• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.29-39
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• 2012

The main objective is to predict the future degradation and maintenance budget for a suspension bridge system. Bayesian inference is applied to find the posterior probability density function of the source parameters (damage indices and serviceability), given ten years of maintenance data. The posterior distribution of the parameters is sampled using a Markov chain Monte Carlo method. The simulated risk prediction for decreased serviceability conditions are posterior distributions based on prior distribution and likelihood of data updated from annual maintenance tasks. Compared with conventional linear prediction model, the proposed quadratic model provides highly improved convergence and closeness to measured data in terms of serviceability, risky factors, and maintenance budget for bridge components, which allows forecasting a future performance and financial management of complex infrastructures based on the proposed quadratic stochastic regression model.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.4
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• pp.211-222
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• 2020

This study suggested a method calculating the influence of effluent discharge from Saemangeum sluice-gates using the particle tracking model. For 2017, we presented the seasonal effects of effluent discharge as probability spatial distributions and compared with the results of the water age, one of the indicators of transport time scale. The influence of sluice-gates effluent discharge increases radially around Sinshi or Gaseok gates, which are expected to be biased toward the south in winter and north in summer due to the effect of seasonal winds. Although the results of the prediction are limited to the 2017 situation, the method of calculating the influence of sluice-gates effluent discharge using the Lagrangian particle tracking model can be used to predict the future of the around Saemangeum.

• Bulletin of the Korean Chemical Society
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• v.33 no.1
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• pp.143-148
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• 2012

The photodissociation dynamics of cyclopropyl bromide ($C_3H_5Br$) and cyclobutyl bromide ($C_4H_7Br$) at 234 nm was investigated. A two-dimensional photofragment ion-imaging technique coupled with a [2+1] resonanceenhanced multiphoton ionization scheme was utilized to obtain speed and angular distributions of the nascent $Br(^2P_{3/2})$ and $Br^*(^2P_{1/2})$ atoms. The recoil anisotropies for the Br and $Br^*$ channels were measured to be ${\beta}_{Br}=0.92{\pm}0.03$ and ${\beta}_{Br^*}=1.52{\pm}0.04$ for $C_3H_5Br$ and ${\beta}_{Br}=1.10{\pm}0.03$ and ${\beta}_{Br^*}=1.49{\pm}0.05$ for $C_4H_7Br$. The relative quantum yield for Br was found to be ${\Phi}_{Br}=0.13{\pm}0.03$ and for $C_3H_5Br$ and $C_4H_7Br$, respectively. The soft radical limit of the impulsive model adequately modeled the related energy partitioning. The nonadiabatic transition probability from the 3A' and 4A' potential energy surfaces was estimated and discussed.

• Animal cells and systems
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• v.1 no.1
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• pp.123-128
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• 1997

The characteristics of allelic polymorphisms of the two (CA)n microsatellite (p599 and ㅅ599) markers spanning the long arm of chromosome 5 were studied in 52 DNA samples from unrelated inhabitants of Seoul (Korea) by using the polymerase chain reaction (PCR) to investigate differences in allele frequencies between Korean and Caucasian populations. The 6 alleles were observed for p599 (CA)n with a polymorphism informative content (PIC) value of 0.71 and 9 alleles for ㅅ599 (CA)n with a PIC value of 0.82. The observed heterozygote frequencies of the loci were estimated to 0.730 and 0.846, respectively. Several allele frequencies of two loci showed significant differences between Korean and Caucasian populations. Genotype data from the two loci were consistent with the Hardy-Weinberg equilibrium by x2 test. Linkage disequilibrium between p599 (CA)n and ㅅ599 (CA)n loci was observed in x2 test between the observed and expected frequency of allelic association. The probability of matching calculated at each locus was 0.104 for p599 (CA)n and 0.043 for ㅅ599 (CA)n, respectively. These results demonstrate the need to determine populationspecific allele frequency distributions for polymorphic markers when performing genetic linkage studies in racially defined several populations.

