• Journal of the Korean Geotechnical Society
• /
• v.30 no.10
• /
• pp.5-18
• /
• 2014

Many regions around the world are vulnerable to rainfall-induced slope failures. A variety of methods have been proposed for revealing the mechanism of slope failure initiation. Current analysis methods, however, do not consider the effects of non-homogeneous soil profiles and variable hydraulic responses on rainfall-induced slope failures. In this study, probabilistic stability analyses were conducted for weathered residual soil slopes with different soil thickness overlying impermeable bedrock to study the rainfall-induced failure mechanisms depending on the soil thickness. A series of seepage and stability analyses of an infinite slope based on one-dimensional random fields were performed to consider the effects of uncertainty due to the spatial heterogeneity of hydraulic conductivity on the failure of unsaturated slopes due to rainfall infiltration. The results showed that a probabilistic framework can be used to efficiently consider various failure patterns caused by spatial variability of hydraulic conductivity in rainfall infiltration assessment for a infinite slope.

• Food Science of Animal Resources
• /
• v.38 no.5
• /
• pp.1043-1054
• /
• 2018

Salmonellosis caused by chicken consumption has been a critical issue in food safety worldwide, including in Korea. The probability of illness from consumption of chicken was simulated in study, based on the recipe of Dakgalbi, a commonly eaten chicken dish in Korea. Additionally, the processing stage at slaughterhouses to decrease Salmonella concentration in broilers was modeled to explore its effect on the likelihood of illness. A Monte Carlo simulation model was created using @RISK. Prevalence of Salmonella in chickens at the retail stage was found to be predominantly important in determining the probability of illness. Other than the prevalence, cooking temperature was found to have the largest impact on the probability of illness. The results also demonstrated that, although chlorination is a powerful tool for decreasing the Salmonella concentration in chicken, this effect did not last long and was negated by the following stages. This study analyzes the effects of variables of the retail-to-table pathway on the likelihood of salmonellosis in broiler consumption, and also evaluates the processing step used to decrease the contamination level of Salmonella in broilers at slaughterhouses. According to the results, we suggest that methods to decrease the contamination level of Salmonella such as chlorination had little effect on decreasing the probability of illness. Overall, these results suggest that preventing contamination of broiler with Salmonella must be a top priority and that methods to reduce the concentration of Salmonella in broilers at slaughterhouses hardly contribute to safe consumption of Salmonella-contaminated chicken.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.6
• /
• pp.2893-2900
• /
• 2014

Background: Statistical methods are very important to precisely measure breast cancer patient survival times for healthcare management. Previous studies considered basic statistics to measure survival times without incorporating statistical modeling strategies. The objective of this study was to develop a data-based statistical probability model from the female breast cancer patients' survival times by using the Bayesian approach to predict future inferences of survival times. Materials and Methods: A random sample of 500 female patients was selected from the Surveillance Epidemiology and End Results cancer registry database. For goodness of fit, the standard model building criteria were used. The Bayesian approach is used to obtain the predictive survival times from the data-based Exponentiated Exponential Model. Markov Chain Monte Carlo method was used to obtain the summary results for predictive inference. Results: The highest number of female breast cancer patients was found in California and the lowest in New Mexico. The majority of them were married. The mean (SD) age at diagnosis (in years) was 60.92 (14.92). The mean (SD) survival time (in months) for female patients was 90.33 (83.10). The Exponentiated Exponential Model found better fits for the female survival times compared to the Exponentiated Weibull Model. The Bayesian method is used to obtain predictive inference for future survival times. Conclusions: The findings with the proposed modeling strategy will assist healthcare researchers and providers to precisely predict future survival estimates as the recent growing challenges of analyzing healthcare data have created new demand for model-based survival estimates. The application of Bayesian will produce precise estimates of future survival times.

• Journal of the Society of Naval Architects of Korea
• /
• v.57 no.2
• /
• pp.80-87
• /
• 2020

As the Arctic sea ice decreases due to various reasons such as global warming, the demand for ships and offshore structures operating in the Arctic region is steadily increasing. In the case of sea ice, the anisotropy is caused by the uncertainty inside the material. For most of the research, nevertheless, estimating the ice load has been treated deterministically. With regard to this, in this paper, a four-point bending strength analysis of an ice specimen was attempted using a stochastic finite element method. First, spatial distribution of the material properties used in the yield criterion was assumed to be a multivariate Gaussian random field. After that, a direct method, which is a sort of stochastic finite element method, and a sensitivity method using the sensitivity of response for random variables were proposed for calculating the probabilistic distribution of ice specimen strength. A parametric study was conducted with different mean vectors and correlation lengths for each material property used in the above procedure. The calculation time was about ten seconds for the direct method and about three minutes for the sensitivity methods. As the cohesion and correlation length increased, the mean value of the critical load and the standard deviation increased. On the contrary, they decreased as the friction angle increased. Also, in all cases, the direct and sensitivity methods yielded very similar results.

