• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.119-133
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• 1999

A Markov Chain Monte Carlo method is developed to compute the software reliability model. We consider computation problem for determining of posterior distibution in Bayseian inference. Metropolis algorithms along with Gibbs sampling are proposed to preform the Bayesian inference of the Mixed model with record value statistics. For model determiniation, we explored the prequential conditional predictive ordinate criterion that selects the best model with the largest posterior likelihood among models using all possible subsets of the component intensity functions. To relax the monotonic intensity function assumptions. A numerical example with simulated data set is given.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.645-652
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• 2016

The statistical modeling of input random variables is necessary in reliability analysis, reliability-based design optimization, and statistical validation and calibration of analysis models of mechanical systems. In statistical modeling methods, there are the Akaike Information Criterion (AIC), AIC correction (AICc), Bayesian Information Criterion, Maximum Likelihood Estimation (MLE), and Bayesian method. Those methods basically select the best fitted distribution among candidate models by calculating their likelihood function values from a given data set. The number of data or parameters in some methods are considered to identify the distribution types. On the other hand, the engineers in a real field have difficulties in selecting the statistical modeling method to obtain a statistical model of the experimental data because of a lack of knowledge of those methods. In this study, commonly used statistical modeling methods were compared using statistical simulation tests. Their advantages and disadvantages were then analyzed. In the simulation tests, various types of distribution were assumed as populations and the samples were generated randomly from them with different sample sizes. Real engineering data were used to verify each statistical modeling method.

• The Korean Journal of Applied Statistics
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• v.31 no.4
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• pp.497-505
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• 2018

Attacking computer systems using ransomware is very common all over the world. Since antivirus and detection methods are constantly improved in order to detect and mitigate ransomware, the ransomware itself becomes equally better to avoid detection. Several new methods are implemented and tested in order to optimize the protection against ransomware. In our work, 582 of ransomware and 942 of normalware sample data along with 30,967 dynamic action sequence variables are used to detect ransomware efficiently. Several variable selection techniques combined with various machine learning based classification techniques are tried to protect systems from ransomwares. Among various combinations, chi-square variable selection and random forest gives the best detection rates and accuracy.

• Industry Promotion Research
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• v.7 no.4
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• pp.1-8
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• 2022

There are many one-shot devices that are used once and thrown away. One-shot devices such as firecrackers and ammunition are typical, and they are stored for a while after manufacture and then disposed of after use when necessary. However, unlike general operating systems, these one-shot devices have not been properly evaluated. This study first examines what the government does to secure reliability in the case of ammunition through ammunition stockpile reliability program. Next, in terms of statistical analysis, we show what the reliability analysis methods are for one-shot devices such as ammunition. Specifically, we show that it is possible to know the level of reliability if sampling inspection plan such as KS Q 0001 which is acceptance sampling plan by attributes is used. Next, non-parametric and parametric methods are introduced as ways to determine the storage reliability of ammunition. Among non-parametric methods, Kaplan-Meier method can be used since it can also handle censored data. Among parametric methods, Weibull distribution can be used to determine the storage reliability of ammunition.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.189-189
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• 2015

본 연구는 기후변화의 영향을 고려한 포아송 강우생성모형의 일종인 MBLRP(Modified Bartlett-Lewis Rectangular Pulse)를 개발하고, 대한민국 주요 도시에 대해 향후 100년간 강우의 변화를 살펴보았다. 기존 MBLRP 모형에서 기후변화에 따른 강우량 변화를 고려할 수 있도록 GCM 모형의 강우 자료를 활용하였고, GCM 모형으로부터 발생하는 불확실성을 고려하기 위해 IPCC의 RCP(Representative Concentration Pathways) 시나리오를 모의한 16개의 GCM 모형을 사용하였다. 2007년부터 2099년까지의 미래기간을 3개의 시 구간으로 구분하고, 16개 GCM 앙상블을 사용하여 미래기간 동안 대한민국 16개 도시에 대해 1000개의 샘플을 BWA 방법을 이용하여 생성하였다. 제어기간(1973-2005) 대비 미래기간(2007-2099)의 변화율을 나타내는 FOC(factor of change)와 온도의 연별 변화율을 나타내는 SF(scaling factor)의 개념을 결합하여 미래기간에 대한 CF(correction factor)를 산정하였다. 이때 CF는 16개 도시의 연 단위 강우량 변화 비율을 월별로 나타내며, 제어기간의 월 강우 관측치와 CF를 몬테카를로 모의를 실시하여 미래기간의 강우 시나리오를 산정한다. 이를 통해 월 평균 강우량 통계치를 연 단위로 얻을 수 있으며, 월 평균 강우량이 월 평균 분산, 무강우확률, 자기상관계수와 가지는 선형 관계를 통해 강우 통계치를 산출한다. 이와 같은 강우 통계치는 가상강우생성모형인 MBLRP 모형에 입력 자료로 활용되어 월 강우량을 시 단위의 강우 시계열 자료로 생성해낸다. 최종적으로 MBLRP 모형으로 산정된 시 단위 강우 시계열은 기후변화 영향을 고려한 GCMs 앙상블로 생성된 강우 시나리오를 기반으로 산출되기 때문에 향후 수자원 분석에 활용 가능할 것이라 기대된다.

