• Title/Summary/Keyword: 성긴 행렬

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An efficient method for computation of receptances of structural systems with sparse, non-proportional damping matrix (성긴 일반 감쇠행렬을 포함하는 구조물에 대한 효율적인 주파수 응답 계산 방법)

  • Park, Jong-Heuck;Hong, Seong-Wook
    • Journal of the Korean Society for Precision Engineering
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    • v.12 no.7
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    • pp.99-106
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    • 1995
  • Frequency response functions are of great use in dynamic analysis of structural systems. The present paper proposes an efficient method for computation of the frequency rewponse functions of linear structural dynamic models with a sparse, non-proportional damping matrix. An exact condensation procedure is proposed which enables the present method to condense the matrices without resulting in any errors. Also, an iterative scheme is proposed to be able to avoid matrix inversion in computing frequency response matrix. The proposed method is illustrated through a numerical example.

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Sparse Signal Recovery with Parallel Orthogonal Matching Pursuit for Multiple Measurement Vectors (병렬OMP 기법을 통한 복수 측정 벡터기반 성긴 신호의 복원)

  • Park, Jeonghong;Ban, Tae Won;Jung, Bang Chul
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.17 no.10
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    • pp.2252-2258
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    • 2013
  • In this paper, parallel orthogonal matching pursuit (POMP) is proposed to supplement the simultaneous orthogonal matching pursuit (S-OMP) which has been widely used as a greedy algorithm for sparse signal recovery for multiple measurement vector (MMV) problem. The process of POMP is simple but effective: (1) multiple indexes maximally correlated with the observation vector are chosen at the first iteration, (2) the conventional S-OMP process is carried out in parallel for each selected index, (3) the index set which yields the minimum residual is selected for reconstructing the original sparse signal. Empirical simulations show that POMP for MMV outperforms than the conventional S-OMP both in terms of exact recovery ratio (ERR) and mean-squared error (MSE).

Sparse Signal Recovery with Parallel Orthogonal Matching Pursuit and Its Performances (병렬OMP 기법을 통한 성긴신호 복원과 그 성능)

  • Park, Jeonghong;Jung, Bang Chul;Kim, Jong Min;Ban, Tae Won
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.17 no.8
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    • pp.1784-1789
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    • 2013
  • In this paper, parallel orthogonal matching pursuit (POMP) is proposed to supplement the orthogonal matching pursuit (OMP) which has been widely used as a greedy algorithm for sparse signal recovery. The process of POMP is simple but effective: (1) multiple indexes maximally correlated with the observation vector are chosen at the firest iteration, (2) the conventional OMP process is carried out in parallel for each selected index, (3) the index set which yields the minimum residual is selected for reconstructing the original sparse signal. Empirical simulations show that POMP outperforms than the existing sparse signal recovery algorithms in terms of exact recovery ratio (ERR) for sparse pattern and mean-squared error (MSE) between the estimated signal and the original signal.

Performance Comparison of Structured Measurement Matrix for Block-based Compressive Sensing Schemes (구조화된 측정 행렬에 따른 블록 기반 압축 센싱 기법의 성능 비교)

  • Ryu, Joong-seon;Kim, Jin-soo
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.20 no.8
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    • pp.1452-1459
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    • 2016
  • Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing in and under Nyquist rate representation. Generally, the measurement prediction usually works well with a small block while the quality of recovery is known to be better with a large block. In order to overcome this dilemma, conventional research works use a structural measurement matrix with which compressed sensing is done in a small block size but recovery is performed in a large block size. In this way, both prediction and recovery are made to be improved at same time. However, the conventional researches did not compare the performances of the structural measurement matrix, affected by the block size. In this paper, by expanding a structural measurement matrix of conventional works, their performances are compared with different block sizes. Experimental results show that a structural measurement matrix with $4{\times}4$ Hadamard transform matrix provides superior performance in block size 4.