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

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.36 no.8
• /
• pp.921-927
• /
• 2012

Reliability-based design optimization (RBDO) can be used to determine the reliability of a system by means of probabilistic design criteria, i.e., the possibility of failure considering stochastic features of design variables and input parameters. To assure these criteria, various reliability analysis methods have been developed. Most of these methods assume that distribution functions are continuous. However, in real problems, because real data is often discrete in form, it is important to estimate the distributions for discrete information during reliability analysis. In this study, we employ the Akaike information criterion (AIC) method for reliability analysis to determine the best estimated distribution for discrete information and we suggest an RBDO method using AIC. Mathematical and engineering examples are illustrated to verify the proposed method.

• Journal of Korea Water Resources Association
• /
• v.46 no.3
• /
• pp.219-227
• /
• 2013

Time series data including change, jump, trend and periodicity generally have nonstationarity. Especially, various methods have been proposed to identify the trend about hydrological time series data. However, among various methods, evaluation about capability of each trend test has not been done a lot. Even for the same data, each method may show the different result. In this study, the simulation was performed for identification about the changes in trend analysis according to the statistical characteristics and the capability in the trend analysis. For this purpose, power test for the trend analysis is conducted using Men-Kendall test, Hotelling-Pabst test, t test and Sen test according to the slope, sample size, standard deviation and significance level. As a result, t test has higher statistical power than the others, while Mann-Kendall, Hotelling-Pabst, and Sen tests were similar results.

• Journal of the Society of Naval Architects of Korea
• /
• v.50 no.5
• /
• pp.334-342
• /
• 2013

In this paper, we analyze the procedure of the design manager for the enhancement of the hull production design process by use of the simulation method. Normally, design manager assigns design jobs according to various methods and estimates the corresponding results. When the construction drawing which is the output of the detail design where a design is dealt by zones, the design manager identifies blocks and analyzes their work difficulties, and assigns jobs to design engineers who are different in capabilities. These processes including the design engineer who can be modeled with man-hours evaluation model are represented in detail as a simulation model. As the high-level modeling for the discrete-event system, we use Event Graph model. And we implemented the simulation using Simkit which is open simulation engine for the discrete-event system. We made the simulation scenario to be written by a user in the scenario generator which is separated from the simulation model, and made the simulation result to be visualized in the form of Gantt chart in a Web. In the scenario of the irregular issuance for various construction drawings which contain different numbers of blocks, we performed the Monte-Carlo simulation according to various assignment methods to find the assignment result that satisfies the mid-term schedule.

• The Korean Journal of Applied Statistics
• /
• v.26 no.2
• /
• pp.291-305
• /
• 2013

Collective traffic data (BPS, PPS etc.) for detection against the distributed denial of service attack on network is the time sequencing big data. The algorithm to detect the change point in the big data should be accurate and exceed in detection time and detection capability. In this work, the sliding window and discretization method is used to detect the change point in the big data, and propose five nonparametric test statistics using empirical distribution functions and ranks. With various distribution functions and their parameters, the detection time and capability including the detection delay time and the detection ratio for five test methods are explored and discussed via monte carlo simulation and illustrative examples.

• Nuclear Engineering and Technology
• /
• v.43 no.6
• /
• pp.567-572
• /
• 2011

A sensitivity analysis of the methods used to evaluate the transport properties of a CdZnTe detector was performed using two different radiations (${\alpha}$ particle and gamma-ray) emitted from an $^{241}Am$ source. The mobility-lifetime products of the electron-hole pair in a planar CZT detector ($5{\times}5{\times}2\;mm^3$) were determined by fitting the peak position as a function of biased voltage data to the Hecht equation. To verify the accuracy of these products derived from ${\alpha}$ particles and low-energy gamma-rays, an energy spectrum considering the transport property of the CZT detector was simulated through a combination of the deposited energy and the charge collection efficiency at a specific position. It was found that the shaping time of the amplifier module significantly affects the determination of the (${\mu}{\tau}$) products; the ${\alpha}$ particle method was stabilized with an increase in the shaping time and was less sensitive to this change compared to when the gamma-ray method was used. In the case of the simulated energy spectrum with transport properties evaluated by the ${\alpha}$ particle method, the peak position and tail were slightly different from the measured result, whereas the energy spectrum derived from the low-energy gamma-ray was in good agreement with the experimental results. From these results, it was confirmed that low-energy gamma-rays are more useful when seeking to obtain the transport properties of carriers than ${\alpha}$ particles because the methods that use gamma-rays are less influenced by the surface condition of the CZT detector. Furthermore, the analysis system employed in this study, which was configured by a combination of Monte Carlo simulation and the Hecht model, is expected to be highly applicable to the study of the characteristics of CZT detectors.