• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.7
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• pp.739-748
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• 1992

A CGM iterative systolic algorithm to solve large sparse linear systems of equations is presented. For implementation of the algorithm, a systolic array using the stripe structure is proposed. The matrix A is decomposed into a strictly lower triangular matrix, a diagonal matrix, and a strictly up-per triangular matrix, and the two formers and the tatter· are concurrently computed by different linear arrays. Hence, the execution time of this approach Is reduced to half of the execution time of the that a linear array is used. computation of the Irregularly distributed sparse matrix can be executed effectively by using the stripe structure.

• Journal of Communications and Networks
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• v.15 no.4
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• pp.370-382
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• 2013

Distributed massive MIMO systems, which have high bandwidth efficiency and can accommodate a tremendous amount of traffic using algorithms such as zero-forcing beam forming (ZFBF), may be deployed in large public venues with the antennas mounted under-floor. In this case the channel gain matrix H can be modeled as a multi-banded matrix, in which off-diagonal entries decay both exponentially due to heavy human penetration loss and polynomially due to free space propagation loss. To enable practical implementation of such systems, we present a multi-banded matrix inversion algorithm that substantially reduces the complexity of ZFBF by keeping the most significant entries in H and the precoding matrix W. We introduce a parameter p to control the sparsity of H and W and thus achieve the tradeoff between the computational complexity and the system throughput. The proposed algorithm includes dense and sparse precoding versions, providing quadratic and linear complexity, respectively, relative to the number of antennas. We present analysis and numerical evaluations to show that the signal-to-interference ratio (SIR) increases linearly with p in dense precoding. In sparse precoding, we demonstrate the necessity of using directional antennas by both analysis and simulations. When the directional antenna gain increases, the resulting SIR increment in sparse precoding increases linearly with p, while the SIR of dense precoding is much less sensitive to changes in p.

• Journal of applied mathematics & informatics
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• v.28 no.5_6
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• pp.1131-1141
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• 2010

We develop an algorithm for computing a sparse approximate inverse for a nonsymmetric positive definite matrix based upon the FFAPINV algorithm. The sparse approximate inverse is computed in the factored form and used to work with some Krylov subspace methods. The preconditioner is breakdown free and, when used in conjunction with Krylov-subspace-based iterative solvers such as the GMRES algorithm, results in reliable solvers. Some numerical experiments are given to show the efficiency of the preconditioner.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.363-370
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• 2017

Data assimilation is an initializing method for air quality forecasting such as PM10. It is very important to enhance the forecasting accuracy. Optimal interpolation is one of the data assimilation techniques. It is very effective and widely used in air quality forecasting fields. The technique, however, requires too much memory space and long execution time. It makes the PM10 air quality forecasting difficult in real time. We propose a fast optimal interpolation data assimilation method for PM10 air quality forecasting using a new kernel tridiagonal sparse matrix and CUDA massively parallel processing architecture. Experimental results show the proposed method is 5~56 times faster than conventional ones.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.5 no.1
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• pp.85-100
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• 2001

In this paper we propose a block-type parallel preconditioner for solving large sparse nonsymmetric linear systems, which we expect to be scalable. It is Multi-Color Block SOR preconditioner, combined with direct sparse matrix solver. For the Laplacian matrix the SOR method is known to have a nondeteriorating rate of convergence when used with Multi-Color ordering. Since most of the time is spent on the diagonal inversion, which is done on each processor, we expect it to be a good scalable preconditioner. Finally, due to the blocking effect, it will be effective for ill-conditioned problems. We compared it with four other preconditioners, which are ILU(0)-wavefront ordering, ILU(0)-Multi-Color ordering, SPAI(SParse Approximate Inverse), and SSOR preconditioner. Experiments were conducted for the Finite Difference discretizations of two problems with various meshsizes varying up to 1024 x 1024, and for an ill-conditioned matrix from the shell problem from the Harwell-Boeing collection. CRAY-T3E with 128 nodes was used. MPI library was used for interprocess communications. The results show that Multi-Color Block SOR and ILU(0) with Multi-Color ordering give the best performances for the finite difference matrices and for the shell problem only the Multi-Color Block SOR converges.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.5
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• pp.409-416
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• 2017

