• Journal of Magnetics
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• v.19 no.1
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• pp.78-83
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• 2014

This paper proposes an efficient reliability-based optimization method for designing a superconducting magnetic energy system in presence of uncertainty. To evaluate the probability of failure of constraints, samplingbased reliability analysis method is employed, where Monte Carlo simulation is incorporated into dynamic Kriging models. Its main feature is to drastically reduce the numbers of iterative designs and computer simulations during the optimization process without sacrificing the accuracy of reliability analysis. Through comparison with existing methods, the validity of the proposed method is examined with the TEAM Workshop Problem 22.

• Communications for Statistical Applications and Methods
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• v.22 no.6
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• pp.625-637
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• 2015

We develop a Bayesian clustering procedure based on a Dirichlet process prior with cluster specific random effects. Gibbs sampling of a normal mixture of linear mixed regressions with a Dirichlet process was implemented to calculate posterior probabilities when the number of clusters was unknown. Our approach (unlike its counterparts) provides simultaneous partitioning and parameter estimation with the computation of the classification probabilities. A Monte Carlo study of curve estimation results showed that the model was useful for function estimation. We find that the proposed Dirichlet process mixture model with cluster specific random effects detects clusters sensitively by combining vague edges into different clusters. Examples are given to show how these models perform on real data.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1645-1651
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• 2016

A lot of data, particularly in the medical field, contain variables that have a measurement error such as blood pressure and body mass index. On the other hand, recently smoothing methods are often used to solve a complex scientific problem. In this paper, we study a Bayesian curve-fitting under functional measurement error model. Especially, we extend our previous model by incorporating covariates free of measurement error. In this paper, we consider penalized splines for non-linear pattern. We employ a hierarchical Bayesian framework based on Markov Chain Monte Carlo methodology for fitting the model and estimating parameters. For application we use the data from the fifth wave (2012) of the Korea National Health and Nutrition Examination Survey data, a national population-based data. To examine the convergence of MCMC sampling, potential scale reduction factors are used and we also confirm a model selection criteria to check the performance.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.06a
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• pp.77-78
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• 2014

A new MCMC method for optimization is presented in this paper, which is called the scanline block Gibbs sampler. Due to its slow convergence speed, traditional Markov chain Monte Carlo (MCMC) is not widely used. In contrast to the conventional MCMC method, it is more convenient to parallelize the scanline block Gibbs sampler. Since The main part of the scanline block Gibbs sampler is to calculate message between each edge, in order to accelerate the calculation of messages passing in scanline sampler, it is parallelized in GPU. It is proved that the implementation on GPU is faster than on CPU based on the experiments on the OpenGM2 benchmark.

• The Korean Journal of Applied Statistics
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• v.11 no.1
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• pp.97-111
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• 1998

This paper suggests a Bayesian method for testing first-order markov correlation among linear regression disturbances. As a Bayesian test criterion, Bayes factor is derived in the form of generalized Savage-Dickey density ratio that is easily estimated by means of posterior simulation via Gibbs sampling scheme. Performance of the Bayesian test is evaluated and examined based upon a Monte Carlo experiment and an empirical data analysis. Efficiency of the posterior simulation is also examined.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1128-1128
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• 2001

We have developed a probe for measuring the light levels inside illuminated fruit. The probe has minimal effect on the light levels being measured and enables the sampling of the light flux at any point within the fruit. We present experimental light extinction rates within apple, nashi, kiwifruit, and mandarin fruit. Moving from the illuminated side to the far side of the fruit, the extinction level follows an initial power law decay as the light diffuses into the fruit then reduces to an exponential decay through the rest of the fruit. Significant variations in the rates of light extinction are found in the core, skin and differing flesh regions. Monte Carlo simulations of the light distribution in fruit, which use scattering and absorption coefficients for the diffusely scattering tissue, and boundary conditions for the skin effects, produce results that follow the experimental results closely.

• Journal of Korean Society for Quality Management
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• v.27 no.3
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• pp.125-141
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• 1999

Bayesian inference and model selection method for software reliability growth models are studied. Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability with Poisson prior information are studied. For model selection, we explored the relative error.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.491-501
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• 2006

We consider a multiple change point model with autoregressive conditional heteroscedasticity (ARCH). The model assumes that all or the part of the parameters in the ARCH equation change over time. The occurrence of the change points is modelled as the discrete time Markov process with unknown transition probabilities. The model is estimated by Markov chain Monte Carlo methods based on the approach of Chib (1998). Simulation is performed using a variant of perfect sampling algorithm to achieve the accuracy and efficiency. We apply the proposed model to the simulated data for verifying the usefulness of the model.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.155-159
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• 2007

This paper calculates and gives the analysis of electron swarm transport coefficients as described electric conductive characteristics of pure Ar, pure $SiH_4$, $Ar-SiH_4$ mixture gases ($SiH_4$--0.5%, 2.5%, 5%) over the range of E/N =$0.01{\sim}300$[Td], P=0.1, 1, 5.0[Torr] by Monte Carlo the Backward prolongation method of the Boltzmann equation using computer simulation without using expensive equipment. 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.

• Bulletin of the Korean Space Science Society
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• 2008.10a
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• pp.27.2-27.2
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• 2008

The STSAT-3 satellite was initiated in October 2006 and will be launched into a lower sun-synchronous earth orbit (~700km) in 2010. COMIS takes hyperspectral images of 30m/60m ground sampling distance over a 30km swath width. The payload will be used for environmental monitoring, such as in-land water quality monitoring of Paldang Lake located next to Seoul, the capital of South Korea. An extensive sensitivity and error budget analysis of COMIS optical system have been performed. As way of estimating aggregate effects of all tolerances, a Monte Carlo simulation is used.