• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.659-668
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• 2006

The uncertainty of the risk of failure of hydraulic structures can be determined by estimating the variance of the risk of failure based on the methods of moments, probability weighted moments, and maximum likelihood assuming that the underlying model is the Gumbel distribution. In this paper, the variance of the risk of failure was derived. Monte Carlo simulation was peformed to verify the characteristics of the derived formulas for various sample size, design life, nonexceedance probability, and variation coefficient. As the results, PWM showed the smallest relative bias and root mean square error than the others while ML showed the smallest ones for relatively large sample siBes regardless of design life and nonexceedance probability. Also, it was found that variation coefficient does not effect on the relative bias and relative root mean square error.

• Journal of Radiation Protection and Research
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• v.29 no.2
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• pp.105-115
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• 2004

The Monte Carlo simulation requires very much time to obtain a result of acceptable accuracy. Therefore we should know the optimum number of history not to sacrifice time as well as the accuracy. In this study, we have investigated the effects of statistical uncertainties of the photon dose calculation. BEAMnrc and DOSXYZnrc systems were used for the Monte Carlo dose calculation and the case of mediastinum was simulated. The several dose calculation result from various number of histories had been obtained and analyzed using the criteria of isodose curve comparison, dose volume histogram comparison(DVH) and root mean-square differences(RMSD). Statistical uncertainties were observed most evidently in isodose curve comparison and RMSD while DVHs were less sensitive. The acceptable uncertainties $(\bar{{\Delta}D})$ of the Monte Carlo photon dose calculation for the radiation therapy were estimated within total 9% error or 1% error for over than $D_{max}/2$ voxels or voxels at maximum dose.

• The Korean Journal of Applied Statistics
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• v.9 no.2
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• pp.13-23
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• 1996

In this paper, we consider the Marshall and Olkin's bivariate exponential model under random censorship for the distribution of failure times of a system with two components. We propose a bootstrap testing procedure for independence and compare the powers of it with other tests via Monte Carlo simulation.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.247-255
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• 2003

Hypothesis testing for the equality of two distributions were considered. Nonparametric kernel density estimates were used for testing equality of distributions. Cross-validatory choice of bandwidth was used in the kernel density estimation. Sampling distribution of considered test statistic were developed by resampling method, called the bootstrap. Small sample Monte Carlo simulation were conducted. Empirical power of considered tests were compared for variety distributions.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.129-139
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• 1998

This article examines the use and some difficulties of Normal Test Variables(NTV) plot for clinical research. Monte Carlo Simulation results are presented based on Normal, Bimodal, Uniform, Exponential and skewed-right distributed Beta Distributions. Further, some solutions are presented and illustrated.

• The Korean Journal of Applied Statistics
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• v.10 no.1
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• pp.121-136
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• 1997

Spatial data is collected on a regular Cartesian lattice. In this paper we consider the model indentification of spatial autoregressive(SAR) models using AIC, BIC, pattern method. The proposed methods are considered as an application of AIC, BIC, 3-patterns for SAR models through three directions; row, column and diagonal directions. Using the Monte Carlo simulation, we test the efficiency of the proposed methods for various SAR models.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.31-39
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• 2006

In many cases, we frequently get a desired information based on the appropriate statistical analysis of collected data sets. Lots of statistical theory rely on the assumption of the normality of the data. In this paper, we compare the empirical power of some normality tests including sample entropy quantity. Monte carlo simulation is conducted for the calculation of empirical power of considered normality tests by varying sample sizes for various distributions.

• Magazine of the Korea Concrete Institute
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• v.3 no.4
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• pp.117-123
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• 1991

It is widely recognized that the strengths of reinforced concrete members have random characteristics due to the variability of the mechanical properties of concrete and steel, the dimensional error as well as incorrect placement of reinforcing bars. Statistical models of the variabilities of strengths of reinforced concrete members, therefore, need to be developed to evaluate the safety level implied in current practices. Based on the probabilistic models of basic factors affecting the R.C. member strengths, in this study, the probabilistic characteristics of member resistance have been studied through Monte Carlo simulation.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.505-514
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• 2000

A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduced mixture failure model of Rayleigh and Erlang(2) pattern. This data augmentation approach facilitates specification of the transitional measure in the Markov Chain. Gibbs steps are proposed to perform the Bayesian inference of such models. For model determination, we explored sum of relative error criterion that selects the best model. A numerical example with simulated data set is given.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.4
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• pp.392-397
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• 2014

Recently, there are many researches on active safety system of intelligent vehicle. To reduce the probability of collision caused by driver's inattention and mistakes, the active safety system gives warning or controls the vehicle toward avoiding collision. For the purpose, it is necessary to recognize and analyze circumstances around. In this paper, we will treat the problem about collision risk assessment. In general, it is difficult to calculate the collision risk before it happens. To consider the uncertainty of the situation, Monte Carlo simulation can be employed. However it takes long computation time and is not suitable for practice. In this paper, we apply neural networks to solve this problem. It efficiently computes the unseen data by training the results of Monte Carlo simulation. Furthermore, we propose the features affects the performance of the assessment. The proposed algorithm is verified by applications in various crash scenarios.