In this paper, we consider a framework of compressed sensing over finite fields. One measurement sample is obtained by an inner product of a row of a sensing matrix and a sparse signal vector. A recovery algorithm proposed in this study for sparse signals based probabilistic decoding is used to find a solution of compressed sensing. Until now compressed sensing theory has dealt with real-valued or complex-valued systems, but for the processing of the original real or complex signals, the loss of the information occurs from the discretization. The motivation of this work can be found in efforts to solve inverse problems for discrete signals. The framework proposed in this paper uses a parity-check matrix of low-density parity-check (LDPC) codes developed in coding theory as a sensing matrix. We develop a stochastic algorithm to reconstruct sparse signals over finite field. Unlike LDPC decoding, which is published in existing coding theory, we design an iterative algorithm using probability distribution of sparse signals. Through the proposed recovery algorithm, we achieve better reconstruction performance as the size of finite fields increases. Since the sensing matrix of compressed sensing shows good performance even in the low density matrix such as the parity-check matrix, it is expected to be actively used in applications considering discrete signals.

• Journal of applied mathematics & informatics
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• v.8 no.2
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• pp.305-326
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• 2001

We introduce a class of multilevel recursive incomplete LU preconditioning techniques (RILUM) for solving general sparse matrices. This techniques is based on a recursive two by two block incomplete LU factorization on the coefficient martix. The coarse level system is constructed as an (approximate) Schur complement. A dynamic preconditioner is obtained by solving the Schur complement matrix approximately. The novelty of the proposed techniques is to solve the Schur complement matrix by a preconditioned Krylov subspace method. Such a reduction process is repeated to yield a multilevel recursive preconditioner.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1359-1371
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• 2020

In transmitting and receiving such a large amount of data, reliable data communication is crucial for normal operation of a device and to prevent abnormal operations caused by errors. Therefore, in this paper, it is assumed that an error correction code (ECC) that can detect and correct errors by itself is used in an environment where massive data is sequentially received. Because an embedded system has limited resources, such as a low-performance processor or a small memory, it requires efficient operation of applications. In this paper, we propose using an accelerated ECC-decoding technique with a graphics processing unit (GPU) built into the embedded system when receiving a large amount of data. In the matrix-vector multiplication that forms the Hamming code used as a function of the ECC operation, the matrix is expressed in compressed sparse row (CSR) format, and a sparse matrix-vector product is used. The multiplication operation is performed in the kernel of the GPU, and we also accelerate the Hamming code computation so that the ECC operation can be performed in parallel. The proposed technique is implemented with CUDA on a GPU-embedded target board, NVIDIA Jetson TX2, and compared with execution time of the CPU.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1163-1185
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• 2024

With the sharp increase in the volume of literature data, researchers must spend considerable time and energy locating desired papers. A paper recommendation is the means necessary to solve this problem. Unfortunately, the large amount of data combined with sparsity makes personalizing papers challenging. Traditional matrix decomposition models have cold-start issues. Most overlook the importance of information and fail to consider the introduction of noise when using side information, resulting in unsatisfactory recommendations. This study proposes a paper recommendation method (PR-SLSMF) using document-level representation learning with citation-informed transformers (SPECTER) and low-rank and sparse matrix factorization; it uses SPECTER to learn paper content representation. The model calculates the similarity between papers and constructs a weighted heterogeneous information network (HIN), including citation and content similarity information. This method combines the LSMF method with HIN, effectively alleviating data sparsity and cold-start issues and avoiding topic drift. We validated the effectiveness of this method on two real datasets and the necessity of adding side information.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.3
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• pp.329-334
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• 1996

Power system is becoming more and more complex and large Existing procedural programming technique can't cope with software flexibility and maintenance problems. So, Object-Oriented Programming (OOP) is increasingly used to solve these problems. OOP in power system analysis field has been greatly developed. This paper applies OOP in power flow analysis, and presents new algorithm which uses only a Jacobian to solve mismatch equations, and introduces a new sparse matrix storage method which is different from existing method. (author). 11 refs., 12 figs., 3 tabs.