• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.1
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• pp.61-83
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• 2009

This study developed a variable demand traffic assignment model by stable dynamics. Stable dynamics, suggested by Nesterov and do Palma[19], is a new model which describes and provides a stable state of congestion in urban transportation networks. In comparison with the user equilibrium model, which is based on the arc travel time function in analyzing transportation networks, stable dynamics requires few parameters and is coincident with intuitions and observations on congestion. It is therefore expected to be a useful analysis tool for transportation planners. In this study, we generalize the stable dynamics into the model with variable demands. We suggest a three stage optimization model. In the first stage, we introduce critical travel times and dummy links and determine variable demands and link flows by applying an optimization problem to an extended network with the dummy links. Then we determine link travel times and path flows in the following stages. We present a numerical example of the application of the model to a given network.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.284-290
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• 1993

In order to improve the adaptability of rice transplanter to seeding with different length when transplanting multicropping rice in south China. The seedling separating planting mechanism is resynthesized in the paper. According to the agronomy requirements of seedling's transplanting, optimum motional path of the tip point of planting needle is obtained. by applying the established kinematic model of the separating planting mechanism, the relevant software is compiled. On the basis of the features of the problem, the constrained optimization method is utilized to solve the problem with 24 restrictions. Thus, the optimum structure parameters are obtained to satisfy the path points accurately.

• Architectural research
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• v.11 no.2
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• pp.19-26
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• 2009

The effect of rebirthing technique on the genetic algorithm (GA)-based size optimization is investigated. The GA mimics the principles of nature and it can gradually improve structural design through biological operations such as fitness, selection, crossover and mutation. However, premature optimum has been often detected in the generic GA with continuous design variable. Since then, the so-called rebirthing technique has been proposed to avoid this problem. However, the performance of the rebirthing technique has not been reported. Therefore, the size optimizations of spatial structures are tackled to investigate the performance of the rebirthing technique on the generic GA. From numerical results, it is well proved that the rebirthing technique is very effective to produce the optimum values regardless of the values of parameters used in the GA operations.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.3
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• pp.24-29
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• 2009

In case of the optimization problems that have many design variables, the conventional genetic algorithms(GA) fall into a trap of local minima with high probability. This problem is called the premature convergence problem. To overcome it, the parallel genetic algorithms which adopt the migration mechanism have been suggested. But it is hard to determine the several parameters such as the migration size and the migration interval for the parallel GAs. Therefore, we propose a new method to determine the migration interval automatically in this paper. To verify its validity, it is applied to some traditional mathematical optimization problems and is compared with the conventional parallel GA. It is also applied to the optimal design of the brushless DC motor for an electric wheel chair which is a real world problem and has five design variables.

• Microbiology and Biotechnology Letters
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• v.17 no.6
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• pp.619-623
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• 1989

The effects of temperature on yeast growth and ethanol production were investigated in batch cultures. The maximum specific growth rate of yeast was obtained at 36$^{\circ}C$ and the maximum specific production rate of ethanol was obtained at 33$^{\circ}C$. A mathematical model was employed to describe the temperature effects in ethanol fermentation and the parameters in the model were expressed as a function of temperature. Optimum temperature control strategy, from the simulation result, consists of starting the fermentation at high temperature and lowering the temperature as the fermentation proceeds.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.346-357
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• 2010

Opportunistic scheduling provides the capability of resource management in wireless networks by taking advantage of multiuser diversity and by allowing delay variation in delivering data packets. It generally aims to maximize system throughput or guarantee fairness and quality of service (QoS) requirements. In this paper, we develop an extended proportional fair (PF) scheduling policy that can statistically guarantee three kinds of QoS. The scheduling policy is derived by solving the optimization problems in an ideal system according to QoS constraints. We prove that the practical version of the scheduling policy is optimal in opportunistic scheduling systems. As each scheduling policy has some parameters, we also consider practical parameter adaptation algorithms that require low implementation complexity and show their convergences mathematically. Through simulations, we confirm that our proposed schedulers show good fairness performance in addition to guaranteeing each user's QoS requirements.

• Journal of the Korea Society for Simulation
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• v.5 no.1
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• pp.91-106
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• 1996

Control Variate (CV) is very useful technique for variance reduction in a wide class of queueing network simulations. However, the loss in variance reduction caused by the estimation of the optimum control coefficients is an increasing function of the number of control variables. Therefore, in some situations, it is required to select an optimal set of control variables to maximize the variance reduction . In this paper, we develop the Adaptive Control Variates (ACV) method which selects an optimal set of control variates during the simulation adatively. ACV is useful to maximize the simulation efficiency when we need iterated simulations to find an optimal solution. One such an example is the Simulated Annealing (SA) because, in SA algorithm, we have to repeat in calculating the objective function values at each temperature, The ACV can also be applied to the queueing network optimization problems to find an optimal input parameters (such as service rates) to maximize the throughput rate with a certain cost constraint.

• Journal of Korean Society on Water Environment
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• v.25 no.1
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• pp.96-101
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• 2009

The aim of this research was to apply experimental design methodology to optimizetion the photolytic degradation of N-nitrosodimethylamine (NDMA). Reactions were mathematically described as a function of parameters such as pH, initial NDMA concentration, and UV intensity using the Box-Behnken method. The results showed that the responses of NDMA removal (%) in photolysis were significantly affected by the synergistic effect of linear term of pH, initial NDMA concentration and UV intensity. The application of Response Surfase Methodology (RSM) using the Box-Behnken method yielded the following regression equation, which is an empirical relationship between the removal (%) of NDMA and test variables in coded unit: Y = 50.929 + 16.073(UV) - 7.909(NDMA) - 27.432(pH) - 11.385(UV)(NDMA) - 7.363(UV)(pH) +13.811(NDMA)(pH). The model predictions agreed well with the experimentally observed result ($R_2(ad.)=89%$).

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.960-963
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• 1996

In this paper, the optimization of a fuzzy controller using genetic algorithm is studied. The fuzzy controller has been widely applied to industries because it is highly flexible, robust easy to implement and suitable for complex systems. Generally, the design of fuzzy controller has difficulties in determining the structure of the rules and the membership functions. To solve these problems, the proposed method optimizes the structure of fuzzy rules and the parameters of membership functions simultaneously in an off-line method. The proposed method is evaluated through computer simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.8
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• pp.649-654
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• 2002

The essential philosophy of the QFT(Quantitative Feedback Theory) is that a suitable controller can be found by loop shaping a nominal loop transfer function such that the frequency response of this function does not violate the QFT bounds. The loop shaping synthesis involves the identification of a structure and its specialization by means of the parameter optimization. This paper presents an optimization algorithm to estimate the controller parameters from the frequency transfer function synthesis using the TLS(Total Least Squares) in the QFT loop shaping procedure. The proposed method identifies the parameter vector of the robust controller from an overdetermined linear system developed from rearranging the two dimensional system matrices and output vectors obtained from the QFT bounds. The feasibility of the suggested algorithm is illustrated with an example.