• Food Science of Animal Resources
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• v.32 no.1
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• pp.118-124
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• 2012

This study was conducted for quantitative microbial risk assessment (QMRA) of Clostridium perfringens with consumption on ham and sausage products in Korea, according to Codex guidelines. Frame-work model as product-retail-consumption pathway composed with initial contamination level, the time and temperature in distributions, and consumption data sets for ham and sausage products and also used the published predictive growth and dose-response models for Cl. perfringens. The simulation model and formulas with Microsoft@ Excel spreadsheet program using these data sets was developed and simulated with @RISK. The probability of foodborne disease by Cl. perfringens with consumption of the ham and sausage products per person per day was estimated as $3.97{\times}10^{-11}{\pm}1.80{\times}10^{-9}$. There were also noted that limitations in this study and suggestion for development of QMRA in the future in Korea.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.409-414
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• 2013

Naive Bayesian classifiers are a powerful and well-known type of classifiers that can be easily induced from a dataset of sample cases. However, the strong conditional independence assumptions can sometimes lead to weak classification performance. Normally, naive Bayesian classifiers use Gaussian distributions to handle continuous attributes and to represent the likelihood of the features conditioned on the classes. The probability density of attributes, however, is not always well fitted by a Gaussian distribution. Another eminent type of classifier is the neuro-fuzzy classifier, which can learn fuzzy rules and fuzzy sets using supervised learning. Since there are specific structural similarities between a neuro-fuzzy classifier and a naive Bayesian classifier, the purpose of this study is to apply learning distribution graphs constructed by a neuro-fuzzy network to naive Bayesian classifiers. We compare the Gaussian distribution graphs with the fuzzy distribution graphs for the naive Bayesian classifier. We applied these two types of distribution graphs to classify leukemia and colon DNA microarray data sets. The results demonstrate that a naive Bayesian classifier with fuzzy distribution graphs is more reliable than that with Gaussian distribution graphs.

• Spatial Information Research
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• v.19 no.2
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• pp.29-36
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• 2011

This study performed quantitative assessment of the performance of adjustment models by a-priori analysis of the statistics of the surface parameter estimates against modeling factors. Lidar, airborne imagery, and SAR imagery have been used to acquire the earth surface elevation, where the shape properties of the surface need to be determined through neighboring observations around target location. In this study, parameters which are selected to be estimated are elevation, slope, second order coefficient. In this study, several factors which are needed to be specified to compose adjustment models are classified into three types: mathematical functions, kernel sizes, and weighting types. Accordingly, a-priori standard deviations of the parameters are computed for varying adjustment models. Then their corresponding confidence regions for both the standard deviation of the estimate and the estimate itself are calculated in association with probability distributions. Thereafter, the resulting confidence regions are compared to each other against the factors constituting the adjustment models and the quantitative performance of adjustment models are ascertained.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.22 no.6
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• pp.587-596
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• 2009

The fiber bridging model is the crucial factor to predict or analyze the tensile behavior of fiber reinforced cementitious composites. This paper presents the fiber bridging constitutive law considering the distribution of fiber inclined angle and the number of fibers in engineered cementitious composites. The distribution of fiber inclined angle and the number of fibers are measured and analyzed by the image processing technique. The fiber distribution are considerably different from those obtained by assuming two- or three-dimensional random distributions for the fiber inclined angle. The simulation of the uniaxial tension behavior was performed considering the distribution of fiber inclined angle and number of fibers measured by the sectional image analysis. The simulation results exhibit multiple cracking and strain hardening behavior that correspond well with test results.

• The Journal of the Acoustical Society of Korea
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• v.35 no.2
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• pp.83-91
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• 2016

Music-similarity computation is crucial in developing music information retrieval systems for browsing and classification. This paper overviews the recently-proposed centroid-model based music retrieval method and applies the distributional similarity measures to the model for retrieval-performance evaluation. Probabilistic distance measures (also called divergence) compute the distance between two probability distributions in a certain sense. In this paper, we consider the alpha divergence in computing distance between two centroid models for music retrieval. The alpha divergence includes the widely-used Kullback-Leibler divergence and Bhattacharyya distance depending on the values of alpha. Experiments were conducted on both genre and singer datasets. We compare the music-retrieval performance of the distributional similarity with that of the vector distances. The experimental results show that the alpha divergence improves the performance of the centroid-model based music retrieval.

• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.262-271
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• 2002

This study presents a practical procedure for the Bayesian inversion of geophysical data. We have applied geostatistical techniques for the acquisition of prior model information, then the Markov Chain Monte Carlo (MCMC) method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter.