• 응용통계연구
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• 제28권2호
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• pp.281-288
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• 2015

본 연구에서는 혼합모형의 추론을 위한 벌점-최대우도추정량의 빠른 계산절차를 제안하다. 제안된 절차는 벌점-최대우도추정량을 위한 추정방정식에서 헷시안 행렬을 화살촉형태를 지닌 희소행렬을 통하여 근사 시킴으로써 계산속도의 향상을 가져왔다. 두 가지 가상실험을 통하여 제안된 근사식을 사용함으로써 얻게되는 계산시간의 감소와 동시에 이를 위하여 지불하여야 하는 근사오차에 대하여 살펴보았다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권6호
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• pp.2806-2825
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• 2018

Unsupervised learning has shown good performance on image, video and audio classification tasks, and much progress has been made so far. It studies how systems can learn to represent particular input patterns in a way that reflects the statistical structure of the overall collection of input patterns. Many promising deep learning systems are commonly trained by the greedy layerwise unsupervised learning manner. The performance of these deep learning architectures benefits from the unsupervised learning ability to disentangling the abstractions and picking out the useful features. However, the existing unsupervised learning algorithms are often difficult to train partly because of the requirement of extensive hyperparameters. The tuning of these hyperparameters is a laborious task that requires expert knowledge, rules of thumb or extensive search. In this paper, we propose a simple and effective unsupervised feature learning algorithm for image classification, which exploits an explicit optimizing way for population and lifetime sparsity. Firstly, a sparse target matrix is built by the competitive rules. Then, the sparse features are optimized by means of minimizing the Euclidean norm ($L_2$) error between the sparse target and the competitive layer outputs. Finally, a classifier is trained using the obtained sparse features. Experimental results show that the proposed method achieves good performance for image classification, and provides discriminative features that generalize well.

• 한국멀티미디어학회논문지
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• 제20권2호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권6호
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• pp.2748-2766
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• 2016

Compared with traditional patch-based sparse representation, recent studies have concluded that group-based sparse representation (GSR) can simultaneously enforce the intrinsic local sparsity and nonlocal self-similarity of images within a unified framework. This article investigates an accelerated split Bregman method (SBM) that is based on GSR which exploits image compressive sensing (CS). The computational efficiency of accelerated SBM for the measurement matrix of a partial Fourier matrix can be further improved by the introduction of a fast Fourier transform (FFT) to derive the enhanced algorithm. In addition, we provide convergence analysis for the proposed method. Experimental results demonstrate that accelerated SBM is potentially faster than some existing image CS reconstruction methods.

• 정보처리학회논문지A
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• 제8A권3호
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• pp.227-234
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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. We compared it with four other preconditioners, which are ILU(0)-wavefront ordering, ILU(0)-Multi-Color ordering, SPAI(SParse Approximate Inverse), and SSOR preconditiner. Experiments were conducted for the Finite Difference discretizations of two problems with various meshsizes varying up to $1025{\times}1024$. CRAY-T3E with 128 nodes was used. MPI library was used for interprocess communications, The results show that Multi-Color Block SOR is scalabl and gives the best performances.

• Journal of Communications and Networks
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• 제15권4호
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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 the Korean Society for Industrial and Applied Mathematics
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• 제5권1호
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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.

• Journal of applied mathematics & informatics
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• 제28권5_6호
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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.

• 한국통신학회논문지
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• 제17권7호
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• pp.739-748
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• 1992

스트라이프 구조를 이용하여 대형 스파스 선형 방정식을 해석하는 CGM 반복 씨스톨릭 알고리즘을 제시하고, 이를 씨스톨릭 어레이로 구현했다. 메트릭스 A를 상삼각 행렬과 대각선행렬, 하삼각 행렬로 나누어서 이들이 별개의 선형 어레이에 의해서 병행적으로 실행되도록 했다. 따라서 1개의 선형 어레이를 사용했을 때보다도 실행시간이 대략 1/2로 단축되며, 스트라이프 구조를 이용하므로서 불규칙하게 분포된 스파스메트릭스의 연산을 효율적으로 할 수 있다.

• 한국정보전자통신기술학회논문지
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• 제10권5호
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• pp.409-416
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• 2017

본 논문은 유한체(finite fields)에서 압축센싱(compressed sensing) 프레임워크를 살펴본다. 하나의 측정 샘플은 센싱행렬의 행과 희소 신호 벡터와의 내적으로 연산되며, 본 논문에서 제안하는 확률적 희소 신호 복원 알고리즘을 이용하여 그 압축센싱의 해를 찾고자 한다. 지금까지 압축센싱은 실수(real-valued)나 복소수(complex-valued) 평면에서 주로 연구되어 왔지만, 이와 같은 원신호를 처리하는 경우 이산화 과정으로 정보의 손실이 뒤따르게 된다. 이에 대한 연구배경은 이산(discrete) 신호에 대한 희소 신호를 복원하고자 하는 노력으로 이어지고 있다. 본 연구에서 제안하는 프레임워크는 센싱행렬로써 코딩 이론에서 사용된 LDPC(Low-Density Parity-Check) 코드의 패러티체크 행렬을 이용한다. 그리고 본 연구에서 제안한 확률적 복원 알고리즘을 이용하여 유한체의 희소 신호를 복원한다. 기존의 코딩 이론에서 발표한 LDPC 복호화와는 달리 본 논문에서는 희소 신호의 확률분포를 이용한 반복적 알고리즘을 제안한다. 그리고 개발된 복원 알고리즘을 통하여 우리는 유한체의 크기가 커질수록 복원 성능이 우수한 결과를 얻었다. 압축센싱의 센싱행렬이 LDPC 패러티체크 행렬과 같은 저밀도 행렬에서도 좋은 성능을 보여줌에 따라 이산 신호를 고려한 응용 분야에서 적극적으로 활용될 것으로 기대된다.