• 한국시뮬레이션학회:학술대회논문집
• /
• 한국시뮬레이션학회 1994년도 추계학술발표회 및 정기총회
• /
• pp.1-2
• /
• 1994

With the prevalence of computers in modern organizations, simulation is receiving more atention as an effectvie decision -making tool. Simualtion is a computer-based numerical technique which uses mathmatical and logical models to approximate the behaviror of a real-world system. However, iptimization of synamic stochastic systems often defy analytical and algorithmic soluions. Although a simulation approach is often free fo the liminting assumption s of mathematical modeling, cost and time consiceration s make simulation the henayst's last resort. Therefore, whenever possible, analytical and algorithmica solutions are favored over simulation. This paper discussed the issues and procedrues for using simulation as a tool for optimization of stochastic complex systems that are dmodeled by computer simulation . Its emphasis is mostly on issues that are speicific to simulation optimization instead of consentrating on the general optimizationand mathematical programming techniques . A simulation optimization problem is an optimization problem where the objective function. constraints, or both are response that can only be evauated by computer simulation. As such, these functions are only implicit functions of decision parameters of the system, and often stochastic in nature as well. Most of optimization techniqes can be classified as single or multiple-resoneses techniques . The optimization of single response functins has been researched extensively and consists of many techniques. In the single response category, these strategies are gradient based search techniques, stochastic approximate techniques, response surface techniques, and heuristic search techniques. In the multiple response categroy, there are basically five distinct strategies for treating the responses and finding the optimum solution. These strategies are graphica techniqes, direct search techniques, constrained optimization techniques, unconstrained optimization techniques, and goal programming techniques. The choice of theprocedreu to employ in simulation optimization depends on the analyst and the problem to be solved. For many practival and industrial optimization problems where some or all of the system components are stochastic, the objective functions cannot be represented analytically. Therefore, modeling by computersimulation is one of the most effective means of studying such complex systems. In this paper, after discussion of simulation optmization techniques, the applications of above techniques will be presented in the modeling process of many flexible manufacturing systems.

• 한국지능시스템학회논문지
• /
• 제12권6호
• /
• pp.577-582
• /
• 2002

Neural network can be trained to approximate an arbitrary nonlinear function of multivariate data like the mini-mill crown values in Automatic Shape Control. The trained weights of neural network can evaluate or generalize the process data outside the training vectors. Sometimes, the blind modeling of the process data is necessary to compare with the scattered analytical model of mini-mill process in isolated electro-mechanical forms. To come up with a viable model, we propose the blind neural-based range-division domain-clustering piecewise-linear modeling scheme. The basic ideas are: 1) dividing the range of target data, 2) clustering the corresponding input space vectors, 3)training the neural network with clustered prototypes to smooth out the convergence and 4) solving the resulting matrix equations with a pseudo-inverse to alleviate the ill-conditioning problem. The simulation results support the effectiveness of the proposed scheme and it opens a new way to the data analysis technique. By the comparison with the statistical regression, it is evident that the proposed scheme obtains better modeling error uniformity and reduces the magnitudes of errors considerably. Approximatly 10-fold better performance results.

• 한국해양공학회지
• /
• 제35권2호
• /
• pp.141-149
• /
• 2021

The A60 class deck penetration piece is a fire-resistance apparatus installed on the deck compartment to protect lives and to prevent flame diffusion in the case of a fire accident in a ship or offshore plant. In this study, the sensitivity of the fire-resistance performance and approximation characteristics for the A60 class penetration piece was evaluated by conducting a transient heat-transfer analysis and fire test. The transient heat-transfer analysis was conducted to evaluate the fire-resistance design of the A60 class deck penetration piece, and the analysis results were verified via the fire test. The penetration-piece length, diameter, material type, and insulation density were used as the design factors (DFs), and the output responses were the weight, temperature, cost, and productivity. The quantitative effects of each DF on the output responses were evaluated using the design-of-experiments method. Additionally, an optimum design case was identified to minimize the weight of the A60 class deck penetration piece while satisfying the allowable limits of the output responses. According to the design-of-experiments results, various approximate models, e.g., a Kriging model, the response surface method, and a radial basis function-based neural network (RBFN), were generated. The design-of-experiments results were verified by the approximation results. It was concluded that among the approximate models, the RBFN was able to explore the design space of the A60 class deck penetration piece with the highest accuracy.

• Journal of Information Technology Applications and Management
• /
• 제11권1호
• /
• pp.117-135
• /
• 2004

Since human decision making behavior is likely to follow nonlinear strategy, it is conjectured that the human decision making behavior can be modeled better by nonlinear models than by linear models. All that linear models can do is to approximate rather than model the decision behavior. This study attempts to test this conjecture by analyzing human decision making behavior and combining the results of the analysis with predictive performance of both linear models and nonlinear models. In this way, this study can examine the relationship between the predictive performance of models and the existence of valid nonlinear strategy in decision making behavior. This study finds that the existence of nonlinear strategy in decision making behavior is highly correlated with the validity of the decision (or the human experts). The second finding concerns the significant correlations between the model performance and the existence of valid nonlinear strategy which is detected by Lens Model. The third finding is that as stronger the valid nonlinear strategy becomes, the better nonlinear models predict significantly than linear models. The results of this study bring an important concept, validity of nonlinear strategy, to modeling human experts. The inclusion of the concept indicates that the prior analysis of human judgement may lead to the selection of proper modeling algorithm. In addition, lens Model Analysis is proved to be useful in examining the valid nonlinearity in human decision behavior.

