• Korean Management Science Review
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• v.13 no.2
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• pp.205-213
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• 1996

This paper develops a simple interactive procedure which can be efficiently used to solve a bicriteria linear programming problem. The procedure exploits the relatively simple structure of the bicriterion linear programming problem. Its application to a transportation problem is also presented. The results demonstrate that the method developed in this paper could be easily applicable to any bicriteria linear program in general.

• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.2
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• pp.21-23
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• 1981

It is known that two-person zero-sum game with vector payoff can be reduced to a multiple objective linear programming. However, in this case, solutions for the game nay not be one, but many, In many cases in reality, one may need only one solution rather than all solutions. This paper develops a method to find a practical solution for the game by linear programming.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.477-484
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• 2001

This paper proposes a hybrid learning algorithm combined momentum and kernel-adatron for improving the performance of regression support vector machine. The momentum is utilized for high-speed convergence by restraining the oscillation in the process of converging to the optimal solution, and the kernel-adatron algorithm is also utilized for the capability by working in nonlinear feature spaces and the simple implementation. The proposed algorithm has been applied to the 1-dimension and 2-dimension nonlinear function regression problems. The simulation results show that the proposed algorithm has better the learning speed and performance of the regression, in comparison with those quadratic programming and kernel-adatron algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.100-104
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• 2003

The SVDD(support vector data description) is one of the most well-known one-class support vector learning methods, in which one tries the strategy of utilizing balls defined on the kernel feature space in order to distinguish a set of normal data from all other possible abnormal objects. The major concern of this paper is to consider the problem of modifying the SVDD into the direction of utilizing ellipsoids instead of balls in order to enable better classification performance. After a brief review about the original SVDD method, this paper establishes a new method utilizing ellipsoids in feature space, and presents a solution in the form of SDP(semi-definite programming) which is an optimization problem based on linear matrix inequalities.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.319-322
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• 1997

In vector control technique, stator currents of an induction motor are transformed to equivalent d-q currents in reference frame consist of d and q axis, each of which is coincide with flux and torque direction respectively. In this paper, the new algorithm is suggested where the stator current through an induction motor torque is monitored by using a vector control method where an additional equipment is not need. The G-programming is used to apply the suggested algorithm in the experiment and this is applied to an actual system to monitor the torque value of an induction motor on real time. To solve the vibration trouble of estimated torque caused from an unbalanced real rotating speed of an induction motor and measured rotating speed by suggesting the reconstructed in a method based on measurement current signal. This produced system testifies an accuracy of an induction motor through the experiment by comparing the reference value of the control method.

• 한국데이터정보과학회:학술대회논문집
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• 2004.10a
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• pp.53-59
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• 2004

Support vector machine(SVM) approach to regression can be found in information science literature. SVM implements the regularization technique which has been introduced as a way of controlling the smoothness properties of regression function. In this paper, we propose a new estimation method based on quadratic loss SVM for a linear fuzzy regression model of Tanaka's, and furthermore propose a estimation method for nonlinear fuzzy regression. This approach is a very attractive approach to evaluate nonlinear fuzzy model with crisp input and output data.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.4 no.2
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• pp.59-66
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• 2000

We consider a multiobjective fractional variational programming problem (P) involving vector valued functions. By using the concept of proper efficiency, a relationship between the primal problem and parametric multiobjective variational problem is indicated.

• The Mathematical Education
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• v.28 no.1
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• pp.41-45
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• 1989

Subdifferentiability of a vector-valued set function is defined first. Then in terms of zero-like functions, a perperly efficient solution of a convex programming problem with set functions will be characterized.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.96-99
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• 1992

Currently, there are various robot programming methods for articulated robots. Although each method has merits and drawbacks, they have commonly weak points for practical application, and especially the weak point can be even more vulnerable when the robot programming requires the subtle feelings of human being. This is because the movement of a human being is synthetic while the robot programming is analytic. Therefore, the present method of programming has limits in performing these kinds of subtle robot movement. In this paper, we propose a direct robot programming method, which generates robot programs based on the force/torque vector applied to a force/torque sensor by the human operator. The method reduces the effort required in the robot programming.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.67-72
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval linear and nonlinear regression models combining the possibility and necessity estimation formulation with the principle of SVM. For data sets with crisp inputs and interval outputs, the possibility and necessity models have been recently utilized, which are based on quadratic programming approach giving more diverse spread coefficients than a linear programming one. SVM also uses quadratic programming approach whose another advantage in interval regression analysis is to be able to integrate both the property of central tendency in least squares and the possibilistic property In fuzzy regression. However this is not a computationally expensive way. SVM allows us to perform interval nonlinear regression analysis by constructing an interval linear regression function in a high dimensional feature space. In particular, SVM is a very attractive approach to model nonlinear interval data. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function for interval nonlinear regression model with crisp inputs and interval output. Experimental results are then presented which indicate the performance of this algorithm.