• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.11 no.7
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• pp.571-580
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• 1998

The numerical model that can describe the ignition of pseudospark discharge using hybrid fluid-particle(Monte Carlo )method has been developed. This model consists of the fluid expression for transport of electrons and ions and Poisson's equation in the electric field. The fluid equation determines the spatiotemporal dependence of charged particle densities and the ionization source term is computed using the Monte carlo method. This model has been used to study the evolution of a discharge in Argon at 0.5 torr, with an applied voltage if 1kV. The evolution process of the discharge has been divided into four phases along the potential distribution : (1) Townsend discharge, (2) plasma formation, (3) onset of hollow cathode effect, (4) plasma expansion. From the numerical results, the physical mechanisms that lead to the rapid rise in current associated with the onset of pseudospark could be identified.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.17 no.1 s.104
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• pp.39-44
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• 2006

This paper proposes a method of moments(MoM)/Monte-Carlo simulation and Physical Optics(PO) model to determine Bistatic Coherent Reflectivity of sea surfaces at various wind speeds. For the MoM simulation, a Gaussian random rough sea surface was generated based on the data of Tae-An ocean at various wind speeds and sea surface heights. The numerical results of the MoM/Monte Carlo simulations were used to verify the validity region of the PO model. It was found that the numerical result for a flat surface agrees quite well with the Fresnel reflection coefficient. The validity of the PO model on the rough sea surface is shown by using ray tracing method.

• Economic and Environmental Geology
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• v.50 no.3
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• pp.239-250
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• 2017

Physically based landslide susceptibility analysis has been recognized as an effective analysis method because it can consider the mechanism of landslide occurrence. The physically based analysis used the slope geometry and geotechnical properties of slope materials as input. However, when the physically based approach is adopted in regional scale area, the uncertainties were involved in the analysis procedure due to spatial variation and complex geological conditions, which causes inaccurate analysis results. Therefore, probabilistic method have been used to quantify these uncertainties. However, the uncertainties caused by lack of information are not dealt with the probabilistic analysis. Therefore, fuzzy set theory was adopted in this study because the fuzzy set theory is more effective to deal with uncertainties caused by lack of information. In addition, the vertex method and Monte Carlo simulation are coupled with the fuzzy approach. The proposed approach was used to evaluate the landslide susceptibility for a regional study area. In order to compare the analysis results of the proposed approach, Monte Carlo simulation as the probabilistic analysis and the deterministic analysis are used to analyze the landslide susceptibility for same study area. We found that Fuzzy Monte Carlo simulation showed the better prediction accuracy than the probabilistic analysis and the deterministic analysis.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.508-516
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• 1999

This paper presents a method for constructing composite power system effective load duration curves(CMELDC) at load points by Monte Carlo method. The concept of effective load duration curves(ELDC) in power system planning is useful and important in both HLII. CMELDC can be obtained from convolution integral processing of the probability function of unsupplied power and the load duration curve at each load point. This concept is analogy to the ELEC in HLI. And, the reliability indices (LOLP, EDNS) for composite power system are evaluated using CMELDC. Differences in reliability levels between HLI and HLII come from considering with the uncertainty associated with the outages of the transmission system. It is expected that the CMELDC can be applied usefully to areas such as reliability evaluation, probabilistic production cost simulation and analytical outage cost assessment, etc. in HLII, DC load flow and Monte Carlo method are used for this study. The characteristics and effectiveness of thes methodology are illustrated by a case study of the IEEE RTS.

