• 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.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.1220-1223
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• 2002

In this paper, numerical robust stability analysis method and its design are presented. L$_2$robust stability of the fuzzy system is analyzed by casting the systems into the diagonal norm bounded linear differential inclusions (DNLDI) formulation. Based on the linear matix inequality (LMI) optimization programming, a numerical method for finding the maximum stable ranges of the fuzzy feedback linarization control gains is proposed.

• 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.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.8 no.1
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• pp.37-45
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• 1994

A method of optimal active and reactive power control for economic operation in electrical power system is presented in this paper. The major features and techniques of this paper are as follows: 1) The method presented for obtaining the equivalent active power balance equation applying the sparse Jacobian matrix of power flow equation instead of using B constant as active power Balance equation considering transmission loss, and for determining directly optimal active power allocation without repeating calculations. 2) More reasonable and economic profit by minimizing total fuel cost of thermal power plants instead of using transmission loss as objective function of reactive Power control can be achieved. 3) Particularly in reactive power control, computing time can be considerably reduced by using Fuzzy Linear Programming instead of using conventional Linear Programming.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.582-589
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• 2003

This paper presents the robust stability analysis and design methodology of the fuzzy feedback linearization control systems. Uncertainty and disturbances with known bounds are assumed to be included Un the Takagi-Sugeno (TS) fuzzy models representing the nonlinear plants. $L_2$ robust stability of the closed system is analyzed by casting the systems into the diagonal norm bounded linear differential inclusions (DNLDI) formulation. Based on the linear matrix inequality (LMI) optimization programming, a numerical method for finding the maximum stable ranges of the fuzzy feedback linearization control gains is also proposed. To verify the effectiveness of the proposed scheme, the robust stability analysis and control design examples are given.

• Industrial Engineering and Management Systems
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• v.13 no.1
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• pp.29-42
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• 2014

Extensive research has been conducted on supplier evaluation and selection as a strategic and crucial component of supply chain management in recent years. However, few articles in the previous literature have been dedicated to the use of fuzzy inference systems as an aid in decision-making. Therefore, this essay attempts to demonstrate the application of this method in evaluating suppliers, based on a comprehensive framework of qualitative and quantitative factors besides the effect of gradual coverage distance. The purpose of this study is to investigate the applicability of the numerous measures and metrics in a multi-objective optimization problem of the supply chain network design with the aim of managing the allocation of orders by coordinating the production lines to satisfy customers' demand. This work presents a dynamic non-linear programming model that examines the important aspects of the strategic planning of the manufacturing in supply chain. The effectiveness of the configured network is illustrated using a sample, following which an exact method is used to solve this multi-objective problem and confirm the validity of the model, and finally the results will be discussed and analyzed.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.9
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• pp.984-993
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• 1992

Fuzzy L.P. with Multi-criteria function is adopted in this VAR planning algorithm to accomplish the optimization of comflicting objectives, such as the amount of the VAR installed and power system loss, while keeping the bus voltage profile within an admissible range. Fuzzy L.P. with Multi-criteria function, a powerful tool dealing with the fuzziness of satisfaction levels of the constraints and the goal of objective functions, enables us to search for the solutions which may contribute in VAR planning. This advantage is not provided by traditional standardized L.P. The effectiveness of the proposed algorithm has been verified by the test on the IEEE-30 bus system.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.31-34
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• 1991

This paper presents a voltage-reactive power control algorithm considering fuzziness. In this paper, a coordination technique based on fuzzy set theory is applied for system loss-voltage compromises. Here, we introduce membership functions to measure the adaptability of real power loss of transmission line and the deviation of load bus voltage from the constraints. Then the optimization of problem is solved by a linear programming technique considering the fuzzy set theory. The objective is a degree of satisfaction about the fuzzy decision-making function. The effectiveness of this algorithm has been verified by testing on sample systems.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.211-213
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• 1992

Fuzzy L. P. with Multi-criteria function is adopted in this VAR allocation algorithm to accomplish the optimization of co-conflicting objectives, such as the amount of the VAR Installed and power system loss, while keeping the bus voltage profile within an admissible range. fuzzy L. P., a powerful tool dealing with the fuzziness of satisfaction levels of the constraints and the goal of objective functions, enables us to search for the solutions which may contribute in VAR planning. This advantage Is not provided by traditional standardized L. P. The effectiveness of the proposed algorithm has been verified by the test on the IEEE-30 bus system.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.103-105
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• 1992

The new approach using fuzzy linear programming with fuzzy multi-criterion is proposed for the best generation mix of a power system. A chracteristic feature of the presented approach is that not only cost but also reliability for goal function can be taken into account by using fuzzy multi-criterion and so more realistic solution can be obtained. The effectiveness of the proposed approach is demonstrated by the best generation mix problem of KEPCO-system size model which contains nuclear, coal, LNG, oil and pump-generator hydro plant in multi-years.