• Title/Summary/Keyword: sparsity

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F.E.M for Analysis of Magnetic Circuits with thin Magnetic Materials (얇은 자성체를 갖는 자기회로의 자장해석을 위한 유한요소법)

  • Kim, Kwon-Sik;Lee, Joon-Ho;Lee, Ki-Sik;Lee, Bok-Yong
    • Proceedings of the KIEE Conference
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    • 1992.07b
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    • pp.573-576
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    • 1992
  • This paper presents a method, which couples the boundary integral terms in the thin magnetic materials with standard FEM used to analyze the rest of it, for analyzing the magnetic fields. The proposed method retains the sparsity and symmetry of the final system matrix, the merits of standard FEM and eliminates the need for fininte elements in the thin magnetic materials, thereby reducing necessary capacity of computer memory and computing time. To verify the usefulness of the proposed alogorithmn, an examples, coil with source currents and thin magnetic materials, is chosen and analyzed. the results are compared with those of the standard FEM by coarse mesh and the proposed method, using standard FEM by fine mesh as a reference.

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Development of LPAKO : Software of Simplex Method for Liner Programming (단체법 프로그램 LPAKO 개발에 관한 연구)

  • 박순달;김우제;박찬규;임성묵
    • Korean Management Science Review
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    • v.15 no.1
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    • pp.49-62
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    • 1998
  • The purpose of this paper is to develope a large-scale simplex method program LPAKO. Various up-to-date techniques are argued and implemented. In LPAKO, basis matrices are stored in a LU factorized form, and Reid's method is used to update LU maintaining high sparsity and numerical stability, and further Markowitz's ordering is used in factorizing a basis matrix into a sparse LU form. As the data structures of basis matrix, Gustavson's data structure and row-column linked list structure are considered. The various criteria for reinversion are also discussed. The dynamic steepest-edge simplex algorithm is used for selection of an entering variable, and a new variation of the MINOS' perturbation technique is suggested for the resolution of degeneracy. Many preprocessing and scaling techniques are implemented. In addition, a new, effective initial basis construction method are suggested, and the criteria for optimality and infeasibility are suggested respectively. Finally, LPAKO is compared with MINOS by test results.

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Orthogonal matching pursuit performance for support selection length (Support 선택 개수에 따른 orthogonal matching pursuit의 성능 연구)

  • Kwon, Seok-Beop;Lee, Jae-Seok;Shim, Byong-Hyo
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2011.07a
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    • pp.135-136
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    • 2011
  • Sparse한 신호 복원 방법으로 underdetemined system에서 l1-minimization을 이용한 compressive sensing의 연구와 함께, l1-minimization비해 간단한 greed 알고리듬도 활발히 연구되고 있다. 이에 본 논문은 greed 알고리듬의 대표적인 orthogonal matching pursuit기법에서 iteration 마다 support 선택 개수에 따른 성능을 연구한다. 모의 실험을 통해 OMP의 iteration 단계에서 하나의 support만 선택하는 것보다 다수의 support를 선택하는 것이 더 낮은 sparsity의 신호를 복원할 수 있고 더 낮은 계산량의 이득을 가져오는 것을 확인 할 수 있다.

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Hole-Filling Method based on Depth-Incorporated Image Inpainting (깊이도를 고려한 인페인팅기반 홀필링 기법)

  • Choi, Jangwon;Choe, Yoonsik;Kim, Yong-Goo
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2011.07a
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    • pp.517-519
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    • 2011
  • 본 논문에서는 DIBR(Depth image-based rendering)을 통해 생성되는 3D 영상의 홀을 고품질로 채우는 방법을 제안한다. 이를 위해, 생성된 영상의 깊이도를 고려한 희소성(Sparsity) 기반의 인페인팅 알고리즘을 홀 채우기에 적용하였다. 본 논문에서 제안하는 알고리즘은 홀 주변의 전경 텍스쳐를 제외한 배경 텍스쳐 정보만을 이용하기 때문에, 홀 채우기 시 전경 텍스쳐와 배경 텍스쳐가 혼합되는 문제점이 발생하지 않는다. 또한 희소성 기반의 인페인팅을 이용하기 때문에 에지 정보를 활용한 고품질의 홀 채우기가 가능하다. 본 논문에서 제안하는 알고리즘과 기존의 홀 채우기 알고리즘과의 주관적 화질 비교 결과, 본 논문에서 제안하는 알고리즘의 우수성을 확인할 수 있었다.

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상하분해 단체법에서 수정 Forrest-Tomlin 방법의 효율적인 구현

  • 김우제;임성묵;박순달
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 1998.10a
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    • pp.63-66
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    • 1998
  • In the implementation of the simplex method program, the representation and the maintenance of basis matrix is very important, In the experimental study, we investigates Suhl's idea in the LU factorization and LU update of basis matrix. First, the triangularization of basis matrix is implemented and its efficiency is shown. Second, various technique in the dynamic Markowitz's ordering and threshold pivoting are presented. Third, modified Forrest-Tomlin LU update method exploiting sparsity is presented. Fourth, as a storage scheme of LU factors, Gustavson data structure is explained. Fifth, efficient timing of reinversion is developed. Finally, we show that modified Forrest-Tomlin method with Gustavson data structure is superior more than 30% to the Reid method with linked list data structure.

