• Journal of applied mathematics & informatics
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• v.15 no.1_2
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• pp.485-493
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• 2004

This paper presents the interesting properties of a certain patterned matrix that plays an significant role in the statistical analysis. The necessary and sufficient condition on the existence of the inverse of the patterned matrix and its determinant are derived. In special cases of the patterned matrix, explicit formulas for its inverse, determinant and the characteristic equation are obtained.

• Communications of the Korean Mathematical Society
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• v.11 no.4
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• pp.1159-1174
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• 1996

The spectrum of the Laplacian matrix of a graph gives an information of the structure of the graph. For example, the product of non-zero eigenvalues of the characteristic polynomial of the Laplacian matrix of a graph with n vertices is n times of the number of spanning trees of that graph. The characteristic polynomial of the Laplacian matrix of a graph tells us the number of spanning trees and the connectivity of given graph. in this paper, we compute the characteristic polynomial of the Laplacian matrix of a graph bundle when its voltage lie in an abelian subgroup of the full automorphism group of the fibre; in particular, the automorphism group of the fibre is abelian. Also we study a relation between the characteristic polynomial of the Laplacian matrix of a graph G and that of the Laplacian matrix of a graph bundle over G. Some applications are also discussed.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.1
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• pp.1-9
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• 1988

The purpose of this paper is study the sensitivity analysis of matrix game. Teh sensitivity analysis of matrix is classified into two types. Type one is to find the characteristic region of an element of the pay off matrix in which the value of the current optimal strategy remains as an optimum. Type two is to find that in which the basis of the current optimal strategy does not change. This paper shows the characteristic regions of basic and nonbasic strategies. Further it is found that the characteristic regions of type one and two are same in the case that the element is that of at least one player's nonbasic strategy.

• Korean Management Science Review
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• v.9 no.1
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• pp.87-92
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• 1992

The purpose of this paper is to study the sensitivity analysis in the matrix game. The third type sensitivity analysis is defined as finding the characteristic region of an element of the payoff matrix in which the set of current active strategies is preserved. First by using the relationship between matrix game and linear programming, we induce the conditions which must be satisfied for preserving the set of current active strategies. Second we show the characteristic regions of active and inactive strategy. It is found that the characteristic regions we suggests in this paper are same with that of the type one sensitivity analysis suggested by Sung[3] except only one case.

• Sleep Medicine and Psychophysiology
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• v.8 no.1
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• pp.67-71
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• 2001

Independent Component Analysis (ICA) is a blind source separation method using unsupervised learning and mutual information theory created in the late eighties and developed in the nineties. It has already succeeded in separating eye movement artifacts from human scalp EEG recording. Several characteristic sleep waves such as sleep spindle, K-complex, and positive occipital sharp transient of sleep (POSTS) can be recorded during sleep EEG recording. They are used as stage determining factors of sleep staging and might be reflections of unknown neural sources during sleep. We applied the ICA method to sleep EEG for sleep waves separation. Eighteen channel scalp longitudinal bipolar montage was used for the EEG recording. With the sampling rate of 256Hz, digital EEG data were converted into 18 by n matrix which was used as a original data matrix X. Independent source matrix U (18 by n) was obtained by independent component analysis method ($U=W{\timex}X$, where W is an 18 by 18 matrix obtained by ICA procedures). ICA was applied to the original EEG containing sleep spindle, K-complex, and POSTS. Among the 18 independent components, those containing characteristic shape of sleep waves could be identified. Each independent component was reconstructed into original montage by the product of inverse matrix of W (inv(W)) and U. The reconstructed EEG might be a separation of sleep waves without other components of original EEG matrix X. This result (might) demonstrates that characteristic sleep waves may be separated from original EEG of unknown mixed neural origins by the Independent Component Analysis (ICA) method.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.390-397
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• 1998

An approach to identifying input forces is proposed using measured structural dynamic responses and its analytical model. The identification of input forces is a reverse process and ill-conditioned problem. Its solution is unstable and generally case dependent. In this paper, the ill-condition is described considering characteristic matrix which is defined by reduced dynamic stiffness matrix. Special attention is focused on the condition number of a characteristic matrix used in the solution algorithm of this reverse process. Simple example is presented in support of the ill-condition of a reverse process.

• Bulletin of the Korean Mathematical Society
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• v.33 no.1
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• pp.75-86
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• 1996

Let G be a finite simple connected graph with vertex set V(G) and edge set E(G). Let A(G) be the adjacency matrix of G. The characteristic polynomial of G is the characteristic polynomial $\Phi(G;\lambda) = det(\lambda I - A(G))$ of A(G). A zero of $\Phi(G;\lambda)$ is called an eigenvalue of G.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.934-937
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• 2003

In this paper, authors use a stereo vision system based on the visual model of human and establish inexpensive method that recognizes moving distance using characteristic points around the robot. With the stereovision. the changes of the coordinate values of the characteristic points that are fixed around the robot are measured. Self-displacement and self-localization recognition system is proposed from coordination reconstruction with those changes. To evaluate the proposed system, several characteristic points that is made with a LED around the robot and two cheap USB PC cameras are used. The mobile robot measures the coordinate value of each characteristic point at its initial position. After moving, the robot measures the coordinate values of the characteristic points those are set at the initial position. The mobile robot compares the changes of these several coordinate values and converts transformation matrix from these coordinate changes. As a matrix of the amount and the direction of moving displacement of the mobile robot, the obtained transformation matrix represents self-displacement and self-localization by the environment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.2
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• pp.608-616
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• 2018

If a general nonlinear system is linearized by the successive multiplication of the 1st and 2nd order systems, then there are four types of poles in this linearized system: the pole of the 1st order system and the equal poles, two distinct real poles, and complex conjugate pair of poles of the 2nd order system. Linear Quadratic (LQ) control is a method of designing a control law that minimizes the quadratic performance index. It has the advantage of ensuring the stability of the system and the pole placement of the root of the system by weighted matrix adjustment. LQ control by the weighted matrix can move the position of the pole of the system arbitrarily, but it is difficult to set the weighting matrix by the trial and error method. This problem can be solved using the characteristic equations of the Hamiltonian system, and if the control weighting matrix is a symmetric matrix of constants, it is possible to move several poles of the system to the desired closed loop poles by applying the control law repeatedly. The paper presents a method of calculating the state weighting matrix and the control law for moving the equal poles with Jordan blocks to two real poles using the characteristic equation of the Hamiltonian system. We express this characteristic equation with a state weighting matrix by means of a trigonometric function, and we derive the relation function (${\rho},\;{\theta}$) between the equal poles and the state weighting matrix under the condition that the two real poles are the roots of the characteristic equation. Then, we obtain the moving-range of the two real poles under the condition that the state weighting matrix becomes a positive semi-finite matrix. We calculate the state weighting matrix and the control law by substituting the two real roots selected in the moving-range into the relational function. As an example, we apply the proposed method to a simple example 3rd order system.

• Korean Journal of Optics and Photonics
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• v.4 no.3
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• pp.317-322
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• 1993

In this paper we propose a method to analyze the nonlinear behavior of an integrated-mirror etalon by the characteristic matrix method. If the dependence of the refractive index and the absorption coefficient upon the light intensity are known, we can couple this with an equation by which we can evaluate the light intensity distribution inside an etalon for the given values of the refractive index and the absorption coefficient. By solving these coupled equations by the iteration method, we evaluate the transmission characteristics of a nonlinear integrated-mirror etalon. By the characteristic matrix method, we have demonstrated the static and dynamic bistable behavior of a nonlinear integrated-mirror etalon.