• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2006.06a
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• pp.507-508
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• 2006

$SF_6$ gas is widely used in industrial of insulation field. In this paper, $N_2$ is mixed to improve pure $SF_6$ gas characteristics. Electron transport coefficients in $SF_6-N_2$ mixture gases are simulated in range of E/N values from 70 to 400 [Td] at 300K and 1 Torr by using Boltzmann equation method. The results have been obtained by using the electron collision cross sections by TOF, PT, SST sampling, compared with the experimental data determined by the other author. It also proved the reliability of the electron collision cross sections and shows the practical values of computer simulation. The result of Boltzmann equation and Monte Carlo Simulation has been compared with experimental data by Ohmori, Lucas and Carter. The swarm parameter from the swarm study are expected to sever as a critical test of current theories of low energy scattering by atoms and molecules.

• Ocean Systems Engineering
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• v.9 no.2
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• pp.135-155
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• 2019

One of the safest and the most economical methods to transfer oil and gas is pipeline system. Prediction and prevention of pipeline failures during its assessed lifecycle has considerable importance. The dropped object is one of the accidental scenarios in the failure of the submarine pipelines. In this paper, using Monte Carlo Sampling, the probability of damage to a submarine pipeline due to a box-shaped dropped object has been calculated in terms of dropped object impact frequency and energy transfer according to the DNV-RP-F107. Finally, Reliability sensitivity analysis considering random variables is carried out to determine the effect intensity of each parameter on damage probability. It is concluded that impact area and drag coefficient have the highest sensitivity and mass and add mass coefficient have the lowest sensitivity on probability of failure.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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• pp.239-250
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• 2022

In many applications, we frequently encounter correlated multiple outcomes measured on the same subject. Joint modeling of such multiple outcomes can improve efficiency of inference compared to independent modeling. For instance, in developmental toxicity studies, fetal weight and number of malformed pups are measured on the pregnant dams exposed to different levels of a toxic substance, in which the association between such outcomes should be taken into account in the model. The number of malformations may possibly have many zeros, which should be analyzed via zero-inflated count models. Motivated by applications in developmental toxicity studies, we propose a Bayesian joint modeling framework for continuous and count outcomes with excess zeros. In our model, zero-inflated Poisson (ZIP) regression model would be used to describe count data, and a subject-specific random effects would account for the correlation across the two outcomes. We implement a Bayesian approach using MCMC procedure with data augmentation method and adaptive rejection sampling. We apply our proposed model to dose-response analysis in a developmental toxicity study to estimate the benchmark dose in a risk assessment.

• Earthquakes and Structures
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• v.25 no.1
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• pp.69-78
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• 2023

Life-cycle performance analysis of a reinforced concrete box section bridge was generated. Moreover, Monte Carlo simulation with important sampling (IS) was used to simulate the bridge material and load uncertainties. The bridge deterioration model was generated with the basic probabilistic principles and updated according to the measurement data. A genetic algorithm (GA) with the response surface model (RSM) was used to determine the deterioration rate. The importance of health monitoring systems to sustain the bridge to give services economically and reliably and the advantages of fiber-optic sensors for SHM applications were discussed in detail. This study showed that the most effective loss of strength in reinforced concrete box section bridges is corrosion of the reinforcements. Due to reinforcement corrosion, the use of the bridge, which was examined, could not meet the desired strength performance in 25 years, and the need for reinforcement. In addition, it has been determined that long-term health monitoring systems are an essential approach for bridges to provide safe and economical service. Moreover the use of fiber optic sensors has many advantages because of the ability of the sensors to be resistant to environmental conditions and to make sensitive measurements.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.677-693
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• 2011

In this paper, we consider Bayesian approaches to zero inflated Poisson model, one of the popular models to analyze zero inflated count data. To generate posterior samples, we deal with a Markov Chain Monte Carlo method using a Gibbs sampler and an exact sampling method using an Inverse Bayes Formula(IBF). Posterior sampling algorithms using two methods are compared, and a convergence checking for a Gibbs sampler is discussed, in particular using posterior samples from IBF sampling. Based on these sampling methods, a real data analysis is performed for Trajan data (Marin et al., 1993) and our results are compared with existing Trajan data analysis. We also discuss model selection issues for Trajan data between the Poisson model and zero inflated Poisson model using various criteria. In addition, we complement the previous work by Rodrigues (2003) via further data analysis using a hierarchical Bayesian model.