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A Research for Appling Singular Value Decomposition to Collaborative Filtering for Coping With the Sparsity of Rating matrix (협력적 여과에서 평가 행렬의 희소성 문제를 해결하기 위한 Singular Value Decomposition의 적용 방법에 관한 연구)

  • Jeong, Jun;Jeong, Dae-jin;Kim, Yong-Han;Rhee, Phill-Kyu
    • Proceedings of the Korea Inteligent Information System Society Conference
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    • 2000.04a
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    • pp.317-322
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    • 2000
  • 인터넷의 발달로 사용자들은 인터넷에서 필요한 정보를 습득할 수 있을 뿐만 아니라, 생활에 필요한 여러 가지 활동들을 할 수 있게 되었다. 특히 주목받는 부분은 구매 활동이다. 따라서 수많은 기업들이 사람들의 구매 활동에 관련된 전자상거래에 투자하고 있고, 현재 Amazon.com 등과 같은 세계적인 사이트들이 서비스를 실시하고 있다. 또한, 전자상거래 사이트들은 사용자들의 구매 활동을 도와주기 위해 추천 시스템의 도입을 추진하고 있다. 추천 시스템은 사용자들로부터 얻어진 정보를 학습하여 이용 가능한 상품 중에서 고객이 좋아할 만한 것은 추천해 주는 시스템이다. 본 논문에서는 추천 시스템에서 사용되는 주요한 방법인 협력적 여과방법에서 초기 rating 행렬의 희소성 문제를 해결하기 위하여 Singular Value decompositon의 적용 방법을 제안하고 있다.

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A note on SVM estimators in RKHS for the deconvolution problem

  • Lee, Sungho
    • Communications for Statistical Applications and Methods
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    • v.23 no.1
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    • pp.71-83
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    • 2016
  • In this paper we discuss a deconvolution density estimator obtained using the support vector machines (SVM) and Tikhonov's regularization method solving ill-posed problems in reproducing kernel Hilbert space (RKHS). A remarkable property of SVM is that the SVM leads to sparse solutions, but the support vector deconvolution density estimator does not preserve sparsity as well as we expected. Thus, in section 3, we propose another support vector deconvolution estimator (method II) which leads to a very sparse solution. The performance of the deconvolution density estimators based on the support vector method is compared with the classical kernel deconvolution density estimator for important cases of Gaussian and Laplacian measurement error by means of a simulation study. In the case of Gaussian error, the proposed support vector deconvolution estimator shows the same performance as the classical kernel deconvolution density estimator.

Training an Artificial Neural Network for Estimating the Power Flow State

  • Sedaghati, Alireza
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.275-280
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    • 2005
  • The principal context of this research is the approach to an artificial neural network algorithm which solves multivariable nonlinear equation systems by estimating the state of line power flow. First a dynamical neural network with feedback is used to find the minimum value of the objective function at each iteration of the state estimator algorithm. In second step a two-layer neural network structures is derived to implement all of the different matrix-vector products that arise in neural network state estimator analysis. For hardware requirements, as they relate to the total number of internal connections, the architecture developed here preserves in its structure the pronounced sparsity of power networks for which state the estimator analysis is to be carried out. A principal feature of the architecture is that the computing time overheads in solution are independent of the dimensions or structure of the equation system. It is here where the ultrahigh-speed of massively parallel computing in neural networks can offer major practical benefit.

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Using Fuzzy Rating Information for Collaborative Filtering-based Recommender Systems

  • Lee, Soojung
    • International journal of advanced smart convergence
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    • v.9 no.3
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    • pp.42-48
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    • 2020
  • These days people are overwhelmed by information on the Internet thus searching for useful information becomes burdensome, often failing to acquire some in a reasonable time. Recommender systems are indispensable to fulfill such user needs through many practical commercial sites. This study proposes a novel similarity measure for user-based collaborative filtering which is a most popular technique for recommender systems. Compared to existing similarity measures, the main advantages of the suggested measure are that it takes all the ratings given by users into account for computing similarity, thus relieving the inherent data sparsity problem and that it reflects the uncertainty or vagueness of user ratings through fuzzy logic. Performance of the proposed measure is examined by conducting extensive experiments. It is found that it demonstrates superiority over previous relevant measures in terms of major quality metrics.

Optimal Power Flow Study by The Newton's Method (뉴톤법에 의한 최적전력 조류계산)

  • Hwang, Kab-Ju
    • Proceedings of the KIEE Conference
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    • 1989.07a
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    • pp.173-178
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    • 1989
  • Optimal Power Flow (OPF) solution by the Newton's method provides a reliable and robust method to classical OPF problems. The major challenge in algorithm development is to identify the binding inequalities efficiently. This paper propose a simple strategy to identify the binding set. From the mechanism of penalty shifting with soft penalty in trial iteration, a active binding sit is identified automatically. This paper also suggests a technique to solve the linear system whore coefficients are presented by the matrix. This implementation is highly efficient for sparsity programming. Case study for 3,5,14,118,190 bus and practrical KEPCO 305 bus system are performed as well.

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