• The Journal of the Korea institute of electronic communication sciences
• /
• v.14 no.4
• /
• pp.781-786
• /
• 2019

We should give a realistic value on the large amounts of relevant data obtained from these studies to achieve effective objectives of the disease study which is dealing with various vital phenomenon today. In this paper, the proposed MKSV algorithm is estimated by optimal probability distribution, and the input pattern is determined. After classifying it into data mining, it is possible to obtain efficient computational quantity and recognition rate. MKSV algorithm is useful for studying the relationship between disease and gene in the present society by simulating the probabilistic flow of gene data and showing fast and effective performance improvement to classify data through the data mining process of big data.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2016.05a
• /
• pp.546-546
• /
• 2016

본 연구에서는 모의 강우 및 실제 발생된 강우 자료를 이용하여 우수관망도의 신뢰도와 위험도를 산출하였다. 모의 강우의 경우 과거 강우자료를 바탕으로 Markov Chain을 이용하여 모의강우발생을 하였으며, 과거 강우자료의 경우 IETD분석을 통해 대표 강우사상을 선정하였다. 또한, 우수관망도의 신뢰도와 위험도를 정량적으로 분석하기 위해 이정호(2012)에서 개발한 방법론을 이용하여 분석하였다. 또한, 우수관망의 경우 강우의 특성에 따라 발생되는 월류특성이 달라질수 있어 본 연구에서는 산정된 대표 강우사상을 그룹화 하여 그룹화된 강우 특성에 따른 위험도를 평가하였다. 우수관망의 경우 배수분구에서 발생되는 내수침수를 방어하기 위해 빗물펌프장이 운영되어 지고 있으며 본 연구에서 빗물펌프장의 운영수위 조건을 시나리오 제작하여 빗물펌프장 운영 수위에 따른 침수저감효과를 분석하였다. 운영수위 조건에 따라 가장 큰 침수저감효과를 보이는 시나리오와 현재운영수위의 월류량을 침수면적으로 변환하기 위해 서울안저누리에서 제공하는 침수흔적도를 이용하여 침수면적으로 변환하였다. 가장 큰 침수저감효과를 보이는 시나리오를 적용하여 침수면적을 산정한 결과 기존의 침수면적에 비해 5.20%의 침수면적이 감소하였다. 본 연구의 결과를 바탕으로 다양한 강우조건 및 펌프의 운영조건을 조합롭게 활용한다면 향후 비구조적 침수저감에 도움을 줄 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2017.05a
• /
• pp.150-150
• /
• 2017

기후변화로 인하여 극한 홍수와 극한 가뭄 발생이 증가할 것으로 전망하고 있어 이에 대한 위험이 대두되고 있는 실정이다. 홍수 및 가뭄 수문시계열의 빈도해석시에 일반적으로 활용되는 정상성 빈도해석기법은 수문자료의 정상성을 기반으로 한 빈도해석이 대부분이기 때문에 기후변화 및 수문자료의 비정상성을 반영한 새로운 빈도해석 기법이 요구되고 있는 상황이다. 본 연구에서는 5개의 대표 관측지점(서울, 포항, 추풍령, 여수, 광주)를 선별하고 1976년부터 2015년까지 일강우자료를 활용하여 기상학적 가뭄지수인 SPI(Standardized Precipitation Index)를 산정하였다. 산정한 SPI의 경향성을 Mann-Kendall 분석을 하였으며, 정상성 및 비정상성 빈도해석을 위하여 최적확률분포로 선정된 GEV 분포 적용하였다. 본 연구에서는 가뭄빈도해석을 위하여 SPI를 입력자료로 활용하였으며, 산정된 SPI의 비정상성을 반영한 비정상성 빈도해석의 경우 Bayesian 모형을 기반으로 한 MCMC(Markov Chain Monte Carlo) 모의를 이용하여 극치분포의 사후분포 매개변수를 추정하였다. 추정 값을 바탕으로 하여 가뭄의 관측소별 빈도해석을 실시하였고 재현기간별-지속기간별 가뭄심도를 추정하여 관측소별 가뭄심도-지속기간-빈도(SDF,Severity-Duration-Frequency) 곡선을 유도하였다. 본 연구를 통하여 정상성과 비정상성 빈도해석 결과의 비교연구를 수행하였으며 기후변화에 따른 비정상 시계열로 구성된 가뭄빈도해석에 매우 유용하게 적용될 수 있을 것으로 나타났다.