• Journal of Ocean Engineering and Technology
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• v.34 no.5
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• pp.316-324
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• 2020

This paper presents comparative studies of reliability analysis and meta-modeling using the sampling method of Monte Carlo simulation for the structure design of an automatic ocean salt collector (AOSC). The thickness sizing variables of structure members are considered as random variables. Probabilistic performance functions are selected from strength performances evaluated via the finite element analysis of an AOSC. The sampling methods used in the comparative studies are simple random sampling and Sobol sequences with varied numbers of sampling. Approximation methods such as the Kriging model is applied to the meta-model generation. Reliability performances such as the probability failure and distribution are compared based on the variation of the sampling method of Monte Carlo simulation. The meta-modeling accuracy is evaluated for the Kriging model generated from the Monte Carlo simulation and Sobol sequence results. It is discovered that the Sobol sequence method is applicable to not only to the reliability analysis for the structural design of marine equipment such as the AOSC, but also to Kriging meta-modeling owing to its high numerical efficiency.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1988.05a
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• pp.56-59
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• 1988

Electron transport properties of gallium arsenide in an electric field are simulated the drift velocity, Mn.energy, electron occupation, mobility in the temperature range $77^{\circ}K-500^{\circ}K$ using a Monte Carlo Method. Therefore it can be used for a GaAs MESFET design.

• Nuclear Engineering and Technology
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• v.35 no.6
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• pp.581-595
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• 2003

Transport lattice codes require the resonance integral tables for the resonant nuclides where the resonance integral is a function of the background cross section and can be prepared through a special program solving the slowing down equation. In case the cross section libraries do not include the resonance integral table for the resonant nuclides, the computational prediction produces a large error. We devised a new method using a Monte Carlo calculation for the effective resonance cross sections to solve this problem provisionally. We extended this method to obtain the resonance integral table for general purpose. The MCNP code is used for the effective resonance integrals and the LIBERTE code for the effective background cross sections. We modified the HELIOS library with the effective cross sections and the resonance integral tables obtained by the newly developed Monte Carlo method, and performed sample calculations using HELIOS and LIBERTE. The results showed that this method is very effective for the resonance treatment.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.20 no.4
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• pp.277-293
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• 2016

We develop an efficient numerical method for pricing the Derivative Linked Securities (DLS). The payoff structure of the hybrid DLS consists with a standard 2-Star step-down type ELS and the range accrual product which depends on the number of days in the coupon period that the index stay within the pre-determined range. We assume that the 2-dimensional Geometric Brownian Motion (GBM) as the model of two equities and a no-arbitrage interest model (One-factor Hull and White interest rate model) as a model for the interest rate. In this study, we employ the Monte Carlo simulation method with the Compute Unified Device Architecture (CUDA) parallel computing as the General Purpose computing on Graphic Processing Unit (GPGPU) technology for fast and efficient numerical valuation of DLS. Comparing the Monte Carlo method with single CPU computation or MPI implementation, the result of Monte Carlo simulation with CUDA parallel computing produces higher performance.

• Journal of Applied Reliability
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• v.17 no.3
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• pp.188-196
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• 2017

Purpose: The purpose of this research is to determine optimal replacement age using non-informative prior information and Bayesian method. Methods: We propose a novel approach using Bayesian method to determine the optimal replacement age in block replacement policy by defining the prior probability with data on failure time and repair time. The Marcov Chain Monte Carlo simulation is used to investigate the asymptotic distribution of posterior parameters. Results: An optimal replacement age of block replacement policy is determined which minimizes cost and nonoperating time when no information on prior distribution of parameters is given. Conclusion: We find the posterior distribution of parameters when lack of information on prior distribution, so that the optimal replacement age which minimizes the total cost and maximizes the total values is determined.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.71-78
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• 2018

In this study, we consider the likelihood ratio test for the covariance matrix of the multivariate normal data. For this, we propose a method for obtaining null distributions of the likelihood ratio statistics by the Monte-Carlo approach when it is difficult to derive the exact null distributions theoretically. Then we compare the performance and precision of distributions obtained by the asymptotic normality and the Monte-Carlo method for the likelihood ratio test through a simulation study. Finally we discuss some interesting features related to the likelihood ratio test for the covariance matrix and the Monte-Carlo method for obtaining null distributions for the likelihood ratio statistics.