• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.60-65
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• 2016

This paper introduces the non-local means (NLM) algorithm for image denoising, and also introduces an improved algorithm which is based on the principal component analysis (PCA). To do the PCA, a covariance matrix of a given image should be evaluated first. If we let the size of neighborhood patches of the NLM S × S², and let the number of pixels Q, a matrix multiplication of the size S² × Q is required to compute a covariance matrix. According to the characteristic of images, such computation is inefficient. Therefore, this paper proposes an efficient method to compute the covariance matrix by sampling the pixels. After sampling, the covariance matrix can be computed with matrices of the size S² × floor (Width/l) × (Height/l).

• Proceedings of the Korean Vacuum Society Conference
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• 1992.02a
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• pp.17-18
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• 1992

SIMS is an indispensable surface analysis instrument in trace element depth p profiling because of high detection sensitivity and excellent depth r resolution, however, it requires standard sample to do quantitative analysis d due to matrix effect depending on the species of impurities and sample m matricies and on the sputtering rates. A Among the SNMS technology developed to supply the deficiency, we researched i into CsX+ SNMS which improved the resul t quanti tati vely wi thout any extra epuipments. So basic SNMS functions were confirmed through matrix element composition rate a analysis using Si02 layer etc. and adaptability to trace element c concentration analysis was tried. For that purpose we compared SIMS depth profile data for Boron which presented s strong matrix effect on account of Fluorin existence after BF2 ion implantation on silicon substrate with SNMS data. d dynamic range were investigated. A After these experements we concluded that CsX+ SNMS reduced matrix effect and we could apply it to profile impurity elements.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.400-402
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• 2006

Matrix multiplication is an important problem in linear algebra. its main significance for combinatorial algorithms is its equivalence to a variety of other problems, such as transitive closure and reduction, solving linear systems, and matrix inversion. Thus the development of high-performance matrix multiplication implies faster algorithms for all of these problems. In this paper. we present a quantitative comparison of the theoretical and empirical performance of key matrix multiplication algorithms and use our analysis to develop a faster algorithm. We propose a Hybrid approach on Winograd's and Strassen's algorithms that improves the performance and discuss the performance of the hybrid Winograd-Strassen algorithm. Since Strassen's algorithm is based on a $2{\times}2$ matrix multiplication it makes the implementation very slow for larger matrix because of its recursive nature. Though we cannot get the theoretical threshold value of Strassen's algorithm, so we determine the threshold to optimize the use of Strassen's algorithm in nodes through various experiments and provided a summary shown in a table and graphs.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.55-64
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• 2000

We propose a criterion for testing homogeneity of matrix intraclass covariance matrices of K multivariate normal populations, It is based on a variable transformation intended to propose and develop a likelihood ratio criterion that makes use of properties of eigen structures of the matrix intraclass covariance matrices. The criterion then leads to a simple test that uses an asymptotic distribution obtained from Box's (1949) theorem for the general asymptotic expansion of random variables.

• Communications for Statistical Applications and Methods
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• v.12 no.3
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• pp.653-657
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• 2005

Eigenstructure of a spatial design matrix of Matheron's variogram estimator in $R^1$ is derived. It is shown that the spatial design matrix in $R^1$ with n/2$\le$h < n has a nice spectral decomposition. The mean, variance, and covariance of this estimator are obtained using the eigenvalues of a spatial design matrix. We also found that the lower bound and the upper bound of the normalized Matheron's variogram estimator.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.6
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• pp.753-759
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• 1999

BPF(block pulse function) has been used widely in the system analysis and controller design. The integral operational matrix of BPF converts the system represented in the form of the differential equation into the algebraic problem. Therefore, it is important to reduce the error caused by the integral operational matrix. In this paper, a new integral operational matrix is derived from the approximating function using Lagrange's interpolation formula. Comparing the proposed integral operational matrix with another, the result by proposed matrix is closer to the real value than that by the conventional matrix. The usefulness of th proposed method is also verified by numerical examples.

• Journal of the Korean Mathematical Society
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• v.54 no.6
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• pp.1667-1682
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• 2017

In this paper, the Hermitian positive definite solutions of the matrix equation $X^s+A^*X-^tA=Q$, where Q is an $n{\times}n$ Hermitian positive definite matrix, A is an $n{\times}n$ nonsingular complex matrix and $s,t{\in}[1,{\infty})$ are discussed. We find a matrix interval which contains all the Hermitian positive definite solutions of this equation. Also, a necessary and sufficient condition for the existence of these solutions is presented. Iterative methods for obtaining the maximal and minimal Hermitian positive definite solutions are proposed. The theoretical results are illustrated by numerical examples.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.5 no.1
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• pp.44-54
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• 1993

This is an experimental study to identify the performance of air-to-air rotary heat exchanger with polyurethane foam matrix. The experimental apparatus including heating AHU(Air Handling Unit), cooling AHU, sensor chamber, and heat exchanger testing unit was designed and manufactured in this study. The performance of heat exchanger with porous polyurethane foam matrix was tested with variations of the density and the thickness of matrix, regulating the wind velocity and the rotational speed of matrix. The actual heat recovery effectiveness, air leakage rate, and pressure drop of heat exchanger were measured and analyzed.

• Journal of the Korean Institute of Telematics and Electronics
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• v.16 no.5
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• pp.18-26
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• 1979

This paper extends some well-known system theories on algebraic matrix Lyapunov and Riccati equations. These extended results contain two point boundary conditions in matrix differential equations and include conventional results as special cases. Necessary and sufficient conditions are derived under which linear systems are stabilizable with feedback gains derived from periodic two-point boundary matrix differential equations. An iterative computation method for two-point boundary differential Riccati equations is given with an initial guess method. The results in this paper are related to periodic feedback controls and also to the quadratic cost problem with a discrete state penalty.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.3 no.1
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• pp.43-53
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• 1999

A real $m{\times}n$ matrix A is called an L-matrix if every matrix in its qualitative class has linearly independent rows. Since the number of the sign pattern matrices of the given size is finite, we can list all patterns lexicographically. In [2], a necessary and sufficient condition for a matrix to be an L-matrix was given. We presented an algorithm which decides whether the given matrix is an L-matrix or not. In this paper, we develope an algorithm and C-program which will determine whether a given matrix is an L-matrix or not, or an SNS-matrix or not. In addition, we have extended our algorithm to be able to classify sign-pattern matrices, and to find barely L-matrices from a given matrix and to list all $m{\times}n$ L-matrices.