• Journal of applied mathematics & informatics
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• v.3 no.2
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• pp.129-148
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• 1996

This paper propose the inference mechanism for handling linear polynomial constraints called consistency checking algorithm based on the feasibility checking algorithm borrowed from linear pro-gramming. in contrast with other approaches proposed algorithm can efficiently and coherented by linear polynomial forms. The developed algorithm is successfully applied to the symbolic simulation that offers a convenient means to conduct multiple simultaneous exploration of model behaviors.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.3B no.3
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• pp.128-132
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• 2003

This paper presents the optimum design of a Linear Induction Motor (LIM) using Genetic algorithm, Niching Genetic algorithm and Neural Network. The design variables are optimized by different optimization methods and the results are discussed.

• 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.

• Communications for Statistical Applications and Methods
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• v.7 no.1
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• pp.47-54
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• 2000

This article is concerned with the algorithm for the Chebyshev estimation with/without linear equality and/or inequality constraints. The algorithm employs a linear scaling transformation scheme to reduce the computational burden which is induced when the data set is quite large. The convergence of the proposed algorithm is proved. And the updating and orthogonal decomposition techniques are considered to improve the computational efficiency and numerical stability.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.1
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• pp.20-26
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• 2011

In this paper, we propose an efficient algorithm for reducing the complexity of LDPC code decoding by using node monitoring (NM) and Piecewise Linear Function Approximation (NP). This NM algorithm is based on a new node-threshold method, and the message passing algorithm. Piecewise linear function approximation is used to reduce the complexity for more. This algorithm was simulated in order to verify its efficiency. Simulation results show that the complexity of our NM algorithm is reduced to about 20%, compared with thoes of well-known method.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.349-353
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• 2008

Using the recently developed ABS algorithm for solving linear Diophantine equations we introduce an algorithm for solving a system of m linear integer inequalities in n variables, m $\leq$ n, with full rank coefficient matrix. We apply this result to solve linear integer programming problems with m $\leq$ n inequalities.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.1
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• pp.147-158
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• 1995

In this paper a face optimization algorithm is developed for solving the problem (P) of optimizing a linear function over the set of efficient solution of a bicriterion linear program. We show that problem (P) can arise in a variety of practical situations. Since the efficient set is in general a nonoconvex set, problem (P) can be classified as a global optimization problem. The algorithm for solving problem (P) is guaranteed to find an exact optimal or almost exact optimal solution for the problem in a finite number of iterations. The algorithm can be easily implemented using only linear programming method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1034-1037
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• 2003

Recently in the field of precision positioning device, the contact-free actuators are gaining focuses with their outstanding performances by eliminating mechanical frictions. Th is paper is about the driving algorithm for contact-free linear actuator. The proposed driving algorithm has similar structure of drives of switched reluctance motor and reduces the normal forces and force ripple. The simulation and experiment are executed to verify the proposed method.

• Communications for Statistical Applications and Methods
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• v.5 no.3
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• pp.581-589
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• 1998

An algorithm is proposed for the $L_1$-estimation with linear equality and inequality constraints in linear regression model. The algorithm employs a linear scaling transformation to obtain the optimal solution of linear programming type problem. And a special scheme is used to maintain the feasibility of the updated solution at each iteration. The convergence of the proposed algorithm is proved. In addition, the updating and orthogonal decomposition techniques are employed to improve the computational efficiency and numerical stability.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.11
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• pp.1231-1238
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• 2015

In this paper, we proposed an efficient algorithm using Node monitoring (NM) and Piecewise Linear Function Approximation(: NP) for reducing the complexity of LDPC code decoding. Proposed NM algorithm is based on a new node-threshold method together with message passing algorithm. Piecewise linear function approximation is used to reduce the complexity of the algorithm. This new algorithm was simulated in order to verify its efficiency. Complexity of our new NM algorithm is improved to about 20% compared with well-known methods according to simulation results.