• Journal of the Korean Geotechnical Society
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• v.23 no.11
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• pp.109-117
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• 2007

Uncertainty is pervasive in rock slope stability analysis due to various reasons and subsequently it may cause serious rock slope failures. Therefore, the importance of uncertainty has been recognized and subsequently the probability theory has been used to quantify the uncertainty since 1980's. However, some uncertainties, due to incomplete information, cannot be handled satisfactorily in the probability theory and the fuzzy set theory is more appropriate for those uncertainties. In this study the random variable is considered as fuzzy number and the fuzzy set theory is employed in rock slope stability analysis. However, the previous fuzzy analysis employed the approximate method, which is first order second moment method and point estimate method. Since previous studies used only the representative values from membership function to evaluate the stability of rock slope, the approximated analysis results have been obtained in previous studies. Therefore, the Monte Carlo simulation technique is utilized to evaluate the probability of failure for rock slope in the current study. This overcomes the shortcomings of previous studies, which are employed vertex method. With Monte Carlo simulation technique, more complete analysis results can be secured in the proposed method. The proposed method has been applied to the practical example. According to the analysis results, the probabilities of failure obtained from the fuzzy Monte Carlo simulation coincide with the probabilities of failure from the probabilistic analysis.

• Proceedings of the Korean Nuclear Society Conference
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• 1999.05a
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• pp.69-69
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• 1999

• Proceedings of the Optical Society of Korea Conference
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• 2002.11a
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• pp.16-17
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• 2002

• Journal of the Korean Statistical Society
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• v.36 no.3
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• pp.435-445
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• 2007

This paper addresses for the first time the use of complete data information criteria in unsupervised learning of the Naive-Bayes model. A Monte Carlo study sets a large experimental design to assess these criteria, unusual in the Bayesian network literature. The simulation results show that complete data information criteria underperforms the Bayesian information criterion (BIC) for these Bayesian networks.

• Macromolecular Research
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• v.11 no.5
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• pp.393-397
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• 2003

By applying a configurational-bias Gibbs ensemble Monte Carlo algorithm, priority simulation results regarding the conformation of non-dilute polyelectrolytes in solvents are obtained. Solutions of freely-jointed chains are considered, and a new method termed strandwise configurational-bias sampling is developed so as to effectively overcome a difficulty on the transfer of polymer chains. The structure factors of polyelectrolytes in the bulk as well as in the confined space are estimated with variations of the polymer charge density.

• Journal of Biomedical Engineering Research
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• v.20 no.2
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• pp.199-204
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• 1999

As the correct measuring of the light dosimetry in biological tissues give the important affection to the effect of PDT treatment we used Monte Carlo simulation to measure the light dosimetry on this study. The parameters using in experiments are the optical properties of the real biological tissue, and we used Henyey-Greenstein phase function among the phase functions. As we results, we displayed the result the change of Fluence rate and the difference against the previous theory was at least 0.35%. Biological tissues using in experiment were Human tissue, pig tissue, rat liver tissue and rabbit muscle tissue. The most of biological tissue have big scattering coefficient in visible wavelength which influences penetration depth. The penetration depth of human tissue in visible region is 1.5~2cm. We showed that it is possible to measure fluence rate and penetration depth within the biological tissues by Monte Carlo simulation very well.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.7
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• pp.657-664
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• 2011

This study proposes an inverse method for estimating the boundary temperature in a gas-filled, onedimensional parallel domain enclosed by parallel plates. The distance between the plates is considered submicron to one mm. In the current method, it is assumed that the conditions of both heat flux and temperature are simultaneously applicable to one boundary, while no conditions are applicable to the other boundary The temperature on one of the boundaries should be inversely determined from the known temperature and heat flux on the other boundary. This study proposes a procedure for estimating the unknown boundary temperature through Monte Carlo simulation. Both the forward and inverse problems employ the Monte Carlo approach. The forward (direct) problem is solved by using the direct simulation Monte Carlo while the inverse solution is obtained by the simulated annealing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.4
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• pp.743-747
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• 2007

There has been steady effect for the development of the electron-beam lithography technologies for the circuit patterning of the future semiconductor devices. In this study, we have performed a Monte-Carlo simulation whore $1{\times}10^4$ electrons with various kinetic energies (100eV, 300eV, 500eV, 700eV, and 1000eV) were shot into polymethyl methacrylate(PMMA) resist of 100-nm thickness. The penetration depth of each electron beam in the resist layer were analyzed using Gaussian analysis method.

• 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.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.790-802
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• 2022

Large-scale reactor simulation often requires the use of Monte Carlo calculation techniques to estimate important reactor parameters. One drawback of these Monte Carlo calculation techniques is they inevitably result in some uncertainty in calculated quantities. The present study includes parametric uncertainty quantification (UQ) and sensitivity analysis (SA) on the Advanced Test Reactor Critical (ATRC) facility housed at Idaho National Laboratory (INL) and addresses some complications due to Monte Carlo uncertainty when performing these analyses. This approach for UQ/SA includes consideration of Monte Carlo code uncertainty in computed sensitivities, consideration of uncertainty from directly measured parameters and a comparison of results obtained from brute-force Monte Carlo UQ versus UQ obtained from a surrogate model. These methodologies are applied to the uncertainty and sensitivity of k_eff for two sets of uncertain parameters involving fuel plate geometry and fuel plate composition. Results indicate that the less computationally-expensive method for uncertainty quantification involving a linear surrogate model provides accurate estimations for k_eff uncertainty and the Monte Carlo uncertainty in calculated k_eff values can have a large effect on computed linear model parameters for parameters with low influence on k_eff.