• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.2
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• pp.30-37
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• 2014

In this study, we studied how to perform the structural analysis which need a large-capacity flash memory with the computer program when the flash memory storage of a personal computer has no enough room for the analysis of structure. As one of the solutions of this problem, the blocking method of stiffness matrix, which is a method that stiffness matrix is divided by a few blocks and each block is sequentially used for the calculation of matrix decomposition, is proposed and an algorithm available in computer program is derived on the method. Finally, A structural analysis program (sNs) based on this study is developed and the correctness and efficiency of the algorithm is founded through some examples which are fundamental in structural analysis.

• Journal of Korean Society for Quality Management
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• v.37 no.3
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• pp.39-53
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• 2009

This paper suggests a new process structuring method, which we call process modularization, for decomposing and grouping activities in a process. Above all, we propose the concept of a module that is a group of activities positioned on the same flow before and after control constructs. Since activities in a module are relatively strongly interrelated with one another, it is important to take into consideration of these together. A design structure matrix (DSM) is used to structure the process because it has a lot of advantages in process modeling and analysis. We developed two algorithms: the restricted topological sorting (RTS) algorithm for ordering activities and the module finding (MF) algorithm for detecting modules in a process, which utilize the DSM. The suggested approach enables a firm's manager to design and analyze the process effectively. We also developed a supporting tool to accelerate the progress of process modularization. The supporting tool aids the process manager in finding the module and understanding the process structure easily. An illustrative example is addressed to show operations of the suggested approach.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.737-741
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• 2009

This paper addresses the factorization method to estimate the projective structure of a scene from feature (points) correspondences over images with occlusions. We propose both a column and a row space approaches to estimate the depth parameter using the subspace constraints. The projective depth parameters are estimated by maximizing projection onto the subspace based either on the Joint Projection matrix (JPM) or on the the Joint Structure matrix (JSM). We perform the maximization over significant observation and employ Tardif's Camera Basis Constraints (CBC) method for the matrix factorization, thus the missing data problem can be overcome. The depth estimation and the matrix factorization alternate until convergence is reached. Result of Experiments on both real and synthetic image sequences has confirmed the effectiveness of our proposed method.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.289-297
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• 1994

A block design will be said to have Property C if the concordance matrix can be expressed as a linear combination of Kronecker product of permutation matrices. No matrix inversions are necessary for the intrablock analysis of the block designs which possesses the Property C(Paik, 1985). In this paper, in order to show the Li type PBIB designs possesses the Property C, we suggest the structure of the concordance matrices of Li type PBIB designs are multi-nested block circulant pattern.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.2
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• pp.189-196
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• 1994

This paper describes a parallel computing algorithm in Gauss elimination of Jacobian matrix to large-scale power system. The structure of Jacobian matrix becomes different according to ordering method of buses. In sequential computation buses are ordered to minimize the number of fill-in in the triangulation of the Jacobian matrix. The proposed method develops the parallelism in the Gauss elimination by using ND(nested dissection) ordering. In this procedure the level structure of the power system network is transformed to be long and narrow by using end buses which results in balance of computing load among processes and maximization of parallel computation. Each processor uses the sequential computation method to preserve the sqarsity of matrix.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.844-848
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• 2005

A method to predict the strain responses from the measurements of displacement responses is considered. The method uses a transformation matrix which is composed of a displacement modal matrix and a strain modal matrix. The method can predict strains at points where displacements are not measured as well as at displacement measuring points. One of the drawbacks of the strain prediction method is that the displacement responses must be measured at many points on a structure simultaneously. This difficulty can be overcome by measuring the FRFs between displacements at a reference point and other point in sequence with a two channel measuring equipment This procedure is based on the assumption that the characteristics of excitation applied to the structure do not vary with time.

• Proceedings of the KAIS Fall Conference
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• 2010.05a
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• pp.18-23
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• 2010

4$\times$4 Butler Matrix structure has been presented in this paper. It can passes the signal with equal power level and phase difference in the 824MHz to 894MHz frequency of the cellular band. Conventional Butler Matrix was implemented as a single layer substrate structure, but in this paper, we use multi-layer substrate structure and eventually we could get it reduced in size than others. We also used the LTCC coupler to reduce the size effectively, instead of using $90^{\circ}$ hybrid coupler composed of microstrip structure. we used Designer V3.5 Ansoft HFSS V11 for design of 4$\times$4 Butler matrix. Finally, we get good agreements between simulation and experimental results.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.81-96
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• 2017

Cumulative logit random effects models are typically used to analyze longitudinal ordinal data. The random effects covariance matrix is used in the models to demonstrate both subject-specific and time variations. The covariance matrix may also be homogeneous; however, the structure of the covariance matrix is assumed to be homoscedastic and restricted because the matrix is high-dimensional and should be positive definite. To satisfy these restrictions two Cholesky decomposition methods were proposed in linear (mixed) models for the random effects precision matrix and the random effects covariance matrix, respectively: modified Cholesky and moving average Cholesky decompositions. In this paper, we use these two methods to model the random effects precision matrix and the random effects covariance matrix in cumulative logit random effects models for longitudinal ordinal data. The methods are illustrated by a lung cancer data set.

• Structural Engineering and Mechanics
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• v.42 no.1
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• pp.39-53
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• 2012

This paper presents an alternative way to derive the exact element stiffness matrix for a beam on Winkler foundation and the fixed-end force vector due to a linearly distributed load. The element flexibility matrix is derived first and forms the core of the exact element stiffness matrix. The governing differential compatibility of the problem is derived using the virtual force principle and solved to obtain the exact moment interpolation functions. The matrix virtual force equation is employed to obtain the exact element flexibility matrix using the exact moment interpolation functions. The so-called "natural" element stiffness matrix is obtained by inverting the exact element flexibility matrix. Two numerical examples are used to verify the accuracy and the efficiency of the natural beam element on Winkler foundation.

• Journal of Korea Multimedia Society
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• v.15 no.6
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• pp.770-783
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• 2012

In this paper, we propose a novel structure recovery algorithm in the projective space using image feature points. We use normalized image feature coordinates for the numerical stability. To acquire an initial value of the structure and motion, we decompose the scaled measurement matrix using the singular value decomposition. When recovering structure and motion in projective space, we introduce matrix decomposition constraints. In the reconstruction procedure, a nonlinear iterative optimization technique is used. Experimental results showed that the proposed method provides proper accuracy and the error deviation is small.