• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.444-448
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• 2003

In this paper, the Lagrange dynamics is studied. A state space representation of Lagrange dynamics and control algorithm based on the state feedback pole placement are presented. The state space model presented is descriptor type linear parameter dependent system. It is shown that the control algorithms based on the linear system theory can be applicable to the state space representation of Lagrange dynamics. To show that the linear system theory can be applicable to the state space representation of Lagrange dynamics, the LMI based regional pole-placement design algorithm is developed and present two examples.

• Bulletin of the Korean Mathematical Society
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• v.38 no.2
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• pp.293-302
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• 2001

We improve the greedy algorithm which is one of the general convergence criterion for certain iterative sequence in a given space by building a constructive greedy algorithm on a normed linear space using an arithmetic average of elements. We also show the degree of approximation order is still $Ο(1\sqrt{\n}$) by a bounded linear functional defined on a bounded subset of a normed linear space which offers a good approximation method for neural networks.

• Proceedings of the IEEK Conference
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• 2002.06b
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• pp.109-112
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• 2002

This paper presents relationship between the dynamic behavior of an asynchronous linear pipeline (ALP) and the performance of the ALP as buffers are allocated. Then the relationship is used in order to characterize a local optimum situation on the buffer design space of the ALP. Using the characterization we propose an efficient algorithm optimizing buffer allocation on an ALP in order to achieve its average case performance. Without the loss of optimality, our algorithm works in linear time complexity so it achieves fast buffer-configuration optimization. This paper makes two contributions. First, it describes relationship between the performance characteristics of an ALP and a local optimum on the buffer design space of the ALP. Second, it devises a buffer allocation algorithm finding an optimum solution in linear time complexity.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.1
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• pp.81-86
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• 2012

In this paper, we proposed a new design methodology of a pattern classification rule based on the local linear discriminant analysis expanded from the generic linear discriminant analysis which is used in the local area divided from the whole input space. There are two ways such as k-Means clustering method and the differential evolutionary algorithm to partition the whole input space into the several local areas. K-Means clustering method is the one of the unsupervised clustering methods and the differential evolutionary algorithm is the one of the optimization algorithms. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods.

• Speech Sciences
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• v.11 no.4
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• pp.75-88
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• 2004

In this paper, we propose a new approach to sequential linear regression adaptation of continuous density hidden Markov models (CDHMMs) based on transformation space model (TSM). The proposed TSM which characterizes the a priori knowledge of the training speakers associated with maximum likelihood linear regression (MLLR) matrix parameters is effectively described in terms of the latent variable models. The TSM provides various sources of information such as the correlation information, the prior distribution, and the prior knowledge of the regression parameters that are very useful for rapid adaptation. The quasi-Bayes (QB) estimation algorithm is formulated to incrementally update the hyperparameters of the TSM and regression matrices simultaneously. Experimental results showed that the proposed TSM approach is better than that of the conventional quasi-Bayes linear regression (QBLR) algorithm for a small amount of adaptation data.

• Journal of the Korean Operations Research and Management Science Society
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• v.37 no.4
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• pp.67-72
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• 2012

In this paper a new method of handling linear constraints for the genetic algorithm is suggested. The method is designed to maintain the feasibility of offsprings during the evolution process of the genetic algorithm. In the genetic algorithm, the chromosomes are coded as the vectors in the real vector space constrained by the linear constraints. A method of handling the linear constraints already exists in which all the constraints of equalities are eliminated so that only the constraints of inequalities are considered in the process of the genetic algorithm. In this paper a new method is presented in which all the constraints of inequalities are eliminated so that only the constraints of equalities are considered. Several genetic operators such as arithmetic crossover, simplex crossover, simple crossover and random vector mutation are designed so that the resulting offspring vectors maintain the feasibility subject to the linear constraints in the framework of the new handling method.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.351-359
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• 2010

This paper proposes a linear system control algorithm with collision avoidance in multiple satellites. Consideration of collision avoidance is augmented by adding a weighting term in the cost function of the original tracking problem in linear quadratic control (LQC). Because the proposed algorithm relies on a similar solution procedure to the original LQC, its inherent advantages, including gain-robustness and optimality, are preserved. To confirm and visualize the derived algorithm, a simple example of two-vehicle motion in the two-dimensional plane is illustrated. In addition, the proposed collision avoidance control is applied to satellite formation flying, and verified by numerical simulations.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.896-899
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• 1996

A new algorithm for planning a collision free path is developed based on linear parametric curve. A collision-free path is viewed as a connected space curve in which the path consists of two straight curve connecting start to target point. A single intermediate connection point is considered in this paper and is used to manipulate the shape of path by organizing the control point in polar coordinate (.theta.,.rho.). The algorithm checks interference with obstacles, defined as GM (Geometry Mapping), and maps obstacles in Euclidean Space into images in CPS (Connection Point Space). The GM for all obstacles produces overlapping images of obstacle in CPS. The clear area of CPS that is not occupied by obstacle images represents collision-free paths in Euclidean Space. Any points from the clear area of CPS is a candidate for a collision-free path. A simulation of GM for number of cases are carried out and results are presented including mapped images of GM and performances of algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.5
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• pp.110-117
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• 2002

This paper proposes a optimization of the design variables of linear motor for the improvement of thrust. Especially, this paper treats the shoe, which can be good to flow of a magnetic flux in linear motor. Firstly, this paper uses a space harmonic analysis method(SHAM) based on Fourier series, for analyzing the characteristics of core type linear motor, including slot structure and shoe. And compare the magnetic flux densities of linear motor at air gap with the results of the SHAM and the Finite Element Method(FEM). Secondly, this paper uses a genetic algorithm, which is good to find the global solutions. The design variables are the pole pitch of magnet, the pitch of slot, the height of slot, the width of shoe and the width of magnet. The maximum thrust with optimum design variables is about 247 N which is improved about 16%.

• Journal of KIISE:Software and Applications
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• v.34 no.5
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• pp.397-406
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• 2007

The problem of finding an optimal alignment between sequence A and B can be solved by dynamic programming algorithm(DPA) efficiently. But, if the length of string was longer, the problem might not be solvable because it requires O(m*n) time and space complexity.(where, $m={\mid}A{\mid},\;n={\mid}B{\mid}$) For space, Hirschberg developed a linear space and quadratic time algorithm, so computer memory was no longer a limiting factor for long sequences. As computers's processor and memory become faster and larger, a method is needed to speed processing up, although which uses more space. For this purpose, we present an algorithm which will solve the problem in quadratic time and linear space. By using division method, It computes optimal alignment faster than LSA, although requires more memory. We generalized the algorithm about division problem for not being divided into integer and pruned additional space by entry/exit node concept. Through the proofness and experiment, we identified that our algorithm uses d*(m+n) space and a little more (m*n) time faster than LSA.