• Journal of Electrical Engineering and Technology
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• v.3 no.4
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• pp.491-498
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• 2008

In this paper a decoupled control of a doubly-fed induction machine(DFIM) feed by a matrix converter is presented. It provides a robust regulation of the stator side active and reactive powers by the direct and quadratic components of the stator current vector, presented in a line-voltage-oriented reference frame. In this case, the stator windings are directly connected to the line grid, while the rotor windings are supplied by this later through a matrix converter controlled by a space vector modulation technique. The proposed solution is suitable for both energy generation and electrical drive applications with restricted speed variation range.

• Journal of applied mathematics & informatics
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• v.10 no.1_2
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• pp.353-360
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• 2002

In this paper we study when a sign pattern matrix A and a sign pattern vector b have the property that the convex hull of the columns of each matrix with sign pattern A contains a vector with sign pattern b. This study generalizes the notion of sign central matrices.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.7
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• pp.662-668
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• 2015

This paper presents an improved predictive control of an induction machine fed by a matrix converter using N-switching vectors as the control action during a complete sampling period of the controller. The conventional model predictive control scheme based matrix converter uses a single switching vector over the same period which introduces high torque ripple. The proposed switching scheme for a matrix converter based model predictive control of an induction machine drive selects the appropriate switching vectors for control of electromagnetic torque with small variations of the stator flux. The proposed method can reduce the ripple of the electrical variables by selecting the switching state as well as the method used in the space vector modulation techniques. Simulation results are presented to verify the effectiveness of the improved predictive control strategy for induction machine fed by a matrix converter.

• Journal of the Korean Statistical Society
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• v.15 no.2
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• pp.97-106
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• 1986

Assume that $X_1, X_2, \cdots, X_N$ are iid p-dimensional normal random vectors ($p \geq 3$) with unknown covariance matrix. The problem of estimating multivariate normal mean vector in an empirical Bayes situation is considered. Empirical Bayes estimators, obtained by Bayes treatmetn of the covariance matrix, are presented. It is shown that the estimators are minimax, each of which domainates teh maximum likelihood estimator (MLE), when the loss is nonsingular quadratic loss. We also derive approximate credibility region for the mean vector that takes advantage of the fact that the MLE is not the best estimator.

• 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.

• Journal of the Optical Society of Korea
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• v.12 no.2
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• pp.103-106
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• 2008

It is shown that the two mode representations, one with vector modes and the other with scalar modes, for the cross spectral density matrices of electromagnetic beams are equivalent to each other. In particular, we suggest a method to find the vector modes from the scalar modes and formulate the cross spectral density matrix as a correlation matrix.

• ETRI Journal
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• v.40 no.4
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• pp.499-510
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• 2018

A vector space based augmented structural kinematic (VSASK) feature descriptor is proposed for human activity recognition. An action descriptor is built by integrating the structural and kinematic properties of the actor using vector space based augmented matrix representation. Using the local or global information separately may not provide sufficient action characteristics. The proposed action descriptor combines both the local (pose) and global (position and velocity) features using augmented matrix schema and thereby increases the robustness of the descriptor. A multiclass support vector machine (SVM) is used to learn each action descriptor for the corresponding activity classification and understanding. The performance of the proposed descriptor is experimentally analyzed using the Weizmann and KTH datasets. The average recognition rate for the Weizmann and KTH datasets is 100% and 99.89%, respectively. The computational time for the proposed descriptor learning is 0.003 seconds, which is an improvement of approximately 1.4% over the existing methods.

• Journal of Astronomy and Space Sciences
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• v.29 no.3
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• pp.287-293
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• 2012

Satellite operating agencies are constantly monitoring conjunctions between satellites and space objects. Two line element (TLE) data, published by the Joint Space Operations Center of the United States Strategic Command, are available as raw data for a preliminary analysis of initial conjunction with a space object without any orbital information. However, there exist several sorts of uncertainties in the TLE data. In this paper, we suggest and analyze a method for estimating the uncertainties in the TLE data through mean, standard deviation of state vector residuals and covariance matrix. Also the estimation results are compared with actual results of orbit determination to validate the estimation method. Characteristics of the state vector residuals depending on the orbital elements are examined by applying the analysis to several satellites in various orbits. Main source of difference between the covariance matrices are also analyzed by comparing the matrices. Particularly, for the Korea Multi-Purpose Satellite-2, we examine the characteristics of the residual variation of state vector and covariance matrix depending on the orbital elements. It is confirmed that a realistic consideration on the space situation of space objects is possible using information from the analysis of mean, standard deviation of the state vector residuals of TLE and covariance matrix.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1138-1145
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• 2013

A novel predictive current control strategy for a sparse matrix converter is presented. The sparse matrix converter is functionally-equivalent to the direct matrix converter but has a reduced number of switches. The predictive current control uses a model of the system to predict the future value of the load current and generates the reference voltage vector that minimizes a given cost function so that space vector modulation is achieved. The results show that the proposed controller for sparse matrix converters controls the load current very effectively and performs very well through simulation and experimental results.

• Journal of Power Electronics
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• v.19 no.4
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• pp.944-955
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• 2019

In this paper, the three-level T-type neutral-point-clamped indirect matrix converter topology and the relative space vector modulation methods are introduced to improve the voltage transfer ratio and output voltage performance. The presented converter topology is based on combinations of cascaded-rectifier and three-level T-type neutral-point-clamp inverter. It can overcome the limitation of voltage transfer ratio of the conventional matrix converter and the high voltage rating of power switches of conventional matrix converter. Two SVPWM strategies for proposed converter are described in this paper to achieve the advantages features such as: sinusoidal input/output currents and three-level output voltage waveforms. Results from Psim 9.0 software simulation are provided to confirm the theoretical analysis. Hence, a laboratory prototype was implemented, and the experimental results are shown to validate the simulation results and to verify the effectiveness of the proposed topology and modulation strategies.