• Journal of the Korean Institute of Telematics and Electronics A
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• v.32A no.9
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• pp.1244-1249
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• 1995

Wavelet transform technique is applied to two important electromagnetic problems:1) to analyze the frequency-domain radar echo from finite-size targets and 2) to the integral solution of two- dimensional electromagnetic scattering problems. Since the frequency- domain radar echo consists of both small-scale natural resonances and large-scale scattering center information, the multiresolution property of the wavelet transform is well suited for analyzing such ulti-scale signals. Wavelet analysis examples of backscattered data from an open- ended waveguide cavity are presented. The different scattering mechanisms are clearly resolved in the wavelet-domain representation. In the wavelet transform domain, the moment method impedance matrix becomes sparse and sparse matrix algorithms can be utilized to solve the resulting matrix equationl. Using the fast wavelet transform in conjunction with the conjugate gradient method, we present the time performance for the solution of a dihedral corner reflector. The total computational time is found to be reduced.

• 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.