• The Journal of Asian Finance, Economics and Business
• /
• v.8 no.4
• /
• pp.665-672
• /
• 2021

The paper examines the impact of shadow economy and corruption, along with public expenditure, trade openness, foreign direct investment (FDI), inflation, and tax revenue on the economic growth of the BRICS countries. Data were collected from the World Bank, Transparency International, and Heritage Foundation over the 1991-2017 period. The Bayesian linear regression method is used to examine whether shadow economy, corruption and other indicators affect the economic growth of countries studied. This paper applies the normal prior suggested by Lemoine (2019) while the posterior distribution is simulated using Monte Carlo Markov Chain (MCMC) technique through the Gibbs sampling algorithm. The results indicate that public expenditure and trade openness can enhance the BRICS countries' economic growth, with the positive impact probability of 75.69% and 67.11%, respectively. Also, FDI, inflation, and tax revenue positively affect this growth, though the probability of positive effect is ambiguous, ranging from 51.13% to 56.36%. Further, the research's major finding is that shadow economy and control of corruption have a positive effect on the economic growth of the BRICS countries. Nevertheless, the posterior probabilities of these two factors are 62.23% and 65.25%, respectively. This result suggests that their positive effect probability is not high.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.21 no.6
• /
• pp.101-108
• /
• 2011

The turnover rate of information security professionals in Korea is over 10% and turnover into non-information security fields accounts for over 50% of all the turnovers [1]. It is not only important to recruit a new quality workforce, but also to make the current workforce perform satisfactorily, to improve their performance, and eventually to attain information security objectives. This study proposes a Markov chain model for the turnover process of information security professionals and forecasts the job duty composition of information security professionals. The results of this study can be applied to secure the justification of government policies for the promotion of information security professionals.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.2
• /
• pp.363-370
• /
• 2022

The study aims to probe the impact of foreign ownership on Vietnamese listed firms' capital structure. This study employs panel data of 288 non-financial firms listed on the Ho Chi Minh City stock exchange (HOSE) and Ha Noi stock exchange (HNX) in 2015-2019. In this research, we applied a Bayesian linear regression method to provide probabilistic explanations of the model uncertainty and effect of foreign ownership on the capital structure of non-financial listed enterprises in Vietnam. The findings of experimental analysis by Bayesian linear regression method through Markov chain Monte Carlo (MCMC) technique combined with Gibbs sampler suggest that foreign ownership has substantial adverse effects on the firms' capital structure. Our findings also indicate that a firm's size, age, and growth opportunities all have a strong positive and significant effect on its debt ratio. We found that the firms' profitability, tangible assets, and liquidity negatively and strongly affect firms' capital structure. Meanwhile, there is a low negative impact of dividends and inflation on the debt ratio. This research has ramifications for business managers since it improves a company's financial resources by developing a strong capital structure and considering foreign investment as a source of funding.

