• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2003년도 추계 학술발표회논문집
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• pp.497-504
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• 2003

An optimal design method for hybrid structural control system of building structures subject to earthquake excitation is presented in this paper. Designing a hybrid structural control system nay be defined as a process that optimizes the capacities and configuration of passive and active control systems as well as structural members. The optimal design proceeds by formulating the optimization problem via a multi-stage goal programming technique and, then, by finding reasonable solution to the optimization problem by means of a goal-updating genetic algorithm. The process of the integrated optimization design is illustrated by a numerical simulation of a nine-story building structure subject to earthquake excitation. The effectiveness of the proposed method is demonstrated by comparing the optimally designed results with those of a hybrid structural control system where structural members, passive and active control systems are uniformly distributed.

• 대한수학회보
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• 제56권6호
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• pp.1467-1483
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• 2019

This paper analyzes a robust optimal reinsurance and investment strategy for an Ambiguity-Averse Insurer (AAI), who worries about model misspecification and insists on seeking robust optimal strategies. The AAI's surplus process is assumed to follow a jump-diffusion model, and he is allowed to purchase proportional reinsurance or acquire new business, meanwhile invest his surplus in a risk-free asset and a risky-asset, whose price is described by an Ornstein-Uhlenbeck process. Under the criterion for maximizing the expected exponential utility of terminal wealth, robust optimal strategy and value function are derived by applying the stochastic dynamic programming approach. Serval numerical examples are given to illustrate the impact of model parameters on the robust optimal strategies and the loss utility function from ignoring the model uncertainty.

• 품질경영학회지
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• 제47권1호
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• pp.87-96
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• 2019

Purpose: The purpose of this paper is to determine an optimal production time for economic production quantity model with preventive maintenance and random defective rate as the function of a machinery deteriorates. Methods: If a machinery shifts from "in-control" state to "out-of-control" state, a proportion of defective items being produced increases. It is assumed that time to state shift is a random variable and follows an arbitrary distribution. The elapsed time until process shift decreases stochastically as a production cycle repeats and quasi-renewal process is used to implement for production facilities to deteriorate. Results: When the exponential parameter for exponential distribution increases, the optimal production time increases. When Weibull distribution is considered, the optimal production time is closely affected by the shape parameter of Weibull distribution. Conclusion: A mathematical model is suggested to find optimal production time and optimal number of production cycles and numerical examples are implemented to validate the patterns for changes of optimal times under different parameters assumptions. The real application is implemented using the proposed approach.

• 제어로봇시스템학회논문지
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• 제8권10호
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• pp.833-841
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• 2002

We develope a model that can manipulate the reflux ratio of a batch distillation process in real time for optimal operations. Firstly, reflux ratio decision model for batch distillation unit was developed using the simple short-cut method. Secondly, more detailed rigorous method was applied to improve the accuracy of the model. Based on these models, operational strategies for the optimal reflux ratio was proposed. The results are illustrated with suitable examples and compared with the results using commercial simulator.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 추계 학술대회 학술발표 논문집
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• pp.95-98
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• 2003

In this paper, an adaptive fuzzy neural control of unknown nonlinear systems based on the rapid learning algorithm is proposed for optimal parameterization. We combine the advantages of fuzzy control and neural network techniques to develop an adaptive fuzzy control system for updating nonlinear parameters of controller. The Fuzzy Neural Network(FNN), which is constructed by an equivalent four-layer connectionist network, is able to learn to control a process by updating the membership functions. The free parameters of the AFN controller are adjusted on-line according to the control law and adaptive law for the purpose of controlling the plant track a given trajectory and it's initial values are off-line preprocessing, In order to improve the convergence of the learning process, we propose a rapid learning algorithm which combines the error back-propagation algorithm with Aitken's $\delta$$\^$2/ algorithm. The heart of this approach ls to reduce the computational burden during the FNN learning process and to improve convergence speed. The simulation results for nonlinear plant demonstrate the control effectiveness of the proposed system for optimal parameterization.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2309-2312
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• 2003

This paper presents the minimization of the energy costs based on energy saving for industrial retort process of canned food. The approved proposed method is related the optimal steam or hot water flow control to achieve desired temperature of retort process. The smooth response and zero steady state error can be also achieved. The performances of the proposed control technique were observed using a small tuna canned food plant in Thailand as an illustrative example. The experimental results are given to support the saving in energy costs and some benefits of the proposed technique.