• 산업경영시스템학회지
• /
• 제35권3호
• /
• pp.52-61
• /
• 2012

The design method for cumulative sum (CUSUM) control charts, which can be robust to autoregressive moving average (ARMA) modeling errors, has not been frequently proposed so far. This is because the CUSUM statistic involves a maximum function, which is intractable in mathematical derivations, and thus any modification on the statistic can not be favorably made. We propose residual-based robust CUSUM control charts for monitoring autocorrelated processes. In order to incorporate the effects of ARMA modeling errors into the design method, we modify parameters (reference value and decision interval) of CUSUM control charts using the approximate expected variance of residuals generated in model uncertainty, rather than directly modify the form of the CUSUM statistic. The expected variance of residuals is derived using a second-order Taylor approximation and the general form is represented using the order of ARMA models with the sample size for ARMA modeling. Based on the Monte carlo simulation, we demonstrate that the proposed method can be effectively used for statistical process control (SPC) charts, which are robust to ARMA modeling errors.

• ETRI Journal
• /
• 제39권5호
• /
• pp.718-728
• /
• 2017

Empirical modeling of wireless fading channels using common schemes such as autoregression and the finite state Markov chain (FSMC) is investigated. The conceptual background of both channel structures and the establishment of their mutual dependence in a confined manner are presented. The novel contribution lies in the proposal of a new approach for deriving the state transition probabilities borrowed from economic disciplines, which has not been studied so far with respect to the modeling of FSMC wireless fading channels. The proposed approach is based on equal portioning of the received signal-to-noise ratio, realized by using an alternative probability construction that was initially highlighted by Tauchen. The associated statistical procedure shows that a first-order FSMC with a limited number of channel states can satisfactorily approximate fading. The computational overheads of the proposed technique are analyzed and proven to be less demanding compared to the conventional FSMC approach based on the level crossing rate. Simulations confirm the analytical results and promising performance of the new channel model based on the Tauchen approach without extra complexity costs.

• Journal of the Optical Society of Korea
• /
• 제7권4호
• /
• pp.258-263
• /
• 2003

Simple algebraic expressions are derived to approximate the optimal input RMS pulse width and the resulting output RMS pulse width in single-mode fibers. The results are compared with the previously published methods and with numerical results by the split-step Fourier method. In addition, for a transform-limited Gaussian input pulse, it is shown that there is no optimum input pulse width to minimize the output spectrum width. Finally, with fiber nonlinearity, it is shown mathematically that there is not an optimum input pulse width to minimize the product,${\sigma}_t{\sigma}_{\omega}$, which is inversely proportional to the transmission capacity of WDM systems.

• 에너지공학
• /
• 제9권4호
• /
• pp.309-318
• /
• 2000

핀-관 열교환기의 효율을 증대시키기 위하여는 열저항을 결정하는 데 있어서 중요한 역할을 하는 공기측 열전달 특성의 향상이 필요하다. 본 연구에서는 핀-관 열교환기의 공리측 성능을 해석하기 위해서 3차원 비압축성 Navier-Stokes 코드를 개발하였으며 이 코드는 시간항에 스칼라 내재적 근사분해법(scalar implicit approximate factorization)절차, 공간항에 유한체적법과 2차의 풍상차분법(upwind differencing)을 사용한다. 서로 다른 3개의 핀형상(평판핀, 슬릿핀, 파형핀)을 고려하였고 이들의 유동 및 열전달 특성을 연구하였다.

• Advances in Energy Research
• /
• 제1권1호
• /
• pp.35-52
• /
• 2013

A model for DMFC anode performance is developed. The model takes into account potential--independent methanol adsorption on the catalyst surface, finite rate of proton transport through the anode catalyst layer (ACL), and a potential loss due to methanol transport in the anode backing layer. An approximate analytical half--cell polarization curve is derived and equations for the anode limiting current density are obtained. The polarization curve is fitted to the curves measured by Nordlund and Lindbergh and parameters resulted from the fitting are discussed.

• Nuclear Engineering and Technology
• /
• 제42권3호
• /
• pp.271-278
• /
• 2010

While the deterministic lattice physics/depletion codes use leakage-corrected critical spectrum (although approximate due to the B1 buckling search employed), Monte Carlo depletion codes currently in use do not have such a feature in spite of their heterogeneity and continuous-energy modeling capability. This paper describes an approach to Monte Carlo depletion with leakage-corrected critical spectrum derived from first principles. This is based on the concept of albedo eigenvalue treated as weight of the reflected neutron in Monte Carlo simulation.