• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.105-109
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• 2008

A robust optimal control system design algorithm in active suspension equipment adopting linear matrix inequalities control system design theory is presented. The validity of the linear matrix inequalities robust control system design in active suspension system through the numerical examples is also investigated.

• Journal of applied mathematics & informatics
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• v.29 no.1_2
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• pp.485-495
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• 2011

Let $A_1$ and $A_2$ be $n{\times}n$ nonzero complex matrices, denote a linear combination of the two matrices by $A=c_1A_1+c_2A_2$, where $c_1$, $c_2$ are nonzero complex numbers. In this paper, we research the problem of the linear combinations in the general case. We give a sufficient and necessary condition for A is an involutive matrix and s+1-potent matrix, respectively, where $A_1$ is a tripotent matrix, with $A_1A_2=A_2A_1$. Then, using the results, we also give the sufficient and necessary conditions for the involutory of the linear combination A, where $A_1$ is a tripotent matrix, anti-idempotent matrix, and involutive matrix, respectively, and $A_2$ is a tripotent matrix, idempotent matrix, and involutive matrix, respectively, with $A_1A_2=A_2A_1$.

• Journal of applied mathematics & informatics
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• v.8 no.2
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• pp.651-658
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• 2001

The projection matrix plays an important role in the linear model theory. In this paper we derive an algebraic relationship between the projection matrices of submatrices of the design matrix. Using this relationship we can easily obtain the projection matrices of any submatrices of the design matrix. Also we show that every projection matrix can be obtained as a linear combination of Kronecker products of identity matrices and matrices with all elements equal to 1.

• Communications of the Korean Mathematical Society
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• v.35 no.2
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• pp.499-516
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• 2020

In this paper, we denote by L the block matrix linear relation, acting on the Banach space X ⊕ Y, of the form ${\mathcal{L}}=\(\array{A&B\\C&D}\)$, where A, B, C and D are four linear relations with dense domains. We first try to determine the conditions under which a block matrix linear relation becomes a demicompact block matrix linear relation (see Theorems 4.1 and 4.2). Second we study Shechter spectra using demicompact linear relations and relatively demicompact linear relations (see Theorem 5.1).

• Journal of the Korean School Mathematics Society
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• v.12 no.1
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• pp.99-114
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• 2009

Although they are interested in Linear Algebra not only in science and engineering but also in humanities and sociology recently, a study of teaching linear algebra is not relatively abundant because linear algebra was taken as basic course in colleges just for 20-30 years. However, after establishing The Linear Algebra Curriculum Study Group in January, 1990, a variety of attempts to improve teaching linear algebra have been emerging. This article looks into series of studies related with teaching matrix. For this the method for teaching the concepts of matrix in relation to historico-genetic principle looking through the process of the conceptual development of matrix-determinants, matrix-systems of linear equations and linear transformation.

• Journal of the Korean Mathematical Society
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• v.50 no.1
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• pp.127-136
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• 2013

The term rank of a matrix A is the least number of lines (rows or columns) needed to include all the nonzero entries in A. In this paper, we obtain a characterization of linear transformations that preserve term ranks of matrices over antinegative semirings. That is, we show that a linear transformation T from a matrix space into another matrix space over antinegative semirings preserves term rank if and only if T preserves any two term ranks $k$ and $l$.

• Journal of applied mathematics & informatics
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• v.35 no.1_2
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• pp.165-180
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• 2017

In this study, Taylor matrix algorithm is designed for the approximate solution of linear and non-linear differential equation systems. The algorithm is essentially based on the expansion of the functions in differential equation systems to Taylor series and substituting the matrix forms of these expansions into the given equation systems. Using the Mathematica program, the matrix equations are solved and the unknown Taylor coefficients are found approximately. The presented numerical approach is discussed on samples from various linear and non-linear differential equation systems as well as stiff systems. The computational data are then compared with those of some earlier numerical or exact results. As a result, this comparison demonstrates that the proposed method is accurate and reliable.

• Journal of Korean Institute of Industrial Engineers
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• v.14 no.1
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• pp.43-49
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• 1988

The purpose of this paper is to study the sensitivity analysis of matrix game by means of linear programming. The relations between matrix game and linear programming is well known. In this paper we first transform matrix game into linear programming. The sensitivity analysis of matrix game is performed by that of linear programming.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.476-479
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• 2000

This paper deals with a robust pole placement method for uncertain linear systems. For all admissible uncertain parameters, a static output feedback controller is designed such that all the poles of the closed loop system are located within the prespecfied disk. It is shown that the existence of a positive definite matrix belonging to a convex set such that its inverse belongs to another convex set guarantees the existence of the output feedback gain matrix for our control problem. By a sequence of convex optimization the aforementioned matrix is obtained. A numerical example is solved in order to illustrate efficacy of our design method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.7
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• pp.739-748
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• 1992

A CGM iterative systolic algorithm to solve large sparse linear systems of equations is presented. For implementation of the algorithm, a systolic array using the stripe structure is proposed. The matrix A is decomposed into a strictly lower triangular matrix, a diagonal matrix, and a strictly up-per triangular matrix, and the two formers and the tatter· are concurrently computed by different linear arrays. Hence, the execution time of this approach Is reduced to half of the execution time of the that a linear array is used. computation of the Irregularly distributed sparse matrix can be executed effectively by using the stripe structure.