• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.225-228
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• 1992

The purpose of this project is the presentation of new method for selection of a scalar control of linear time-periodic system. The approach has been proposed by Radziszewski and Zaleski [4] and utilizes the quadratic form of Lyapunov function. The system under consideration is assigned either in closed-loop state or in modal variables as in Calico, Wiesel [1]. The case of scalar control is considered, the gain matrix being assumed to be at worst periodic with the system period T, each element being represented by a Fourier series. As the optimal gain matrix we consider the matrix ensuring the minimum value of the larger real part of the two Poincare exponents of the system. The method, based on two-step optimization procedure, allows to find the approximate optimal gain matrix. At present state of art determination of the gain matrix for this case has been done by systematic numerical search procedure, at each step of which the Floquet solution must be found.

• Journal of the military operations research society of Korea
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• v.25 no.2
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• pp.1-14
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• 1999

Metamodels using response surface methodology (RSM) are used for the optimality analysis of linear programming (LP). They have the form of a simple polynomial, and predict the optimal objective function value of an LP for various levels of the constraints. The metamodelling techniques for optimality analysis of LP can be applied to large-scale LP models. What is needed is some large-scale application of the techniques to verify how accurate they are. In this paper, we plan to use the large scale LP model, strategic transport optimal routing model (STORM). The developed metamodels of the large scale LP can provide some useful information.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.19 no.4
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• pp.417-428
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• 2015

Although, in general, the random fluctuation of interest rates gives a limited impact on portfolio optimization, their stochastic nature may exert a significant influence on the process of selecting the proportions of various assets to be held in a given portfolio when the stochastic volatility of risky assets is considered. The stochastic volatility covers a variety of known models to fit in with diverse economic environments. In this paper, an optimal strategy for portfolio selection as well as the smoothness properties of the relevant value function are studied with the dynamic programming method under a market model of both stochastic volatility and stochastic interest rates.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2018.05a
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• pp.72-73
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• 2018

The accuracy of contingency estimation plays an important role for dealing with the uncertainty of the financial success of construction project. Its' estimation may be used for various purposes such as schedule control, emergency resolve, and quality expense, etc. This paper presents a contingency estimation method which is schedule control specific. The method 1) implements stochastic EVMS, 2) detects a specific timing for schedule compression, 3) identifies an optimal strategy for shortening planned schedule, 4) finds a probability density function (PDF) of project cost overrun, and 5) estimates the optimal contingency cost based on the level of confidence. The method facilitates expeditious decisions involved in project budgeting. The validity of the method is confirmed by performing test case.

• Computational Structural Engineering
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• v.4 no.4
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• pp.91-96
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• 1991

This study focus on the development of optimization techniques to prestressed concrete box girder bridges erected in cantilever construction method. Optimization techniques are used to find optimal cross sections, i.e., those having minimum cost for such bridges. The problem is treated as an unconstrained nonlinear programming problem and the program has been developed to compute the objective function and optimal value. Numerical results are verified by the data of existing bridges.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.654-656
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• 1998

This paper presents an optimal identification method of nonlinear and complex system that is based on fuzzy-neural network(FNN). The FNN used simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM Algorithm to find initial parameters of membership function. And then to obtain optimal parameters, we use the genetic algorithm. Genetic algorithm is a random search algorithm which can find the global optimum without converging to local optimum. The parameters such as membership functions, learning rates and momentum coefficients are easily adjusted using the genetic algorithms. Also, the performance index with weighted value is introduced to achieve a meaningful balance between approximation and generalization abilities of the model. To evaluate the performance of the FNN, we use the time series data for 9as furnace and the sewage treatment process.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.5
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• pp.661-666
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• 2015

This paper proposes a method for solving running cost problem by minimizing switching reactive power compensation devices. An objective function was modeled by calculating the weighting value of cost, and a solution was derived using ILP. This paper suggests optimal coordinative control method between FACTS, Shunt Reactors, Capacitors and OLTC. Therefore, it is valuable for decision maker in determining order and capacity of devices which gaining a voltage stabilization. As a result, the objectives of voltage stabilization and cost minimization were achieved simultaneously. This realizes the economic efficiency of the system. We start by showing how to solve systems of linear equations using the language of pivots and tableaus. The effectiveness of this technique is demonstrated in modified PSS/E MIGUM 45 bus system. The simulation results show the effectiveness of this algorithm by comparing the outcome withseveral established methods.

• Journal of Korea Society of Industrial Information Systems
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• v.10 no.3
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• pp.21-29
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• 2005

The most of automated negotiation systems are dependent upon negotiators' offers, as negotiation is going on. Particularly, the preference, evaluation function and negotiation strategy are variously changed at every negotiation round by the negotiator and have an effect on the counter offers. Therefore, this study proposed the automated negotiation methodology or negotiation model which makes the negotiator's participation minimize. To minimize negotiator's participation, the preference of negotiator was predicted by the ratio of seller and buyer's count offers and the evaluation function of negotiator was also predicted by least squares approximation method at every negotiation round. The predicted evaluation function was evaluated and selected by $R^2$ value, coefficient of determination. Finally the optimal counter offers were generated by the genetic algorithm using the predicted preference and value function.

• Journal of Korea Water Resources Association
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• v.32 no.6
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• pp.731-740
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• 1999

Autocorrelation function is widely used as a tool measuring linear dependence of hydrologic time series. However, it may not be appropriate for choosing decorrelation time or delay time ${\tau}_d$ which is essential in nonlinear dynamics domain and the mutual information have recommended for measuring nonlinear dependence of time series. Furthermore, some researchers have suggested that one should not choose a fixed delay time ${\tau}_d$ but, rather, one should choose an appropriate value for the delay time window ${\tau}_d={\tau}(m-1)$, which is the total time spanned by the components of each embedded point for the analysis of chaotic dynamics. Unfortunately, the delay time window cannot be estimated using the autocorrelation function or the mutual information. Basically, the delay time window is the optimal time for independence of time series and the delay time is the first locally optimal time. In this study, we estimate general dependence of hydrologic time series using the C-C method which can estimate both the delay time and the delay time window and the results may give us whether hydrologic time series depends on its linear or nonlinear characteristics which are very important for modeling and forecasting of underlying system.

• Fire Science and Engineering
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• v.29 no.5
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• pp.51-56
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• 2015

In this study, optimal design methods were applied to the agent discharge flow of clean agent fire extinguishing systems. The methods combined optimal design theory and engineering theory for engineering analysis in a design program or coast savings in value engineering. Optimal design parameters were determined to optimize the agent discharge flow based on the design theory of the clean agent fire extinguishing systems and the theory of optimal design. The design factors were verified in regard to suitability for the performance of fire extinguishing systems. The results provide a foundation for optimal design method methods in other fire extinguishing systems. Optimization of the agent discharge flow of the discharge nozzle was confirmed by the constraints on the inner diameter of the discharge nozzle and the pipe, agent arrival time, flow, and pressure variation of the agent. The deviation of discharge pressure and agent time of the agent discharge nozzle were found to correlate with the pressure variation.