• The Korean Journal of Applied Statistics
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• v.35 no.1
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• pp.131-146
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• 2022

Logit models are commonly used to predicting and classifying categorical response variables. Most Bayesian approaches to logit models are implemented based on the Metropolis-Hastings algorithm. However, the algorithm has disadvantages of slow convergence and difficulty in ensuring adequacy for the proposal distribution. Therefore, we use auxiliary mixture sampler proposed by Frühwirth-Schnatter and Frühwirth (2007) to estimate logit models. This method introduces two sequences of auxiliary latent variables to make logit models satisfy normality and linearity. As a result, the method leads that logit model can be easily implemented by Gibbs sampling. We applied the proposed method to diabetes data from the Community Health Survey (2020) of the Korea Disease Control and Prevention Agency and compared performance with Metropolis-Hastings algorithm. In addition, we showed that the logit model using auxiliary mixture sampling has a great classification performance comparable to that of the machine learning models.

• Journal of Korea Water Resources Association
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• v.42 no.12
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• pp.1039-1052
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• 2009

Radar measurement with high temporal and spatial resolutions can be a valuable source of data, especially in the areas where rain gauge installment is not practical. However, this kind of data brings with it many errors. The objective of this paper is to propose a method to evaluate statistically the quantitative and qualitative accuracy at different radar ranges, temporal intervals and raingage densities and use a bias adjustment technique to improve the quality of radar rainfall for the purpose of hydrological application. The method is tested with the data of 2 storm events collected at Jindo (S band) and Kwanak (C band) radar stations. The obtained results show that the accuracy of radar rainfall estimation increases when time interval rises. Radar data at the shorter range seems to be more accurate than the further one, especially for C-band radar. Using the Monte Carlo simulation experiment, we find out that the sampling error of the bias between radar and gauge rainfall reduces nonlinearly with increasing raingage density. The accuracy can be improved considerably if the real-time bias adjustment is applied, making adjusted radar rainfall to be adequately good to apply for hydrological application.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.28 no.4
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• pp.351-359
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• 2015

In order to exactly evaluate the seismic collapse capacity of a structure, probabilistic approach is required by considering uncertainties related to its structural properties and ground motion. Regardless of the types of uncertainties, they influence on the seismic response of a structures and their effects are required to be estimated. An incremental dynamic analysis(IDA) is useful to investigate uncertainty-propagation due to ground motion. In this study, a 3-story steel moment-resisting frame is selected for a prototype frame and analyzed using the IDA. The uncertainty-propagation is assessed with categorized parameters representing epistemic uncertainties, such as the seismic weight, the inherent damping, the yield strength, and the elastic modulus. To do this, the influence of the uncertainty-propagation to the seismic collapse capacity of the prototype frame is probabilistically evaluated using the incremental dynamic analyses based on the Monte-Carlo simulation sampling with the Latin hypercube method. Of various parameters related to epistemic uncertainty-propagation, the inherent damping is investigated to be the most influential parameter on the seismic collapse capacity of the prototype frame.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.21 no.6
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• pp.495-507
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• 2009

The expected sliding distance for the lifetime of a caisson breakwater has a limitation to be used as the stability criterion of the breakwater. Since the expected sliding distance is calculated as the mean of simulated sliding distances for the lifetime, there is possibility for the actual sliding distance to exceed the expected sliding distance. To overcome this problem, the exceedance probability of the allowable sliding distance is used to assess the stability of sliding. Latin Hypercube sampling and Crude Monte Carlo simulation were used to calculate the exceedance probability. The doubly-truncated normal distribution was considered to complement the physical disadvantage of the normal distribution as the random variable distribution. In the case of using the normal distribution, the cross-sections of Okgye, Hwasun, and Donghae NI before reinforcement were found to be unstable in all the limit states. On the other hand, when applying the doubly-truncated normal distribution, the cross-sections of Hwasun and Donghae NI before reinforcement were evaluated to be unstable in the repairable limit state and all the limit states, respectively. Finally, the shortcoming of the expected sliding distance as the stability criterion was investigated, and we reasonably assessed the stability of sliding of caissons by using the exceedance probability of allowable sliding distance for the caisson breakwaters in Korea.

• Journal of the Korea Computer Graphics Society
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• v.16 no.3
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• pp.1-10
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• 2010

In this paper, we propose a novel noiseless method of BRDF rendering on a GPU in real-time. Illumination at a surface point is formulated as an integral of BRDF producted with incident radiance over the hemi-sphere domain. The most popular method to compute the integral is the Monte Carlo method, which needs a large number of samples to achieve good image quality. But, it leads to increase of rendering time. Otherwise, a small number of sample points cause serious image noise. The main contribution of our work is a new importance sampling scheme producing a set of incoming ray samples varying continuously with respect to the eye ray. An incoming ray is importance-based sampled at different latitude angles of the eye ray, and then the ray samples are linearly connected to form a curve, called a thread. These threads give continuously moving incident rays for eye ray change, so they do not make image noise. Since even a small number of threads can achieve a plausible quality and also can be precomputed before rendering, they enable real-time BRDF rendering on the GPU.