• 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.

• Journal of Korea Water Resources Association
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• v.40 no.2 s.175
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• pp.171-182
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• 2007

In this study, as a method for decreasing the confidence interval of the estimates of Clark hydrograph's concentration time and storage coefficient, regression equations of these parameters with respect to those of rainfall, meteorology, and basin characteristics are derived and analyzed using the Monte Carlo simulation technique. The results are also reviewed by comparing them with those derived by applying the Bootstrap technique and empirical equations. Results derived from this research are summarized as follows. (1) Even in case of limited rainfall events are available, it is possible to estimate the mean runoff characteristics by considering the affecting factors to runoff characteristics. (2) It is also possible to use the Monte Carlo simulation technique for estimating and evaluating the confidence intervals for concentration time and storage coefficient. The confidence intervals estimated in this study were found much narrower than those of Yoo et al. (2006). (3) A supporting result could also be derived using the Bootstrap technique. However, at least 20 independent rainfall events are necessary to get a rather significant result for concentration time and storage coefficient. (4) No empirical equations are found to be properly applicable for the study basin. However, empirical equations like the Kraven(I) and Kraven(II) are found valid for the estimation of concentration time, on the other hand the Linsley is found valid for the storage coefficient In this study basin. But users of these empirical formula should be careful as these also provide a wide range of possible values.

• Korean Journal of Hydrosciences
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• v.5
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• pp.57-70
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• 1994

For the system benefit optimization by considering risk or reliability from a multiple reservoir system using the Monte Carlo Technique, Many stochastically generated inflow series have to be used for the system analysis. In this study, the stochastically generated inflow series for the multiple reservoir system operation are preprocessed according to the considering system objectives and operating time periods. Through this procedure, several representative inflow series which have discrate probability levels and operation horizons are selected among the thousands of generated inflows. Then a deterministic optimization technique is applied to the hydropower energy estimation from the Han River Reservoir System which considers five reservoirs in this study. It took much less computational requirements than using the original Morite Carlo Technique, even though estimated result was almost similar.

• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.373-384
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• 2020

Hydrological models are based on a combination of parameters that describe the hydrological characteristics and processes within a watershed. For this reason, the model performance and accuracy are highly dependent on the parameters. However, model uncertainties caused by parameters with stochastic characteristics need to be considered. As a follow-up to the study conducted by Choi et al (2020), who developed a relatively simple semi-distributed hydrological model, we propose a tool to estimate the posterior distribution of model parameters using the Metropolis-Hastings algorithm, a type of Markov-Chain Monte Carlo technique, and analyze the uncertainty of model parameters and simulated stream flow. In addition, the uncertainty caused by the parameters of each version is investigated using the lumped and semi-distributed versions of the applied model to the Hapcheon Dam watershed. The results suggest that the uncertainty of the semi-distributed model parameters was relatively higher than that of the lumped model parameters because the spatial variability of input data such as geomorphological and hydrometeorological parameters was inherent to the posterior distribution of the semi-distributed model parameters. Meanwhile, no significant difference existed between the two models in terms of uncertainty of the simulation outputs. The statistical goodness of fit of the simulated stream flows against the observed stream flows showed satisfactory reliability in both the semi-distributed and the lumped models, but the seasonality of the stream flow was reproduced relatively better by the distributed model.

• 전자공학회논문지 IE
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• v.43 no.1
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• pp.59-66
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• 2006

In this study, we have proposed a motion equation to control synchro drive mobile robot, a path plan to compute and track the best path to given destination and a technique utilizing uniform distribution and cluster management based Monte Carlo localization to have track current position of moving robot. In the localization test which was repeated 73 times resulted as following. The average process time of original Monte Carlo localization was 12.8ms. The proposed cluster management Monte Carlo localization resulted 9.3ms. Also the proposed method resulted correctly in the cases where original method failed.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.1
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• pp.101-113
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• 2015

The Monte-Carlo (MC) technique is very efficient solution for statistical problem. Various MC methods can easily be applied to statistical circuit performance analysis. Recently, as the number of process parameters and their impact, has increasingly affected circuit performance, a sufficient sample size is required in order to consider high dimensionality, profound nonlinearity, and stringent accuracy requirements. Also, it is important to identify the performance of circuit as soon as possible. In this paper, Fast MC method is proposed for efficient analysis of circuit performance. The proposed method analyzes performance using enhanced-precision Latin Hypercube Sampling Monte Carlo (LHSMC). To increase the accuracy of the analysis, we calculate the effective dimension for the low discrepancy value on critical parameters. This will guarantee a robust input vector for the critical parameters. Using a 90nm process parameter and OP-AMP, we verified the accuracy and reliability of the proposed method in comparison with the standard MC, LHS and Quasi Monte Carlo (QMC).

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.12 no.2
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• pp.97-108
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• 2008

In this paper Bayes estimator of the parameter and reliability function of the zero-truncated Poisson distribution are obtained. Furthermore, recurrence relations for the estimator of the parameter are also derived. Monte Carlo simulation technique has been made for comparing the Bayes estimator and reliability function with the corresponding maximum likelihood estimator (MLE) of zero-truncated Poisson distribution.

• Nuclear Engineering and Technology
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• v.16 no.2
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• pp.97-105
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• 1984

A method and computer code for the uncertainty analysis in the top event unavailability are developed and tested by combining Monte Carlo Method and Moments method with fault tree reduction technique. Using system fault trees and unavailability data selected in WASH-1400, the efficiency of the proposed method is tested and these results are compared with those obtained by Monte Carlo method. It is shown that the results are sufficiently good in accuracy and computation time is considerably reduced compared with those by Monte Carlo method.

• The Journal of Engineering Geology
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• v.5 no.3
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• pp.301-308
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• 1995

In this study, a stochastic finite element method is proposed with a view to consider rock property variations in the analysis of structural behavior on underground caverns. Here, the Monte carlo simulation technique, which has been widely used in probabilistic applications in many engineering fields, is applied for the analysis of the effect rock property distribution. Using the newly developed computer program based on the above - mentioned method, the underground opening in biaxial stress field is analyzed considering the effect of material property variation.

• International Journal of Precision Engineering and Manufacturing
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• v.8 no.1
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• pp.73-78
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• 2007

The objective of this work was to simulate lead frame etching characteristics to optimize the etching process, Characteristics such as the etching factor and uniformity were investigated for different actual operating conditions, including pressure, distance from the nozzle tip, pipe pitch, and feed speed. The correlation between the etching and spray characteristics was analyzed to develop the etching model. Spray characteristics obtained from an experiment using a phase Doppler anemometer system were then simulated using a Monte-Carlo technique, The etching process model was coded in the Java language, The spray and etching characteristics were correlated with each other and simulated results agreed well with the measured data for a lead frame etching process, The optimal operating parameters under various conditions were successfully determined.