• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.4
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• pp.513-522
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• 2019

We calculate the benefit of distributed combined heat power generators from avoiding a transmission expansion cost by building distributed generators near electricity demand centers. We determine a transmission expansion plan by solving a mixed integer linear problem, where we modify capacities of existing transmission lines and build new transmission lines. We calculate the benefit by comparing the sum of generation and transmission expansion costs with or without distributed generators through two simulation frames. In the first frame, for the current demand, we substitute existing distributed generators for non-distributed generators and measure an additional cost to balance the generation and demand. In the second frame, for increased future demand, we compare the cost to invest only in distributed generators to the cost to invest only in non-distributed generators. As a result, we show that the distributed generators have at least 5.8 won/kWh of the benefit from avoiding the transmission expansion cost.

• Journal of applied mathematics & informatics
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• v.29 no.5_6
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• pp.1395-1407
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• 2011

When a Sequential Quadratic Programming (SQP) method is used to solve the nonlinear programming problems, one of the main difficulties is that the Quadratic Programming (QP) subproblem may be incompatible. In this paper, an SQP algorithm is given by modifying the traditional QP subproblem and applying a class of $l_{\infty}$ penalty function whose penalty parameters can be adjusted automatically. The new QP subproblem is compatible. Under the extended Mangasarian-Fromovitz constraint qualification condition and the boundedness of the iterates, the algorithm is showed to be globally convergent to a KKT point of the non-linear programming problem.

• Journal of the military operations research society of Korea
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• v.28 no.2
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• pp.34-55
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• 2002

This paper deals with the multiple attribute decision making problem when a decision maker incompletely articulates his/her preferences about the attribute weight and alternative value. Furthermore, we consider the attribute tree which is structured hierarchically. Techniques for establishing dominance with linear partial information are proposed in a hierarchically structured attribute tree. The linear additive value function under certainty is used in the model. The incompletely specified information constructs a feasible region of linear constraints and therefore the pairwise dominance relationship between alternatives leads to intractable non-linear programming. Hence, we propose solution techniques to handle this difficulty. Also, to handle the tree structure, we break down the attribute tree into sub-trees. Due to there cursive structure of the solution technique, the optimization results from sub-trees can be utilized in computing the value interval on the topmost attribute. The value intervals computed by the proposed solution techniques can be used to establishing the pairwise dominance relation between alternatives. In this paper, pairwise dominance relation will be represented as strict dominance and weak dominance, which ware already defined in earlier researches.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.110-113
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• 1991

An electromagnet is one of the important devices in magnetic levitation system. Its weight takes large part in the total weight of a vehicle. That is the reason why it is important to design the electromagnet optimally to maximize the attraction force with constant volume. This study presents the optimum value of the design variables which can produce the maximal attraction force under constant magnet volume. For this, non-linear programming in optimization technique is used. And to confirm reliability of the results, the optimally designed electromagnet is analyzed by FEM. The attraction force of the optimally designed electromagnet is increased maximally 72% compared with that of the basic model. And the results obtained by non-linear programming has 30% error compared with that of FEM.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1441-1452
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• 2015

This paper proposes a new Hybrid Particle Swarm Optimization (HPSO) method that integrates the Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) techniques. The proposed method is applied to solve Economic Dispatch(ED) problems considering prohibited operating zones, ramp rate limits, capacity limits and power balance constraints. In the proposed HPSO method, the best features of both EP and PSO are exploited, and it is capable of finding the most optimal solution for the non-linear optimization problems. For validating the proposed method, it has been tested on the standard three, six, fifteen and twenty unit test systems. The numerical results show that the proposed HPSO method is well suitable for solving non-linear economic dispatch problems, and it outperforms the EP, PSO and other modern metaheuristic optimization methods reported in the recent literatures.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.272-272
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• 2000

since the controller is part or the overall closed-Loop system, it is necessary that the designed controller be able to tolerate some uncertainty in its coefficients. The adequate stability and performance margins are required for the designed nominal controllers. In the paper. we study the method to design the non-fragile fixed-structured controller for real parametric uncertain systems. When we impose the controller parameter perturbation, the structure of the controller must be given. Therefore, we assume that the controller has fixed-structure. The fixed-structure controller is practically necessary especially when the robust controller synthesis results in a high-order controller. In SISO systems, we propose the robust controller design method using the Mapping theorem. In the method, the plant uncertainty and controller Parameter are of the multilineal form in the stability and performance conditions. Then, the controller synthesis problem is easily recast to Linear Programming Problem.

• Journal of applied mathematics & informatics
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• v.18 no.1_2
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• pp.1-23
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• 2005

The decentralized static output feedback design problem is considered. A constrained trust region method is developed that solves this optimal control problem when a complete set of state variables is not available. The considered problem is interpreted as a non-linear (non-convex) constrained matrix optimization problem. Then, a decentralized constrained trust region method is developed for this problem class exploiting the diagonal structure of the problem and using inexact computations. Finally, numerical results are given for the proposed method.

• Smart Structures and Systems
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• v.5 no.1
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• pp.69-79
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• 2009

A non-clipped semi-active stochastic optimal control strategy for nonlinear structural systems with MR dampers is developed based on the stochastic averaging method and stochastic dynamical programming principle. A nonlinear stochastic control structure is first modeled as a semi-actively controlled, stochastically excited and dissipated Hamiltonian system. The control force of an MR damper is separated into passive and semi-active parts. The passive control force components, coupled in structural mode space, are incorporated in the drift coefficients by directly using the stochastic averaging method. Then the stochastic dynamical programming principle is applied to establish a dynamical programming equation, from which the semi-active optimal control law is determined and implementable by MR dampers without clipping in terms of the Bingham model. Under the condition on the control performance function given in section 3, the expressions of nonlinear and linear non-clipped semi-active optimal control force components are obtained as well as the non-clipped semi-active LQG control force, and thus the value function and semi-active nonlinear optimal control force are actually existent according to the developed strategy. An example of the controlled stochastic hysteretic column is given to illustrate the application and effectiveness of the developed semi-active optimal control strategy.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.6
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• pp.780-785
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• 2010

This paper introduces GP(Genetic Programming) based color detection model for an object detection and tracking. Existing color detection methods have used linear/nonlinear transformatin of RGB color-model and improved color model for illumination variation by optimization or learning techniques. However, most of cases have difficulties to classify various of colors because of interference of among color channels and are not robust for illumination variation. To solve these problems, we propose illumination robust and non-parametric multi-colors detection model using evolution of GP. The proposed method is compared to the existing color-models for various colors and images with different lighting conditions.

• Journal of the Korean Operations Research and Management Science Society
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• v.6 no.2
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• pp.35-49
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• 1981

This paper is concerned with developing a Multi-period Behavioral Model for the portfolio selection problem. The unique feature of the model is that it treats a number of factors and decision variables considered germane in decision making on an interrelated basis. The formulated problem has the structure of a Chance Constrained programming Model. Then empoloying arguments of Central Limit Theorem and normality assumption the stochastic model is reduced to that of a Non-Linear Programming Model. Finally, a number of interesting properties for the reduced model are established.