vector method. The method is useful in enhancing the sparsity of the inverse of L while preserving the aparsity of matrix. The performance of algorithm is compared with conventional algorithms by means of several power system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5860-5876
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• 2018

A single snapshot data can only provide limited amount of information so that the rank of covariance matrix is not full, which is not adopted to complete the parameter estimation directly using the traditional super-resolution method. Aiming at solving the problem, a joint time delay and angle estimation using matrix pencil method based on information reconstruction vector for orthogonal frequency division multiplexing (OFDM) signal is proposed. Firstly, according to the channel frequency response vector of each array element, the algorithm reconstructs the vector data with delay and angle parameter information from both frequency and space dimensions. Then the enhanced data matrix for the extended array element is constructed, and the parameter vector of time delay and angle is estimated by the two-dimensional matrix pencil (2D MP) algorithm. Finally, the joint estimation of two-dimensional parameters is accomplished by the parameter pairing. The algorithm does not need a pseudo-spectral peak search, and the location of the target can be determined only by a single receiver, which can reduce the overhead of the positioning system. The theoretical analysis and simulation results show that the estimation accuracy of the proposed method in a single snapshot and low signal-to-noise ratio environment is much higher than that of Root Multiple Signal Classification algorithm (Root-MUSIC), and this method also achieves the higher estimation performance and efficiency with lower complexity cost compared to the one-dimensional matrix pencil algorithm.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.3
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• pp.91-102
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• 1994

For the analysis of textured image, it requires large storage space and computation time to calculate the matrix features such as SGLDM(Spatial Gray Level Dependence Matrix). NGLDM(Neighboring Gray Level Dependence Matrix). NSGLDM(Neighboring Spatial Gray Level Dependence Matrix) and GLRLM(Gray Level Run Length Matrix). In spite of a large amount of information that each matrix contains, a set of several correlated scalar features calculated from the matrix is not sufficient to approximate it. In this paper, we propose a new classifier for textured images based on these matrices in which the projected vectors of each matrix on the meaningful directions are used as features. In the proposed method, an unknown image is classified to the class of a known image that gives the maximum similarity between the projected model vector from the known image and the vector from the unknown image. In the experiment to classify images of agricultural products, the proposed method shows good performance as much as 85-95% of correct classification ratio.

• Proceedings of the KIPE Conference
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• 2015.07a
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• pp.383-384
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• 2015

This paper presents a new space vector modulation (SVM) method for indirect matrix converters (IMCs) to reduce commonmode voltage as well as minimize output current ripple in a high voltage transfer ratio. In the proposed SVM, the three-vector modulation scheme is used in the rectifier stage, while the nonzero state modulation technique, where the three nearest active vectors are selected to synthesize the desired output voltage, is applied to inverter stage to reduce the CMV. The proposed SVM method can significantly reduce the output current ripple and common-mode voltage of the IMC without any extra hardware. Simulated results are provided to demonstrate the effectiveness of the proposed SVM method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.100-104
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• 2003

The SVDD(support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to consider the problem of modifying the SVDD into the direction of utilizing ellipsoids instead of balls in order to enable better classification performance. After a brief review about the original SVDD method, this paper establishes a new method utilizing ellipsoids in feature space, and presents a solution in the form of SDP(semi-definite programming) which is an optimization problem based on linear matrix inequalities.

• 한국전산유체공학회:학술대회논문집
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• 1998.11a
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• pp.153-159
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• 1998

The Newton-Krylov method on the unstructured grid flow solver using the cell-centered spatial discretization oi compressible Euler equations is presented. This flow solver uses the reconstructed primitive variables to get the higher order solutions. To get the quadratic convergence of Newton method with this solver, the careful linearization of face flux is performed with the reconstructed flow variables. The GMRES method is used to solve large sparse matrix and to improve the performance ILU preconditioner is adopted and vectorized with level scheduling algorithm. To get the quadratic convergence with the higher order schemes and to reduce the memory storage. the matrix-free implementation and Barth's matrix-vector method are implemented and compared with the traditional matrix-vector method. The convergence and computing times are compared with each other.