• Proceedings of the Korea Society for Simulation Conference
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• 2001.05a
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• pp.169-169
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• 2001

With the aid of fast computing power, resampling techniques are being introduced for simulation output analysis (SOA). Autocorrelation among the output from discrete-event simulation prohibit the direct application of resampling schemes (Threshold bootstrap, Binary bootstrap, Stationary bootstrap, etc) extend its usage to time-series data such as simulation output. We present a new method for inference from a regenerative process, regenerative bootstrap, that equals or exceeds the performance of classical regenerative method and approximation regeneration techniques. Regenerative bootstrap saves computation time and overcomes the problem of scarce regeneration cycles. Computational results are provided using M/M/1 model.

• Proceedings of the Korea Society for Simulation Conference
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• 1994.10a
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• pp.12-12
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• 1994

Simulation output knowledge analysis is one of problem-solving and/or knowledge adquistion process by investgating the system behavior under study through simulation . This paper describes an approach to simulation outputknowldege analysis using fuzzy neural network model. A fuzzy neral network model is designed with fuzzy setsand membership functions for variables of simulation model. The relationship between input parameters and output performances of simulation model is captured as system behavior knowlege in a fuzzy neural networkmodel by training examples form simulation exepreiments. Backpropagation learning algorithms is used to encode the knowledge. The knowledge is utilized to solve problem through simulation such as system performance prodiction and goal-directed analysis. For explicit knowledge acquisition, production rules are extracted from the implicit neural network knowledge. These rules may assit in explaining the simulation results and providing knowledge base for an expert system. This approach thus enablesboth symbolic and numeric reasoning to solve problem througth simulation . We applied this approach to the design problem of broadband communication network.

• Journal of the Korea Society for Simulation
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• v.27 no.3
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• pp.65-73
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• 2018

In this paper, we propose a simulation output analysis environment using Elastic Stack technology in order to reduce the complexity of the simulation analysis process. The proposed simulation output analysis environment automatically transfers simulation outputs to a centralized analysis server from a set of simulation execution resources, physically separated over a network, manages the collected simulation outputs in a fashion that further analysis tasks can be easily performed, and provides a connection to analysis and visualization services of Kibana in Elastic Stack. The proposed analysis environment provides scalability where a set of computation resources can be added on demand. We demonstrate how the proposed simulation output analysis environment can perform the simulation output analysis effectively with an example of spreading epidemic diseases, such as influenza and flu.

• Journal of Korean Institute of Industrial Engineers
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• v.20 no.4
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• pp.73-84
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• 1994

Output analysis is one of the most important fields of simulation to achieve the accurate simulation results. This study shows how to analyze simulation output data in the steady state using Procrustes analysis technique which has not been used in the field of simulation yet. In this paper Procrustes analysis method is used to perform the analysis of simulation output efficiently and effectively by applying the improved version of the method. The experiments are conducted using M/M/1 queueing simulation model. The results obtained by Procrustes analysis method show better estimates for average waiting times and average queue lengths which are closer to true values and narrower confidence intervals than when replication-deletion method is used. Also it requires the smaller number of simulation runs.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.437-450
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• 1994

The problem of testing for a change in the parameter of a stochastic process is particularly important in simulation studies. In studies of the steady state characteristics of a simulation model, it is important to identify initialization bias and to evaluate efforts to control this problem. A simulation output have the characteristics of chaotic behavior because of sensitive dependence on initial conditions. For that reason, we will apply Lyapunov exponent for diagnosis of chaotic motion to simulation output analysis. This paper proposes two methods for diagnosis of steady state in simulation output. In order to evaluate the performance and effectiveness of these methods using chaos theory, M/M/I(.inf.) queueing model is used for testing point estimator, average bias.

• Journal of Power System Engineering
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• v.13 no.6
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• pp.110-116
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• 2009

In the present study, we proposed a simulation model of vacuum booster with AMESIM software to predict the output characteristic. And we performed the sensitivity analysis of output characteristic with main design parameters, such as diaphragm diameter. All of these parameters are main design parameters in the procedure of vacuum booster design. The simulation results of this paper offer qualitative information of vacuum booster output. Therefore, the simulation results of this paper will be used effectively for the design procedure of vacuum booster in the industrial field.

• Journal of the Korea Society for Simulation
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• v.7 no.1
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• pp.27-37
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• 1998

Simulation output is generally stochastic and autocorrelated, and includes the initial condition bias. To exclude the bias, the determination of truncation point has been one of important issues for the steady-state simulation output analysis. In this paper, two methods are presented for detection of truncation point in order to estimate efficiently the steady-state measure of simulation output. They are based on the Euclidean distance equation, and the backpropagation algorithm in Neural Networks. The experimental results obtained by M/M/1 and M/M/2 show that the proposed methods are very promising with respect to coverage and relative bias. The methods could be used for the on-line analysis of simulation outputs.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.72-76
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• 2001

Experimental design in simulation provides an efficient way of economizing simulation runs since a considerable number of simulation runs that originally were planned can be reduced by this approach. This experimental design method is an active area of research together with the output analysis and so no single panacea seems to exist so far. Thus, selection of techniques of experimental design and output analysis more lithely depends upon the objective of simulation analysis, budget constraint and sometimes the analysts subjective judgment. This paper attempts to describe an experimental design methodology for port investment analysis using a case study in a bulk terminal in Korea. Detailed display will be focused on simulation period, warm-up period, the number of replications needed in production runs after brief explanation on the system configuration.

• Journal of the Korea Society for Simulation
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• v.11 no.3
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• pp.1-12
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• 2002

Experimental design in simulation provides an efficient way of economizing simulation runs since a considerable number of simulation runs that originally were planned can be reduced by this approach. This experimental design method is an active area of research together with the output analysis and so no single panacea seems to exist so far. Thus, selection of techniques of experimental design and output analysis more likely depends upon the objective of simulation analysis, budget constraint and sometimes the analyst's subjective judgment. This paper attempts to describe an experimental design methodology for port investment analysis using a case study in a bulk terminal in Korea. Detailed display will be focused on simulation period, warm-up period, the number of replications needed in production runs after brief explanation on the system configuration.

• Journal of the Korea Society for Simulation
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• v.21 no.3
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• pp.11-16
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• 2012

Simulation is the imitation of the operation of a real-world process or system over time. It concerns the study of the operating characteristics of real systems. Typically, a simulation project consists of several steps such as data collection, coding, model verification, model validation, experimental design, output data analysis, and implementation. Among these steps of a simulation study this paper focus on statistical analysis methods of simulation output data. Specially, we explain how to develop confidence interval estimators for mean ${\mu}$ in terminating and non-terminating simulation cases. We, then, explore the estimation techniques for $f({\mu})$, where the function $f({\bullet})$ is a nonlinear that is continuously differentiable in a neighborhood of ${\mu}$ with $f'({\mu}){\neq}0$.