A Compressed Sensing-Based Signal Detection Technique for Generalized Space Shift Keying Systems (일반화된 공간천이변조 시스템에서 압축센싱기술을 이용한 수신신호 복호 알고리즘)

  • Park, Jeonghong;Ban, Tae Won;Jung, Bang Chul
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.18 no.7
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    • pp.1557-1564
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    • 2014
  • In this paper, we propose a signal detection technique based on the parallel orthogonal matching pursuit (POMP) is proposed for generalized shift space keying (GSSK) systems, which is a modified version of the orthogonal matching pursuit (OMP) that is widely used as a greedy algorithm for sparse signal recovery. The signal recovery problem in the GSSK systems is similar to that in the compressed sensing (CS). In the proposed POMP technique, multiple indexes which have the maximum correlation between the received signal and the channel matrix are selected at the first iteration, while a single index is selected in the OMP algorithm. Finally, the index yielding the minimum residual between the received signal and the M recovered signals is selected as an estimate of the original transmitted signal. POMP with Quantization (POMP-Q) is also proposed, which combines the POMP technique with the signal quantization at each iteration. The proposed POMP technique induces the computational complexity M times, compared with the OMP, but the performance of the signal recovery significantly outperform the conventional OMP algorithm.

Documents recommendation using large citation data (거대 인용 자료를 이용한 문서 추천 방법)

  • Chae, Minwoo;Kang, Minsoo;Kim, Yongdai
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.5
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    • pp.999-1011
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    • 2013
  • In this research, we propose a document recommendation method which can find documents that are relatively important to a specific document based on citation information. The key idea is parameter tuning in the Neumann kernal which is an intermediate between a measure of importance (HITS) and of relatedness (co-citation). Our method properly selects the tuning parameter ${\gamma}$ in the Neumann kernal minimizing the prediction error in future citation. We also discuss some comutational issues needed for analysing large citation data. Finally, results of analyzing patents data from the US Patent Office are given.

Performance Comparison of BCS-SPL Techniques Against a Variety of Restoring Block Sizes (복원 블록 크기 변화에 따른 BCS-SPL기법의 이미지 복원 성능 비교)

  • Ryu, Joong-seon;Kim, Jin-soo
    • Journal of Korea Society of Industrial Information Systems
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    • v.21 no.3
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    • pp.21-28
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    • 2016
  • Compressed sensing is a signal processing technique for efficiently acquiring and reconstructing in an under-sampled (i.e., under Nyquist rate) representation. Specially, a block compressed sensing with Smoothed Projected Landweber (BCS-SPL) framework is one of the most widely used schemes. Currently, a variety of BCS-SPL schemes have been actively studied. However, when restoring, block sizes have effects on the reconstructed visual qualities, and in this paper, both a basic scheme of BCS-SPL and several modified schemes of BCS-SPL with structured measurement matrix are analyzed for the effects of the block sizes on the performances of reconstructed image qualities. Through several experiments, it is shown that a basic scheme of BCS-SPL provides superior performance in block size 4.

A review on robust principal component analysis (강건 주성분분석에 대한 요약)

  • Lee, Eunju;Park, Mingyu;Kim, Choongrak
    • The Korean Journal of Applied Statistics
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    • v.35 no.2
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    • pp.327-333
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    • 2022
  • Principal component analysis (PCA) is the most widely used technique in dimension reduction, however, it is very sensitive to outliers. A robust version of PCA, called robust PCA, was suggested by two seminal papers by Candès et al. (2011) and Chandrasekaran et al. (2011). The robust PCA is an essential tool in the artificial intelligence such as background detection, face recognition, ranking, and collaborative filtering. Also, the robust PCA receives a lot of attention in statistics in addition to computer science. In this paper, we introduce recent algorithms for the robust PCA and give some illustrative examples.

A Meshless Method Using the Local Partition of Unity for Modeling of Cohesive Cracks (점성균열 모델을 위한 국부단위분할이 적용된 무요소법)

  • Zi, Goangseup;Jung, Jin-kyu;Kim, Byeong Min
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.26 no.5A
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    • pp.861-872
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    • 2006
  • The element free Galerkin method is extended by the local partition of unity method to model the cohesive cracks in two dimensional continuum. The shape function of a particle whose domain of influence is completely cut by a crack is enriched by the step enrichment function. If the domain of influence contains a crack tip inside, it is enriched by a branch enrichment function which does not have the LEFM stress singularity. The discrete equations are obtained directly from the standard Galerkin method since the enrichment is only for the displacement field, which satisfies the local partition of unity. Because only particles whose domains of influence are influenced by a crack are enriched, the system matrix is still sparse so that the increase of the computational cost is minimized. The condition for crack growth in dynamic problems is obtained from the material instability; when the acoustic tensor loses the positive definiteness, a cohesive crack is inserted to the point so as to change the continuum to a discontiuum. The crack speed is naturally obtained from the criterion. It is found that this method is more accurate and converges faster than the classical meshless methods which are based on the visibility concept. In this paper, several well-known static and dynamic problems were solved to verify the method.