• Title/Summary/Keyword: Eigen-decomposition

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An Estimating Method for Priority Vector in AHP, Using the Eigen-Decomposition of a Skew-Symmetric Matrix (AHP에서 왜대칭행렬의 고유분해를 이용한 중요도 추정법의 제안)

  • 이광진
    • The Korean Journal of Applied Statistics
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    • v.17 no.1
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    • pp.119-134
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    • 2004
  • Generally to estimate the priority vector in AHP, an eigen-vector method or a log-arithmic least square method is applied to pairwise comparison matrix itself. In this paper an estimating method is suggested, which is applied to pairwise comparison matrix adjusted by using the eigen-decomposition of skew-symmetric matrix. We also show theoretical background, meanings, and several advantages of this method by example. This method may be useful in case that pairwise comparison matrix is quite inconsistent.

Reduction in Computational Complexity of KLT-CVQ using UTV Decomposition (UTV 분해를 이용한 KLT-CVQ 코더의 계산량 개선)

  • Ju, Hyunho;Kim, Moo Young
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 2012.07a
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    • pp.176-177
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    • 2012
  • 사람의 음성을 압축하는 방법으로 Code Excited Linear Prediction (CELP) 코더가 주로 사용되어 왔다. CELP 코더의 수신단에서는 양자화 된 여기신호를 LPC 필터로 합성하여 신호를 복원한다. LPC 합성필터의 영향으로 양자화 된 여기신호의 보로노이 셀 모양이 변형되는 문제점이 있기 때문에 이런 문제점을 해결하기 위해서 Karhunen-Loeve-Transform based Classify vector Quantization (KLT-CVQ) 코더가 제안되었다. 기존 KLT-CVQ 코더는 KLT 변환과 class 선택을 위해서 Eigen Value Decomposition (EVD)을 이용해서 eigen vector와 eigen value를 계산한다. 본 논문에서는 EVD 대신에 UTV Decomposition (UTVD)을 이용하여 KLT-CVQ의 계산량 문제점을 개선하는 방법을 제안한다.

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ANALYSIS OF EIGEN VALUES FOR EFFECTIVE CHOICE OF SNAPSHOT DATA IN PROPER ORTHOGONAL DECOMPOSITION (적합직교분해 기법에서의 효율적인 스냅샷 선정을 위한 고유값 분석)

  • Kang, H.M.;Jun, S.O.;Yee, K.
    • Journal of computational fluids engineering
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    • v.22 no.1
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    • pp.59-66
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    • 2017
  • The guideline of selecting the number of snapshot dataset, $N_s$ in proper orthogonal decomposition(POD) was presented via the analysis of Eigen values based on the singular value decomposition(SVD). In POD, snapshot datasets from the solutions of Euler or Navier-Stokes equations are utilized to SVD and a reduced order model(ROM) is constructed as the combination of Eigen vectors. The ROM is subsequently applied to reconstruct the flowfield data with new set of flow conditions, thereby enhancing the computational efficiency. The overall computational efficiency and accuracy of POD is dependent on the number of snapshot dataset; however, there is no reliable guideline of determining $N_s$. In order to resolve this problem, the order of maximum to minimum Eigen value ratio, O(R) from SVD was analyzed and presented for the decision of $N_s$; in case of steady flow, $N_s$ should be determined to make O(R) be $10^9$. For unsteady flow, $N_s$ should be increased to make O(R) be $10^{11\sim12}$. This strategy of selecting the snapshot dataset was applied to two dimensional NACA0012 airfoil and vortex flow problems including steady and unsteady cases and the numerical accuracies according to $N_s$ and O(R) were discussed.

A Study On The Eigen-properties of A 2-D Square Waveguide by the Krylov-Schur Iteration Method (Krylov-Schur 순환법에 의한 2차원 사각도파관에서의 고유치 문제에 관한 연구)

  • Kim, Yeong Min;Kim, Dongchool;Lim, Jong Soo
    • Journal of the Institute of Electronics and Information Engineers
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    • v.50 no.11
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    • pp.28-35
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    • 2013
  • The Krylov-Schur algorithm has been applied to reveal the eigen-properties of the wave guide having the square cross section. The eigen-matrix equation has been constructed from FEM with the basis function of the tangential edge-vectors of the triangular element. This equation has been treated firstly with Arnoldi decomposition to obtain a upper Hessenberg matrix. The QR algorithm has been carried out to transform it into Schur form. The several eigen values satisfying the convergent condition have appeared in the diagonal components. The eigen-modes for them have been calculated from the inverse iteration method. The wanted eigen-pairs have been reordered in the leading principle sub-matrix of the Schur matrix. This sub-matrix has been deflated from the eigen-matrix equation for the subsequent search of other eigen-pairs. These processes have been conducted several times repeatedly. As a result, a few primary eigen-pairs of TE and TM modes have been obtained with sufficient reliability.

