• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.440-446
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• 2007

This paper addresses a new representation of DFT matrix via the Jacket transform based on the element inverse processing. We simply represent the inverse of the DFT matrix following on the factorization way of the Jacket transform, and the results show that the inverse of DFT matrix is only simply related to its sparse matrix and the permutations. The decomposed DFT matrix via Jacket matrix has a strong geometric structure that exhibits a block modulating property. This means that the DFT matrix decomposed via the Jacket matrix can be interpreted as a block modulating process.

• Proceedings of the KIPE Conference
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• 2011.07a
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• pp.419-420
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• 2011

본 논문은 스파스 매트릭스 컨버터(Sparse matrix converter)의 단일 스위치 또는 두 개의 스위치의 개방 사고에 대한 진단 방법을 제안한다. 스파스 매트릭스 컨버터는 단방향 전력용 스위치의 개수를 줄이면서 기존의 매트릭스 컨버터와 동일한 성능을 갖는 새로운 토폴로지이다. 제안된 기법은 입력과 출력의 전류를 이용하여 만든 패턴을 비교하여 고장 진단뿐 아니라 고장 난 스위치의 위치까지 식별할 수 있다. 시뮬레이션 결과를 통해 제안한 기법의 타당성을 검증한다.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.485-486
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• 2018

In this paper, we consider a binary recovery framework of the Compressed Sensing (CS) problem. We derive an upper bound for $L_0$ recovery performance of a binary sparse signal in terms of the dimension N and sparsity K of signals, the number of measurements M. We show that the upper bound obtained from this work goes to the limit bound when the sensing matrix sufficiently become dense. In addition, for perfect recovery performance, if the signals are very sparse, the sensing matrices required for $L_0$ recovery are little more dense.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.1
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• pp.52-58
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• 2000

Signal propagation in nonlinear optical fiber is analyzed numerically by using SS-FEM (Split-Step Finite Element Method). By adopting cubic element function in FEM, soliton equation of which exact solution was well known, has been solved. Also, accuracy of numerical results and computing times are compared with those of Fourier method, and we have found that solution obtained from using FEM was very relatively accurate. Especially, to reduce CPU time in matrix computation in each step, the matrix imposed by the boundary condition is approximated as a sparse matrix. As a result, computation time was shortened even with the same or better accuracy when compared to those of the conventional FEM and Fourier method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.17 no.4
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• pp.53-62
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• 2021

Deep learning models such as convolutional neural networks and recurrent neual networks process a huge amounts of data, so they require a lot of storage and consume a lot of time and power due to memory access. Recently, research is being conducted to reduce memory usage and access by compressing data using the feature that many of deep learning data are highly sparse and localized. In this paper, we propose a compression-decompression method of storing only the non-zero data and the location information of the non-zero data excluding zero data. In order to make the location information of non-zero data, the matrix data is divided into sections uniformly. And whether there is non-zero data in the corresponding section is indicated. In this case, section division is not executed only once, but repeatedly executed, and location information is stored in each step. Therefore, it can be properly compressed according to the ratio and distribution of zero data. In addition, we propose a hardware structure that enables compression and decompression without complex operations. It was designed and verified with Verilog, and it was confirmed that it can be used in hardware deep learning accelerators.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.1-9
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• 2021

OpenGL compute shader is a shader stage that operate differently from other shader stage and it can be used for the calculating purpose of any data in parallel. This paper proposes a GPU-based parallel algorithm for computing sparse linear systems through conjugate gradient using an iterative method, which perform calculation on OpenGL compute shader. Basically, this sparse linear solver is used to solve large linear systems such as symmetric positive definite matrix. Four well-known matrix formats (Dense, COO, ELL and CSR) have been used for matrix storage. The performance comparison from our experimental tests using eight sparse matrices shows that GPU-based linear solving system much faster than CPU-based linear solving system with the best average computing time 0.64ms in GPU-based and 15.37ms in CPU-based.

• KIISE Transactions on Computing Practices
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• v.20 no.11
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• pp.610-614
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• 2014

Like many types of scientific data, results from simulations of volcanic ash diffusion are of a clustered sparse matrix in the netCDF format. Since these data sets are large in size, they generate high storage and transmission costs. In this paper, we suggest a new method that reduces the size of the data of volcanic ash diffusion simulations by converting the multi-dimensional index to a single dimension and keeping only the starting point and length of the consecutive zeros. This method presents performance that is almost as good as that of ZIP format compression, but does not destroy the netCDF structure. The suggested method is expected to allow for storage space to be efficiently used by reducing both the data size and the network transmission time.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.12
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• pp.739-745
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• 2014

It has been shown in compressive sensing that every s-sparse $x{\in}R^N$ can be recovered from the measurement vector y=Ax or the noisy vector y=Ax+e via ${\ell}_1$-minimization as soon as the 3s-restricted isometry constant of the sensing matrix A is smaller than 1/2 or smaller than $1/\sqrt{3}$ by applying the Iterative Hard Thresholding (IHT) algorithm. However, recovery can be guaranteed by practical algorithms for some certain assumptions of acquisition schemes. One of the key assumption is that the sensing matrix must satisfy the Restricted Isometry Property (RIP), which is often violated in the setting of many practical applications. In this paper, we studied a generalization of RIP, called Restricted Biorthogonality Property (RBOP) for anisotropic cases, and the new recovery algorithms called oblique pursuits. Then, we provide an analysis on the success of sparse recovery in terms of restricted biorthogonality constant for the IHT algorithms.

• 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.