• Title/Summary/Keyword: square of matrix

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Generalized Intuitionistic Fuzzy Matrices

  • Park, Jin-Han;Park, Yong-Beom
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2004.10a
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    • pp.351-354
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    • 2004
  • Using the idea of generalized intuitionistic fuzzy set, we study the notion of generalized intuitionistic fuzzy matrices as a generalization of fuzzy matrices, We show that some properties of a square generalized intuitionistic fuzzy matrix such as reflexivity, transitivity and circularity are carried over to the adjoint generalized intuitionistic fuzzy matrix.

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A NOTE ON CONVERTIBLE {0,1} MATRICES

  • Kim, Si-Ju;Park, Taeg-Young
    • Communications of the Korean Mathematical Society
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    • v.12 no.4
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    • pp.841-849
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    • 1997
  • A square matrix A with $per A \neq 0$ is called convertible if there exists a {1, -1} matrix H such that $per A = det(H \circ A)$ where $H \circ A$ denote the Hadamard product of H and A. In this paper, ranks of convertible {0, 1} matrices are investigated and the existence of maximal convertible matrices with its rank r for each integer r with $\left\lceil \frac{n}{2} \right\rceil \leq r \leq n$ is proved.

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Numerical solution of linear elasticity by preconditioning cubic spline collocation

  • Lee, Yong-Hun
    • Communications of the Korean Mathematical Society
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    • v.11 no.3
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    • pp.867-880
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    • 1996
  • Numerical approximations to the linear elasticity are traditionally based on the finite element method. In this paper we propose a new formulation based on the cubic spline collocation method for linear elastic problem on the unit square. We present several numerical results for the eigenvalues of the matrix represented by cubic collocation method and preconditioner matrix which is preconditioned by FEM and FDM. Finally we present the numerical solution for some example equation.

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An approach to improving the Lindley estimator

  • Park, Tae-Ryoung;Baek, Hoh-Yoo
    • Journal of the Korean Data and Information Science Society
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    • v.22 no.6
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    • pp.1251-1256
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    • 2011
  • Consider a p-variate ($p{\geq}4$) normal distribution with mean ${\theta}$ and identity covariance matrix. Using a simple property of noncentral chi square distribution, the generalized Bayes estimators dominating the Lindley estimator under quadratic loss are given based on the methods of Brown, Brewster and Zidek for estimating a normal variance. This result can be extended the cases where covariance matrix is completely unknown or ${\Sigma}={\sigma}^2I$ for an unknown scalar ${\sigma}^2$.

Study on The Stiffness Locking Phenomenon and Eigen Problem in Mindlin Plate (Mindlin 판의 강성 과잉 현상과 고유치에 관한 연구)

  • 김용우;박춘수;민옥기
    • Transactions of the Korean Society of Mechanical Engineers
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    • v.15 no.2
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    • pp.445-454
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    • 1991
  • In this thesis, Mindlin plate element with nine nodes and three degrees-of-freedom at each node is formulated and is employed in eigen-analysis of a rectangular plates in order to alleviate locking phenomenon of eigenvalues. Eigenvalues and their modes may be locked if conventional $C_{0}$-isoparametric element is used. In order to reduce stiffness locking phenomenon, two methods (1, the general reduced and selective integration, 2, the new element that use of modified shape function) are studied. Additionally in order to reduce the error due to mass matrix, two mass matrixes (1, Gauss-Legendre mass matrix, 2, Gauss-Lobatto mass matrix) are considered. The results of eigen-analysis for two models (the square plate with all edges simply-supported and all edges built-in), computed by two methods for stiffness matrix and by two mass matrixes are compared with theoretical solutions and conventional numerical solutions. These comparisons show that the performance of the two methods with Gauss-Lobatto mass matrix is better than that of the conventional plate element. But, by considering the spurious rigid body motions, the element which employs modified shape function with full integration and Gauss-Lobatto mass matrix can elevate the accuracy and convergence of numerical solutions.

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).

Robust Optimal Control of Robot Manipulators with a Weighting Matrix Determination Algorithm

  • Kim, Mi-Kyung;Kang, Hee-Jun
    • International Journal of Precision Engineering and Manufacturing
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    • v.5 no.3
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    • pp.77-84
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    • 2004
  • A robust optimal control design is proposed in this study for rigid robotic systems under the unknown loads and the other uncertainties. The uncertainties are reflected in the performance index, where the uncertainties are bounded for the quadratic square of the states with a positive definite weighting matrix. An iterative algorithm is presented for the determination of the weighting matrix required for necessary robustness. Computer simulations have been done for a weight-lifting operation of a two-link manipulator and the simulation results shows that the proposed algorithm is very effective for a robust control of robotic systems.

SOME PERMANENTAL INEQUALITIES

  • Hwang, Suk-Geun
    • Bulletin of the Korean Mathematical Society
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    • v.26 no.1
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    • pp.35-42
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    • 1989
  • Let .ohm.$_{n}$ and Pm $t_{n}$ denote the sets of all n*n doubly stochastic matrices and the set of all n*n permutation matrices respectively. For m*n matrices A=[ $a_{ij}$ ], B=[ $b_{ij}$ ] we write A.leq.B(A$a_{ij}$ .leq. $b_{ij}$ ( $a_{ij}$ < $b_{ij}$ ) for all i=1,..,m; j=1,..,n. Let $I_{n}$ denote the identity matrix of order n, let $J_{n}$ denote the n*n matrix all of whose entries are 1/n, and let $K_{n}$=n $J_{n}$. For a complex square matrix A, the permanent of A is denoted by per A. Let $E_{ij}$ denote the matrix of suitable size all of whose entries are zeros except for the (i,j)-entry which is one.hich is one.

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Application of covariance adjustment to seemingly unrelated multivariate regressions

  • Wang, Lichun;Pettit, Lawrence
    • Communications for Statistical Applications and Methods
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    • v.25 no.6
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    • pp.577-590
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    • 2018
  • Employing the covariance adjustment technique, we show that in the system of two seemingly unrelated multivariate regressions the estimator of regression coefficients can be expressed as a matrix power series, and conclude that the matrix series only has a unique simpler form. In the case that the covariance matrix of the system is unknown, we define a two-stage estimator for the regression coefficients which is shown to be unique and unbiased. Numerical simulations are also presented to illustrate its superiority over the ordinary least square estimator. Also, as an example we apply our results to the seemingly unrelated growth curve models.

Square and Cube Root Algorithms in Finite Field and Their Applications (유한체상의 제곱근과 세제곱근을 찾는 알고리즘과 그 응용)

  • Cho, Gook Hwa;Ha, Eunhye;Koo, Namhun;Kwon, Soonhak
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.37A no.12
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    • pp.1031-1037
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    • 2012
  • We study an algorithm that can efficiently find square roots and cube roots by modifying Tonelli-Shanks algorithm, which has an application in Number Field Sieve (NFS). The Number Field Sieve, the fastest known factoring algorithm, is a powerful tool for factoring very large integer. NFS first chooses two polynomials having common root modulo N, and it consists of the following four major steps; 1. Polynomial Selection 2. Sieving 3. Matrix 4. Square Root. The last step of NFS needs the process of square root computation in Number Field, which can be computed via square root algorithm over finite field.