• 한국인터넷방송통신학회논문지
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• 제10권6호
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• pp.47-53
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• 2010

본문은 단일 교차로의 주기시간을 결정하는 시간 간격을 계층분석방법(Analytic Hierachy Process)을 이용하여 도출하여 내었다. 주어진 불확실한 상황 가운데 결정적인 도출물을 얻기위해서는 비선형특성의 퍼지 적분이나 적응 계수를 사용하여 얻어내어야 하는데 이를 기반으로한 방법이 계층분석방법이다. 계층분석방법을 사용한 제어기와 고정시간 제어를 한 교통제어기와의 비교를 위하여 모의 실험을 통한 결과를 가지고서 비교 검토하였다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.156-161
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• 1994

In this paper, we consider an optimal control problem of a nonlinear stochastic system. Dynamic programming approach is employed for the formulation of a stochastic optimal control problem. As an optimality condition, dynamic programming equation so called the Bellman equation is obtained, which seldom yields an analytical solution, even very difficult to solve numerically. We obtain the numerical solution of the Bellman equation using an algorithm based on the finite difference approximation and the contraction mapping method. Optimal controls are constructed through the solution process of the Bellman equation. We also construct a test case in order to investigate the actual performance of the algorithm.

• 품질경영학회지
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• 제26권1호
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• pp.61-73
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• 1998

The quality control literature contains a substantial number of articles concerned with how to optimally choose control limits in order to minimize production cost. The purpose of the this study is to determine the economic setting for the process mean of an industrial process. In this study it is assumed that the lower control limit is set by government regulations and the u, pp.r limit and process mean are chosen based on economic considerations. Much research has been conducted on this problem under the condition of the fixed rpm(rate per minute). However a variance can be increased in proportion to the level of rpm and the increase of the variance can change the optimal process mean. Therefore, it is desirable to determine both the process mean and the level of rpm simultaneously. In this paper, a mathematical model is presented which considers the u, pp.r limit and the rpm as variables.

• Structural Engineering and Mechanics
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• 제38권1호
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• pp.39-63
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• 2011

Referring to the formulation of physical stochastic optimal control of structures and the scheme of optimal polynomial control, a nonlinear stochastic optimal control strategy is developed for a class of structural systems with hysteretic behaviors in the present paper. This control strategy provides an amenable approach to the classical stochastic optimal control strategies, bypasses the dilemma involved in It$\hat{o}$-type stochastic differential equations and is applicable to the dynamical systems driven by practical non-stationary and non-white random excitations, such as earthquake ground motions, strong winds and sea waves. The newly developed generalized optimal control policy is integrated in the nonlinear stochastic optimal control scheme so as to logically distribute the controllers and design their parameters associated with control gains. For illustrative purposes, the stochastic optimal controls of two base-excited multi-degree-of-freedom structural systems with hysteretic behavior in Clough bilinear model and Bouc-Wen differential model, respectively, are investigated. Numerical results reveal that a linear control with the 1st-order controller suffices even for the hysteretic structural systems when a control criterion in exceedance probability performance function for designing the weighting matrices is employed. This is practically meaningful due to the nonlinear controllers which may be associated with dynamical instabilities being saved. It is also noted that using the generalized optimal control policy, the maximum control effectiveness with the few number of control devices can be achieved, allowing for a desirable structural performance. It is remarked, meanwhile, that the response process and energy-dissipation behavior of the hysteretic structures are controlled to a certain extent.