• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1114-1116
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• 1999

This paper presents a new algorithm which evaluates production cost and reliability indices under various constraints of the thermal generation system. In order to consider the operational constraints of thermal units effectively, the proposed algorithm is based on Monte Carlo techniques instead of analytical ones which have difficulty in modelling the units with additional constraints. At that point, generating units are modelled into two types, base load units and peaking units. These generating unit models are used in state duration sampling simulation for which approach can readily consider the peaking unit operating cycles and easily calculates frequency-duration indices. The proposed production simulation algorithm is applied to the IEEE Reliability Test System, and performs the production simulation under the given constraints. The results show that the proposed algorithm is accurate, reliable and useful.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.9
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• pp.889-895
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• 2012

A vertical launching system(VLS) is a system for holding and firing missiles on surface ships. When a missile is launched in VLS, relative motion between canister and missile and drag force induced by wind can cause initial unstability of a missile. Thus dynamic analysis of initial behavior of vertically launched missile should be performed to prevent collision with any structure of a ship. In this study, dynamic analyses of initial behavior of vertically launched missile are performed using Monte-Carlo simulation, which relys on random sampling and probabilistic distribution of variables. Each parameter related with dynamic behavior of a missile is modeled with probability variables and Recurdyn, a commercial software for multi body dynamic analysis, is used to perform Monte-Carlo simulation. As a result, initial behavior of a missile is evaluated with respect to various performance indexes in a probabilistic sense and sensitivity of the each parameters is calculated.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.6
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• pp.720-727
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• 1995

A Monte Carlo computer simulation study was conducted to determine the most efficient sampling design for the blue crab dredge capture efficiency experiment performed in Chesapeake Bay, Maryland, U. S. A. The input values were the number of dredge tracks in each experimental area, the number of tows per experiment, the number of experiments, the mean density of crabs per unit area, the negative binomial coefficient, the gear capture efficiency, and the tow error. As a result of the study, a four-track experiment with twenty to twenty-eight tows was estimated to be the best in terms of precision and accuracy of the gear capture efficiency.

• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.305-313
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• 2002

Regarding to inference about a scalar measure of internal scatter of Ρ-variate normal population, this paper considers an interval estimation of the generalized variance, │$\Sigma$│. Due to complicate sampling distribution, fully parametric frequentist approach for the interval estimation is not available and thus Bayesian method is pursued to calculate the highest probability density (HPD) interval for the generalized variance. It is seen that the marginal posterior distribution of the generalized variance is intractable, and hence a weighted Monte Carlo method, a variant of Chen and Shao (1999) method, is developed to calculate the HPD interval of the generalized variance. Necessary theories involved in the method and computation are provided. Finally, a simulation study is given to illustrate and examine the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.1
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• pp.6-11
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• 2011

This paper proposes a new collision risk assessment system for pedestrians's safety. Monte Carlo Simulation (MCS) method is a one of the most popular method that rely on repeated random sampling to compute their result, and this method is also proper to get the results when it is unfeasible or impossible to compute an exact result. Nevertheless its advantages, it spends much time to calculate the result of some situation, we apply not only MCS but also Neural Networks in this problem. By Monte carlo method, we make some sample data for input of neural networks and by using this data, neural networks can be trained for computing collision probability of whole area where can be measured by sensors. By using this trained networks, we can estimate the collision probability at each positions and velocities with high speed and low error rate. Computer simulations will be shown the validity of our proposed method.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.53-63
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• 2013

This paper presents an importance sampling(IS) embedded experimental frame(EF) design for efficient Monte Carlo (MC) simulation. To achieve IS principles, the proposed EF contains two embedded sub-models, which are classified into Importance Sampler(IS) and Bias Compensator(BC) models. The IS and BC models stand between the existing system model and EF, which leads to enhancement of model reusability. Furthermore, the proposed EF enables to achieve fast stochastic simulation as compared with the crude MC technique. From the abstract two case studies with the utilization of the proposed EF, we can gain interesting experimental results regarding remarkable enhancement of simulation performance. Finally, we expect that this work will serve various content areas for enhancing simulation performance, and besides, it will be utilized as a tool to understand and analyze social phenomena.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1832-1842
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• 2014

A novel probabilistic small signal stability analysis (PSSSA) method considering load correlation is proposed in this paper. The superiority Latin hypercube sampling (LHS) technique combined with Monte Carlo simulation (MCS) is utilized to investigate the probabilistic small signal stability of power system in presence of load correlation. LHS helps to reduce the sampling size, meanwhile guarantees the accuracy and robustness of the solutions. The correlation coefficient matrix is adopted to represent the correlations between loads. Simulation results of the two-area, four-machine system prove that the proposed method is an efficient and robust sampling method. Simulation results of the 16-machine, 68-bus test system indicate that load correlation has a significant impact on the probabilistic analysis result of the critical oscillation mode under a certain degree of load uncertainty.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.21 no.5
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• pp.405-409
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• 2009

Reliability analysis of coastal structure using importance sampling was shown. When Monte Carlo simulation is used to evaluate overturng failure probability of coastal structure, very low failure probability leads to drastic increase in simulation time. However, importance sampling which uses randomly chosen design candidates around the failure surface makes it possible to analyze very low failure probability efficiently. In the numerical example, failure probability of caisson type quay wall was analyzed by using importance sampling and performance according to the level of failure probability was shown.

• Communications for Statistical Applications and Methods
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• v.14 no.1
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• pp.57-69
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• 2007

Adaptive cluster sampling design is known as a sampling method for rare clustered population. Three modified adaptive cluster sampling designs are proposed. The adjusted Hansen-Hurwitz estimator and the Horvitz-Thompson estimator are considered. Efficiency issue of the proposed sampling designs is discussed in a Monte-Carlo simulation study.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.151-161
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• 1997

An easy Monte Carlo Gibbs sampling approach is suggested for Bayesian analysis of cumulative logit models for ordinal polytomous data. Because in the cumulative logit model the posterior conditional distributions of parameters are not given in convenient forms for random sample generation, appropriate latent variables are introduced into the model so that in the new model all the conditional distributions are given in very convenient forms for implementation of the Gibbs sampler.