• Communications for Statistical Applications and Methods
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• v.19 no.2
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• pp.237-246
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• 2012

Bayesian methods have been recently used to identify multiple change-points. However, the studies for small data are limited. This paper suggests the Bayesian noncentral t distribution change-point model for small data, and applies the Metropolis-Hastings-within-Gibbs Sampling algorithm to the proposed model. Numerical results of simulation and real data show the performance of the new model in terms of the quality of the resulting estimation of the numbers and positions of change-points for small data.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.286-292
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• 2008

RTLS 시스템은 이동 객체에 RTLS 태그를 부착한 후 태그에서 발산되는 신호를 이용하여 실시간으로 위치를 파악하는 시스템으로 최근 항만 물류 및 자산 관리 분야에서 객체의 실시간 위치를 파악하기 위해 활용되고 있다. RTLS 시스템은 태그의 위치를 측정하기 위해 삼각 측량 법이나, Proximity matching법을 사용한다. 삼각 측량법은 3개 이상의 리더에서 수신된 신호 세기나 신호의 도달 시간을 이용하여 삼각측량 방식으로 위치를 결정하는 알고리즘으로, 전파의 난반사나 장애물등에 민감하며, Proximity matching법은 위치 샘플링 값에 대한 근접성을 이용한 통계 정보를 바탕으로 하여 위치를 결정하는 알고리즘으로 위치 정확도를 높일 수 있으나, 샘플링 데이터 개수에 따라 정확도가 크게 변화하는 문제가 있다. 본 논문에서는 이러한 위치 정보의 오차를 줄이기 위하여, Fingerprint 방식의 확률 모델에 TDOA 방식에서 사용되는 요소들을 혼합하여 확률에 의한 불확실성을 줄이고 더 높은 정확도의 위치 정보를 전달하는 위치 보정 기법을 제안한다. 본 논문에서 제안하는 2단계 위치 보정 기법은 먼저, Fingerprint 데이터 셋으로부터 현재 측정된 위치의 신호정보를 이용한 확률 모델을 적용하여 단 하나의 후보자를 결정한다. 둘째, 측정된 정보와 후보자 위치 정보를 기반으로 TDOA에서 사용하는 기하학적 위치 결정 방법을 변형한 알고리즘을 이용해 측정된 위치를 보정함으로써, TDOA 방식이나, Fingerprint 방식 둘 중 하나만 사용하는 것보다 향상된 위치의 정확도를 제공한다. 그리고 본 논문에서는 제안한 위치 보정 기법을 위한 위치 보정 모듈을 설계하였으며, RTLS 미들웨어에 이를 반영하여 구현하였다.

• Journal of the korean academy of Pediatric Dentistry
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• v.37 no.4
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• pp.422-428
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• 2010

Giomer is fluoride-releasing, resin-based dental materials that comprise PRG(pre-reacted glass ionomer) filler. The purpose of this study was to evaluate the shear bond strength of Giomer using self-etching primer systems to bovine dentin. Bovine incisors were mounted in self-curing orthodontic resin and the facial surfaces were wet ground on SIC paper to expose the dentin. Total 100 samples were made and divided randomly into 4 groups, Giomer group(I), Composite resin group(II) and Compomer group(III), Giomer and single bottle adhesive group(IV). The shear bond strengths of 25 samples per each group were measured using universal testing machine. And data were analyzed statistically with One-way ANOVA and Scheffe test. Giomer group(I) showed the significantly higher bond strength than Compomer group(III)(p<0.05). There was no significant difference between Giomer group(I) and Composite resin group(II)(p>0.05). And there is no significant difference between gourp(I) and group(IV). Based on the results of present study, the use of Giomer as an esthetic restorative material for primary teeth might be justified. It is considered that more study about the fluoride releasing ability is needed to evaluate the anticariogenic effect of giomer.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.781-792
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• 2009

At an early stage of genomic investigations, a small sample of microarrays is used in gene expression experiments to identify small subsets of candidate genes for a further accurate investigation. Unlike the statistical analysis methods for a large sample of microarrays, an appropriate statistical method for identifying small subsets is a randomization test that provides exact P values. These exact P values from a randomization test for a small sample of microarrays are discrete. The possible existence of differentially expressed genes in the sample of a full set of genes can be tested for the null hypothesis of a uniform distribution. Subsets of smaller P values are of prime interest for a further accurate investigation and identifying these outlier cells from a multinomial distribution of P values is possible by M test of Fuchs et al. (1980). Above all, the genome-wide gene expressions in microarrays are correlated, but the majority of statistical analysis methods in the microarray analysis are based on an independence assumption of genes and ignore the possibly correlated expression levels. We investigated with simulation studies the effect that correlated gene expression levels could have on the randomization test results and M test results, and found that the effects are often not ignorable.

• Journal of the Korean earth science society
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• v.35 no.1
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• pp.69-87
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• 2014

This paper suggests a conditional simulation framework based on multiple data transformations for geostatistical simulation of compositional data. First, log-ratio transformation is applied to original compositional data in order to apply conventional statistical methodologies. As for the next transformations that follow, minimum/maximum autocorrelation factors (MAF) and indicator transformations are sequentially applied. MAF transformation is applied to generate independent new variables and as a result, an independent simulation of individual variables can be applied. Indicator transformation is also applied to non-parametric conditional cumulative distribution function modeling of variables that do not follow multi-Gaussian random function models. Finally, inverse transformations are applied in the reverse order of those transformations that are applied. A case study with surface sediment compositions in tidal flats is carried out to illustrate the applicability of the presented simulation framework. All simulation results satisfied the constraints of compositional data and reproduced well the statistical characteristics of the sample data. Through surface sediment classification based on multiple simulation results of compositions, the probabilistic evaluation of classification results was possible, an evaluation unavailable in a conventional kriging approach. Therefore, it is expected that the presented simulation framework can be effectively applied to geostatistical simulation of various compositional data.