• Communications for Statistical Applications and Methods
• /
• v.30 no.2
• /
• pp.163-178
• /
• 2023

GARCH-X(1, 1) model specifies that conditional variance follows an AR(1) process and includes a past exogenous variable. This study proposes a new class from that model by allowing a more general (non-linear) variance function to follow an AR(1) process. The functions applied to the variance equation include exponential, Tukey's ladder, and Yeo-Johnson transformations. In the framework of normal and student-t distributions for return errors, the empirical analysis focuses on two stock indices data in developed countries (FTSE100 and SP500) over the daily period from January 2000 to December 2020. This study uses 10-minute realized volatility as the exogenous component. The parameters of considered models are estimated using the adaptive random walk metropolis method in the Monte Carlo Markov chain algorithm and implemented in the Matlab program. The 95% highest posterior density intervals show that the three transformations are significant for the GARCHX(1, 1) model. In general, based on the Akaike information criterion, the GARCH-X(1, 1) model that has return errors with student-t distribution and variance transformed by Tukey's ladder function provides the best data fit. In forecasting value-at-risk with the 95% confidence level, the Christoffersen's independence test suggest that non-linear models is the most suitable for modeling return data, especially model with the Tukey's ladder transformation.

• Communications for Statistical Applications and Methods
• /
• v.31 no.5
• /
• pp.497-519
• /
• 2024

In reliability analysis, the probability P(Y < X) is significant because it denotes availability and dependability in a stress-strength model where Y and X are the stress and strength variables, respectively. In reliability theory, the inverse Lomax distribution is a well-established lifetime model, and the literature is developing inference techniques for its reliability attributes. In this article, we are interested in estimating the stress-strength reliability R = P(Y < X), where X and Y have an unknown common scale parameter and follow the inverse Lomax distribution. Using Bayesian and non-Bayesian approaches, we discuss this issue when both stress and strength are expressed in terms of lower record values. The parametric bootstrapping techniques of R are taken into consideration. The stress-strength reliability estimator is investigated using uniform and gamma priors with several loss functions. Based on the proposed loss functions, the reliability R is estimated using Bayesian analyses with Gibbs and Metropolis-Hasting samplers. Monte Carlo simulation studies and real-data-based examples are also performed to analyze the behavior of the proposed estimators. We analyze electrical insulating fluids, particularly those used in transformers, for data sets using the stress-strength model. In conclusion, as expected, the study's results showed that the mean squared error values decreased as the record number increased. In most cases, Bayesian estimates under the precautionary loss function are more suitable in terms of simulation conclusions than other specified loss functions.

• Journal of Korean Society on Water Environment
• /
• v.28 no.1
• /
• pp.38-49
• /
• 2012

In this study, we analyze statistical characteristics of influent water quality in Daejeon waste water treatment plant and apply a stochastic model for data generation. In the analysis, the influent water quality data from year 2003 to 2008, except for year 2006, are used. Among water quality variables, we find strong correlations between BOD and T-N; T-N and T-P; BOD and T-P; $COD_{Mn}$ and T-P; and BOD and $COD_{Mn}$. We also find that different water quality variables follow different theoretical probability distribution functions, which also depends on whether the seasonal cycle is removed. Finally, we generate the influent water quality data using the multi-season 1st Markov model (Thomas-Fiering model). With model parameters calibrated for the period 2003~2005, the generated data for 2007~2008 are well compared with observed data showing good agreement in general. BOD and T-N are underestimated by the stochastic model. This is mainly due to the statistical difference in observed data itself between two periods of 2003~2005 and 2007~2008. Therefore, we expect the stochastic model can be applied with more confidence in the case that the data follows stationary pattern.

• The Journal of the Korea Contents Association
• /
• v.10 no.8
• /
• pp.104-110
• /
• 2010

In the field of biotechnology, computer can play varied roles such as the ordinal analysis, ordianl comparison, nutation tracing, analogy comparison for drug design, estimation of protein function, cell mechanism, and verifying the role of a gene for preventing diseases. Additionally, by constructing database, it can provide an application for the cloning process in other data researches, and be used as a basis for the comparative genetics. For the most of researcher about biotechnology, they need to use the tool that can do all of job above. This study is focused on looking into problems of existing systems to analysis bio data, and designing an improved analyzing system that can propose a solution. In additional, it has been considered to improve the performance of each constituent, and all the constituents, which have been separately processed, are combind in a single system to get over old problems of the existing system.