• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.540-544
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• 2009

Hydrologic pattern under climate change has been paid attention to as one of the most important issues in hydrologic science group. Rainfall and runoff is a key element in the Earth's hydrological cycle, and associated with many different aspects such as water supply, flood prevention and river restoration. In this regard, a main objective of this study is to evaluate design flood using simulation techniques which can consider a full spectrum of uncertainty. Here we utilize a weather state based stochastic multivariate model as conditional probability model for simulating the rainfall field. A major premise of this study is that large scale climatic patterns are a major driver of such persistent year to year changes in rainfall probabilities. Uncertainty analysis in estimating design flood is inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. A comprehensive discussion on design flood under climate change is provided.

• Journal of the Korean Society of Visualization
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• v.5 no.1
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• pp.12-15
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• 2007

Macromolecule diffusion in cells and tissues is important for cell signaling, metabolism and locomotion. Biophysical methods, including non-invasive or minimally invasive in-vivo photobleaching techniques and single quantum-dot tracking, have been used to measure the rates of macromolecule diffusion in living cells and tissues, including central nervous system and tumors. Mathematical modeling and statistical analysis of experimental data revealed various modes of diffusion, which are strongly coupled with spatiotemporal changes in nanoscale structures and material properties.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.225-228
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• 2002

This research seeks to derive the design rainfalls through the L-moment with the test of homogeneity, independence and outlier of data on annual maximum daily rainfall in 38 Korean rainfall stations. To select the fit appropriate distribution of annual maximum daily rainfall data according to rainfall stations, applied were Generalized Extreme Value (GEV), Generalized Logistic (GLO) and Generalized Pareto (GPA) probability distributions were applied. and their aptness was judged Dusing an L-moment ratio diagram and the Kolmogorov-Smirnov (K-S) test, the aptitude was judged of applied distributions such as GEV, GLO and GPA. The GEV and GLO distributions were selected as the appropriate distributions. Their parameters were estimated Targetingfrom the observed and simulated annual maximum daily rainfalls and using Monte Carlo techniques, the parameters of GEV and GLO selected as suitable distributions were estimated and. dDesign rainfallss were then derived, using the L-moment. Appropriate design rainfalls were suggested by doing a comparative analysis of design rainfall from the GEV and GLO distributions according to rainfall stations.

• Journal of the Korean Statistical Society
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• v.16 no.2
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• pp.92-101
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• 1987

Our interest is to access in some way teh relative odds or probability that a multivariate observation Z belongs to one of k multivariate normal populations with unequal covariance matrices. We derived the empirical Bayes posterior odds ratio for the classification rule when population parameters are unknown. It is a generalization of the posterior odds ratio suggested by Gelsser (1964). The classification rule does not have complicated distribution theory which a large variety of techniques from the sampling viewpoint have. The proposed posterior odds ratio is compared to the Gelsser's posterior odds ratio through a Monte Carlo study. The results show that the empiricla Bayes posterior odds ratio, in general, performs better than the Gelsser's. Especially, for large dimension of Z and small training sample, the performance is prominent.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.803-814
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• 2016

Recent times have seen an exponential increase in the amount of spatial data, which is in many cases associated with temporal data. Recent advances in computer technology and computation of hierarchical Bayesian models have enabled to analyze complex spatio-temporal data. Our work aims at modeling data of daily average nitrogen dioxide (NO2) levels obtained from 25 air monitoring sites in Seoul between 2003 and 2010. We considered an independent Gaussian process model and an auto-regressive model and carried out estimation within a hierarchical Bayesian framework with Markov chain Monte Carlo techniques. A Gaussian predictive process approximation has shown the better prediction performance rather than a Hierarchical auto-regressive model for the illustrative NO2 concentration levels at any unmonitored location.

• Journal of the Korean Data and Information Science Society
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• v.28 no.1
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• pp.207-215
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• 2017

In this paper we study pooling effects in Bayesian testing procedures of independence for contingency tables from small areas. In small area estimation setup, we typically use a hierarchical Bayesian model for borrowing strength across small areas. This techniques of borrowing strength in small area estimation is used to construct a Bayes test of independence for contingency tables from small areas. In specific, we consider the methods of direct or indirect pooling in multinomial models through Dirichlet priors. We use the Bayes factor (or equivalently the ratio of the marginal likelihoods) to construct the Bayes test, and the marginal density is obtained by integrating the joint density function over all parameters. The Bayes test is computed by performing a Monte Carlo integration based on the method proposed by Nandram and Kim (2002).

• 유체기계공업학회:학술대회논문집
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• 2006.08a
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• pp.493-496
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• 2006

Rarefied gas flows through two-dimensional micro channels are studied numerically for the performance optimization of a nanomembrane-based Knudsen compressor. The effects of the wall temperature distributions on the thermal transpiration flow patterns are examined. The flow has a pumping effect, and the mass flow rates through the channel are calculated. The results show that a steady one-way flow is induced for a wide range of the Knudsen number. The DSMC(direct simulation Monte Carlo) method with VHS(variable hard sphere) model and NTC(no time counter) techniques has been applied in this work to obtain numerical solutions.

• Transactions of the Korean Society of Mechanical Engineers
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• v.7 no.3
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• pp.241-248
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• 1983

In the present work a time-dependent reliability model for a typical solar domestic hot water and heating system is developed using the method of Fault Tree Analysis and existing mathematical techniques. The reference system used in this analysis is a typical solar heating system. The system reliability structure has been identified with the aid of Fault Tree methods. In addition, a simulation of the solar system reliability has been performed employing the Monte Carlo method. In the computer simulation, failure rate data such as WASH-1400, MIL-HDBK-217B, and Green and Bourne are used as input data. These results show that the developed reliability model is capable of expressing the primary failure phenomena of the solar heating and domestic hot water system.

• Journal of Industrial Technology
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• v.5
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• pp.37-46
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• 1985

Recently-used structural reliability models are studied, and the usage and characteristics of each method are discussed. Although the First-Order Second Moment method may be efficient in structural reliability analysis, it has limitations which the limit state equation is linear and all the variables are normal. In that point, the Advanced Second-Moment(ASM) method have many good results, but computation of iterative method are trublesome. The results of ASM method similar to Variance Reduction Techniques(VRT), which is one of the Monte Carlo simulation methods. As a results, it is concluded that ASM method and VRT method are most efficient one.

• Journal of Korean Society for Quality Management
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• v.24 no.3
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• pp.50-64
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• 1996

This article is concerned with procedures for detecting multiple y outliers in linear regression. The outward-testing procedure, which is controled by the initial subset and the minimum residuals, is suggested by two phases. The performance of this procedure is compared with others by Monte Carlo techniques and found to be superior. The procedure, however, fails in detecting y outliers that are on high-leverage cases in Phase 1. Thus, we proposed ELMS algorithm for a set of suspect observations, in Phase 1. In Phase 2, the proposed testing is conducted using the studentized residuals to see which of the suspect cases are outliers. Several examples are analyzed.