A Study on The eigen-properties on Varied Structural 2-Dim. Waveguides by Krylov-Schur Iteration Method (Krylov-Schur 순환법을 이용한 다양한 2차원 구조의 도파관들에 관한 연구)

  • Kim, Yeong Min;Lim, Jong Soo
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.2
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    • pp.10-14
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    • 2014
  • Krylov-Schur iteration method has been applied to the 2-Dim. waveguides of the varied geometrical structure. The eigen-equations for them have been constructed from FEM based on the tangential edge vectors of triangular elements. The eigen-values and their modes have been determined from the diagonal components of the Schur matrices and its transforming matrices. The eigen-pairs as the results have been revealed visually in the schematic representations.

Reduced Rank Eigen-Space Beamforming for Adaptive Array Systems (적응형 배열 안테나를 위한 감소 차수 고유 공간 빔형성 알고리즘)

  • Hyeon, Seung-Heon;Choi, Seung-Won
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.33 no.4C
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    • pp.336-341
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    • 2008
  • In this paper, beamforming algorithm is proposed which can obtain diversity gain in beamforming system that deploy antenna elements with half-wavelength. The proposed algorithm provides beam-pattern using eigen-vectors that span received signal subspace. The criterion to decide optimal rank of eigen-space used for beamforming is also proposed. A beamforming system applied the proposed algorithm shows better performance with diversity gain as getting larger angle spread. This paper provides a description of proposed algorithm with analysis of the performance using various computer simulations.

Pseudo Jacket Matrix and Its MIMO SVD Channel (Pseudo Jacket 행렬을 이용한 MIMO SVD Channel)

  • Yang, Jae-Seung;Kim, Jeong-Su;Lee, Moon-Ho
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.15 no.5
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    • pp.39-49
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    • 2015
  • Some characters and construction theorems of Pseudo Jacket Matrix which is generalized from Jacket Matrix introduced by Jacket Matrices: Construction and Its Application for Fast Cooperative Wireless signal Processing[27] was announced. In this paper, we proposed some examples of Pseudo inverse Jacket matrix, such as $2{\times}4$, $3{\times}6$ non-square matrix for the MIMO channel. Furthermore we derived MIMO singular value decomposition (SVD) pseudo inverse channel and developed application to utilize SVD based on channel estimation of partitioned antenna arrays. This can be also used in MIMO channel and eigen value decomposition (EVD).

Study of Finite Element Eigen Analysis of Beam With Arbitrary Cross Section (임의 단면형상을 갖는 보의 고유치 해석에 관한 연구)

  • Park Keun-Man;Cho Jin-Rae;Jung Weui-Bong;Bai Soo-Ryong
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2006.04a
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    • pp.764-771
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    • 2006
  • The use of 3-D finite elements for the eigen analysis of beam-like structures with arbitrary section shape may not be practical in certain cases, from the aspect of CPU time. In this connection, this paper presents a systematic algorithm for decomposing an arbitrary section into finite number of basic ones and computing essential sectional quantities required for the eigen analysis using the beam theory. The numerical accuracy of the proposed method is assesed from the comparison with the 3-D finite . element method.

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Semidefinite Spectral Clustering (준정부호 스펙트럼의 군집화)

  • Kim, Jae-Hwan;Choi, Seung-Jin
    • Proceedings of the Korean Information Science Society Conference
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    • 2005.07a
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    • pp.892-894
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    • 2005
  • Graph partitioning provides an important tool for data clustering, but is an NP-hard combinatorial optimization problem. Spectral clustering where the clustering is performed by the eigen-decomposition of an affinity matrix [1,2]. This is a popular way of solving the graph partitioning problem. On the other hand, semidefinite relaxation, is an alternative way of relaxing combinatorial optimization. issuing to a convex optimization[4]. In this paper we present a semidefinite programming (SDP) approach to graph equi-partitioning for clustering and then we use eigen-decomposition to obtain an optimal partition set. Therefore, the method is referred to as semidefinite spectral clustering (SSC). Numerical experiments with several artificial and real data sets, demonstrate the useful behavior of our SSC. compared to existing spectral clustering methods.

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A Study on Eigen-properties of a 3-Dim. Resonant Cavity by Krylov-Schur Iteration Method (Krylov-Schur 순환법을 이용한 3-차원 원통구조 도파관의 고유특성 연구)

  • Kim, Yeong Min;Lim, Jong Soo
    • Journal of the Institute of Electronics and Information Engineers
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    • v.51 no.7
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    • pp.142-148
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    • 2014
  • Krylov-Schur iteration method has been applied to the 3-Dim. resonant cavity of a cylindrical form. The vector Helmholtz equation has been analysed for the resonant field strength in homogeneous media by FEM. An eigen-equation has been constructed from element equations basing on tangential edges of the tetrahedra element. This equation made up of two square matrices associated with the curl-curl form of the Helmholtz operator. By performing Krylov-Schur iteration loops on them, Eigen-values and their modes have been determined from the diagonal components of the Schur matrices and its transforming matrices. Eigen-pairs as a result have been revealed visually in the schematic representations. The spectra have been compared with each other to identify the effect of boundary conditions.