• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.6
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• pp.307-315
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• 2003

This paper proposes a novel simulation method of WPGS (Wind Power Generation System). The rotation speed control method of turbine under variable wind speed using the pitch control is proposed. Moreover, when wind speed exceeds the cut-out wind speed, the turbine will be stopped by controlling pitch angle to 90$^{\circ}$, otherwise it will be controlled to steady-state operation. For the purpose of effective simulation, the SWRW (Simulation method for WPGS using Real Weather condition) is used for the utility interactive WPGS simulation in this paper, in which those of three topics for the WPGS simulation: user-friendly method, applicability to grid-connection and the utilization of the real weather conditions, are satisfied. It is impossible to consider the real weather conditions in the WPGS simulation using the EMTP type of simulators and PSPICE, etc. External parameter of the real weather conditions is necessary to ensure the simulation accuracy. The simulation of the WPGS using the real weather conditions including components modeling of wind turbine system is achieved by introducing the interface method of a non-linear external parameter and FORTRAN using PSCAD/EMTDC in this paper. The simulation of long-term, short-term, over cut-out and under cut-out wind speeds will be peformed by the proposed simulation method effectively. The efficiency of wind power generator, power converter and flow of energy are analyzed by wind speed of the long-term simulation. The generator output and current supplied into utility can be obtained by the short-term simulation. Finally, transient-state of the WPGS can be analyzed by the simulation results of over cut-out and under cut-out wind speeds, respectively.

• Korean Journal of Food Science and Technology
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• v.38 no.4
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• pp.589-593
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• 2006

The objective of this study was to evaluate the use of fuzzy math strategy to calculate variability and uncertainty in quantitative risk assessment. We compared the propagation of uncertainty using fuzzy math simulation with Monte Carlo simulation. The risk far Listeria monocytogenes contamination was estimated for carcass and processed pork by fuzzy math and Monte Carlo simulations, respectively. The data used in these simulations were taken from a recent report on pork production. In carcass, the mean values for the risk from fuzzy math and Monte Carlo simulations were -4.393 log $CFU/cm^2$ and -4.589 log $CFU/cm^2$, respectively; in processed pork, they were -4.185 log $CFU/cm^2$ and -4.466 log $CFU/cm^2$ respectively. The distribution of values obtained using the fuzzy math simulation included all of the results obtained using the Monte Carlo simulation. Consequently, fuzzy math simulation was found to be a good alternative to Monte Carlo simulation in quantitative risk assessment of pork production.

• Journal of IKEEE
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• v.24 no.2
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• pp.426-434
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• 2020

FMI is being researched as a standard for linking large-scale simulation of CPS. In order to guarantee the reliability of the results in large-scale simulations using FMI, event handling through time management techniques is required. This paper aims to guarantee real-time performance and accuracy in large-scale co-simulation environments such as CPS. Synchronize the wallclock time and simulation time to ensure real time. Also, to ensure the accuracy, before the simulation, the event is checked and the simulation is performed with the smallest step size while maintaining the real time until the event occurrence time. As a result, the events occurring in the co-simulation environment are processed immediately and sequentially, ensuring the real-time performance and minimizing the numerical integration error by maximizing the simulation resolution. In the experiment, the proposed method was processed immediately, and it was confirmed that the numerical integration error is reduced by about 1/5 unlike the existing time management method which does not guarantee the resolution.

• Journal of KIISE
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• v.42 no.2
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• pp.213-219
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• 2015

Simulation is used these days to verify the hypothesis or the new technology. In particular, National Defense Modeling & Simulation (M&S) is used to predict wartime situation and conduct the military training. National Defense M&S can be divided into three parts, live simulation, virtual simulation, and constructive simulation. Live simulation is based on the real environment, which allows more realistic sumulation; however, it has decreased budget efficiency, but reduced depictions of reality. In contrast, virtual and constructive simulations which are based on the virtual environment, have increased budget efficiency, but reduced depictions of reality. Thus, if the three parts of the M&S are combined to make the L-V-C combined environment, the disadvantages of each simulation can be complemented to increases the quality of the simulation. In this paper, a method of interworking heterogeneous simulation middeware for L-V-C combined environment is proposed, and the test results of interworking between Data Distribution Service (DDS) and High Level Architecture (HLA) are shown.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.1-2
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• 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.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.06a
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• pp.0.1-0
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• 1994

• Proceedings of the Korea Society for Simulation Conference
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• 1997.04a
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• pp.9-9
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• 1997

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.5
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• pp.135-147
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• 2018

The objective of this study was to analyze the characteristics of combined 1D/2D inundation simulation of riverside farmland using the Hydrologic Engineering Center - River Analysis System (HEC-RAS). We compared and analyzed inundation simulation results between 1D and combined 1D/2D hydraulic simulation using HEC-RAS. Calibration and validation of stream stage were performed using three rainfall events. The coefficient of determination ($R^2$) and root mean square error (RMSE) between simulated and observed stream stage were 0.935 - 0.957 and 0.250 m - 0.283 m in calibration and validation, respectively. The inundation area showed no significant difference in 1D and combined 1D/2D simulation ($8.48km^2$ in 1D simulation, $8.75km^2$ in combined 1D/2D simulation). The average inundation depth by 1D simulation was 1.4 m deeper than combined 1D/2D simulation. In the lower inundation depth, the inundation area by combined 1D/2D simulation was larger than inundation area by 1D simulation. As the inundation depth increased, the inundation area by 1D simulation became wider. In the case of the 1D/2D combined simulation, low elevation areas along the river bank were inundated widely. Compared to 1D/2D combined simulation, the flood radius in some sections was longer in 1D simulation. In the 1D analysis, because the low altitude riverside farmlands are also assumed to stream, it is calculated that riverside farmlands have the same stage as the mainstream when the stream is overflowed. Therefore, the inundation area seems to be overestimated in those sections. In other regions, the inundation areas tend to be broken depending on overflow by each stream cross-section. In the case of river flooding, the overflow is expected to flow to the lower area depending on the terrain, such as the results of the combined 1D/2D simulation. It is concluded that the results of combined 1D/2D inundation simulation reflected the topographical characteristics of low-lying farmland.

• Journal of Korean Vacuum Science & Technology
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• v.6 no.1
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• pp.22-29
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• 2002

The process of energetic deposition has been simulated by using molecular dynamics (W) simulation and kinetic Monte Carlo (KMC) simulation. The atomistic mechanisms of film growth in energetic deposition are discussed.

• 대한약리학회:학술대회논문집
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• 2006.10a
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• pp.80.1-80.1
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• 2006