• Journal of applied mathematics & informatics
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• v.28 no.3_4
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• pp.687-696
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• 2010

In this paper, an algorithm for computing the sparse approximate inverse factor of matrix $A^{T}\;A$, where A is an $m\;{\times}\;n$ matrix with $m\;{\geq}\;n$ and rank(A) = n, is proposed. The computation of the inverse factor are done without computing the matrix $A^{T}\;A$. The computed sparse approximate inverse factor is applied as a preconditioner for solving normal equations in conjunction with the CGNR algorithm. Some numerical experiments on test matrices are presented to show the efficiency of the method. A comparison with some available methods is also included.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.5
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• pp.459-467
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• 1992

This paper presents a new algorithm for the countermeasure to alleviate the line overloads due to contingency without shedding loads in a power system. This method for relieving the line overloads by line switching is based on obtaining the kine outage distribution factors-the linear sensitivity factors, which give the amount of change in the power flow of each line due to the removal of a line in a power system. There factors are made up of the elements of sparse bus reactance matrix and brach reactances. In this paper a fast algorithm and program is presented for obtaining only the required bus reactance elements which corresponds to a non-zero elements of bus admittance matrix, and elements of columns which correspond to two terminal buses of the overloaded(monitored) line. The proposed algorithm has been validated in tests on a 6-bus and the 30-bus test system.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.134-140
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• 2011

Recent work in compressed sensing theory shows that $M{\times}N$ independent and identically distributed sensing matrix whose entries are drawn independently from certain probability distributions guarantee exact recovery of a sparse signal with high probability even if $M{\ll}N$. In particular, it is well understood that the $L_1$-minimization algorithm is able to recover sparse signals from incomplete measurements. In this paper, we propose a novel sparse signal reconstruction method that is based on the reweighted $L_1$-minimization via support detection.

• ETRI Journal
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• v.39 no.1
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• pp.1-12
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• 2017

In this paper, we present a new approach for the progressive compression of three-dimensional (3D) mesh geometry using redundant frame dictionaries and sparse approximation techniques. We construct the proposed frames from redundant linear combinations of the eigenvectors of a combinatorial mesh Laplacian matrix. We achieve a sparse synthesis of the mesh geometry by selecting atoms from a frame using matching pursuit. Experimental results show that the resulting rate-distortion performance compares favorably with other progressive mesh compression algorithms in the same category, even when a very simple, sub-optimal encoding strategy is used for the transmitted data. The proposed frames also have the desirable property of being able to be applied directly to a manifold mesh having arbitrary topology and connectivity types; thus, no initial remeshing is required and the original mesh connectivity is preserved.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.1-3
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• 2015

In this paper, we propose a sparse signal reconstruction method referred to as the matching pursuit with a pruning-based tree search (PTS-MP). Two key ingredients of PTS-MP are the pre-selection to put a restriction on columns of the sensing matrix to be investigated and the tree pruning to eliminate unpromising paths from the search tree. In our simulations, we confirm that PTS-MP is effective in recovering sparse signals and outperforms conventional sparse recovery algorithms.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1991.10a
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• pp.23-23
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• 1991

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2002.09a
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• pp.18-18
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• 2002

• Proceedings of the KIEE Conference
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• 1974.01a
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• pp.16-17
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• 1974

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.205-211
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• 2012

The retrieved documents have to be transformed into proper data structure for the clustering algorithms of statistics and machine learning. A popular data structure for document clustering is document-term matrix. This matrix has the occurred frequency value of a term in each document. There is a sparsity problem in this matrix because most frequencies of the matrix are 0 values. This problem affects the clustering performance. The sparseness of document-term matrix decreases the performance of clustering result. So, this research uses the factor score by factor analysis to solve the sparsity problem in document clustering. The document-term matrix is transformed to document-factor score matrix using factor scores in this paper. Also, the document-factor score matrix is used as input data for document clustering. To compare the clustering performances between document-term matrix and document-factor score matrix, this research applies two typed matrices to self organizing map (SOM) clustering.

• Geophysics and Geophysical Exploration
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• v.7 no.4
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• pp.234-244
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• 2004

In order to calculate 3-dimensional wavefield using finite-element method in frequency domain, we must factor so huge sparse impedance matrix. Because of difficulties of handling of this huge impedance matrix, 3-dimensional wave equation modeling is conducted mainly in time domain. In this study, we simulate the 3-D wavefield using finite-element method in Laplace domain by combining high-performance parallel finite-element solver and SWEET (Suppressed Wave Equation Estimation of Traveltime) algorithm which can calculate the traveltime and the amplitude. To verify this combination, we applied it to the SEG/EAGE 3D salt model in serial and parallel computing environments.