• Nuclear Engineering and Technology
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• 제46권5호
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• pp.699-706
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• 2014

The probabilistic seismic performance of a standard Korean nuclear power plant (NPP) with an idealized isolation is investigated in the present work. A probabilistic seismic hazard analysis (PSHA) of the Wolsong site on the Korean peninsula is performed by considering peak ground acceleration (PGA) as an earthquake intensity measure. A procedure is reported on the categorization and selection of two sets of ground motions of the Tohoku earthquake, i.e. long-period and common as Set A and Set B respectively, for the nonlinear time history response analysis of the base-isolated NPP. Limit state values as multiples of the displacement responses of the NPP base isolation are considered for the fragility estimation. The seismic risk of the NPP is further assessed by incorporation of the rate of frequency exceedance and conditional failure probability curves. Furthermore, this framework attempts to show the unacceptable performance of the isolated NPP in terms of the probabilistic distribution and annual probability of limit states. The comparative results for long and common ground motions are discussed to contribute to the future safety of nuclear facilities against drastic events like Tohoku.

• 산업경영시스템학회지
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• 제30권3호
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• pp.103-108
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• 2007

This paper presents the conceptual framework for estimating and predicting system's susceptibility to failure as function of condition parameter value which is representing the current status of performance measure using on-line performance reliability. The performance of such system depends on one parameter with a probability distribution that degrades with time gracefully. Performance reliability represents the probability that physical performance will remain satisfactory over a finite period of time or usage cycles in the future. An empirical physical performance function is constructed to incorporate explanatory variables (operating and environmental conditions) over a time or usage dimension. This function enables one to model device performance and the associated classical reliability measures simultaneously, in the performance domain and time domain. The conditional performance reliability structure developed represents a tool to predict system performance over time or usage for next usage period. By enabling such a framework, it can bring us more efficient planning and execution in system's operation control as well as maintenance to reduce costs and/or increase profits.

• The Journal of Asian Finance, Economics and Business
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• 제7권11호
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• pp.251-257
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• 2020

The study investigates the role of commodity prices and tax purpose recognition on bitcoin prices. Since the introduction of bitcoin in 2008, emphasis has focused on economists, policy-makers and analysts drastically increasing bitcoin's accessibility and commodity values (Dumitrescu & Firică, 2014). This study employs GARCH and EGARCH from ARCH/GARCH family on daily nature data. We measure the volatile behavior of bitcoin by employing auto-regressive conditional heteroscedasticity model with the aim to explore the relationship between major commodities and bitcoin volatility. We focus on major commodities like gold, silver, platinum, and crude oil to be regressed with bitcoin. The daily prices of commodities were retrieved from www.investing.com and bitcoin prices from www.coindesk.com for the period from 29April 2013 to 16 October 2018. Results confirmed the currency's long-term volatile behavior, which is due to its composition and market dynamics, whereas the existence of asymmetric information effect is not confirmed. Tax recognition by other countries may in future help in controlling the volatility as bitcoin is not a country-specific security. But, only silver impacts on volatility in comparison to oil prices and platinum, which is due to its similar features with gold. Eventually, bitcoin can be used for risk diversification and money making.

• The Journal of Asian Finance, Economics and Business
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• 제7권11호
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• pp.83-93
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• 2020

This empirical research aims to modeling and improving the forecasting accuracy of the volatility pattern by employing the Saudi Arabia stock market (Tadawul)by studying daily closed price index data from October 2011 to December 2019 with a number of observations being 2048. In order to achieve significant results, this study employs many mathematical functions which are non-linear spectral model Maximum overlapping Discrete Wavelet Transform (MODWT) based on the best localized function (Bl14), autoregressive integrated moving average (ARIMA) model and generalized autoregressive conditional heteroskedasticity (GARCH) models. Therefore, the major findings of this study show that all the previous events during the mentioned period of time will be explained and a new forecasting model will be suggested by combining the best MODWT function (Bl14 function) and the fitted GARCH model. Therefore, the results show that the ability of MODWT in decomposition the stock market data, highlighting the significant events which have the most highly volatile data and improving the forecasting accuracy will be showed based on some mathematical criteria such as Mean Absolute Percentage Error (MAPE), Mean Absolute Scaled Error (MASE), Root Means Squared Error (RMSE), Akaike information criterion. These results will be implemented using MATLAB software and R- software.

• Journal of the Korean Data and Information Science Society
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• 제10권1호
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• pp.29-36
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• 1999

분할표 분석에서 승산비 (odds ratio)에 대한 추론은 중요하다. 이에 대한 정확한 추론은 비중심초기하(noncentral hypergeometric) 분포의 누적확률등의 계산이 요구되어 표본의 크기가 클 경우 많은 양의 계산과 계산시간이 요구되므로 StatXact 등의 프로그램을 이용하는 것이 일반적이다. 본 논문에서는 정확한 추론에 대한 대안적 방법으로 안부점 근사(saddlepoint approximation)의 결과를 이용한 점근적 추론법을 제시하였다. 이 방법은 비교적 소표본의 경우에도 정확한 추론의 결과와 일치하며, 기존의 정규근사를 이용한 방법에 비해 매우 뛰어난 정확도를 유지함을 예제를 통해 확인하였다.

• 한국전산구조공학회논문집
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• 제23권6호
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• pp.641-649
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• 2010

Estimation of uncertain parameters is required in many engineering problems which involve probabilistic structural analysis as well as prognosis of existing structures. In this case, Bayesian framework is often employed, which is to represent the uncertainty of parameters in terms of probability distributions conditional on the provided data. The resulting form of distribution, however, is not amenable to the practical application due to its complex nature making the standard probability functions useless. In this study, Markov chain Monte Carlo (MCMC) method is proposed to overcome this difficulty, which is a modern computational technique for the efficient and straightforward estimation of parameters. Three case studies that implement the estimation are presented to illustrate the concept. The first one is an inverse estimation, in which the unknown input parameters are inversely estimated based on a finite number of measured response data. The next one is a metamodel uncertainty problem that arises when the original response function is approximated by a metamodel using a finite set of response values. The last one is a prognostics problem, in which the unknown parameters of the degradation model are estimated based on the monitored data.

• 한국정보전자통신기술학회논문지
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• 제11권2호
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• pp.169-174
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• 2018

일반적으로 자료의 효과 연속형인 경우 분산분석과 이산형인 경우 분할표 카이제곱 검정을 통계적 분석방법으로 사용한다. 다차원의 자료에서는 계층적 구조의 분석이 요구되어지며 자료간의 인과관계를 나타내기 위해 통계적 선형모형을 채택하여 분석한다. 선형모형의 구조에서는 자료의 정규성이 요구되어지며 일부 자료에서는 비 선형모형을 채택할 수도 있다. 특히, 설문조사 자료 구조는 문항의 특성상 이산형 자료의 형태가 많아 모형의 조건에 만족하지 않는 경우가 종종 발생한다. 자료구조의 차원이 높아질수록 인과관계, 교호작용, 연관성분석 등에 다차원 범주형 자료 분석 방법을 사용한다. 본 논문에서는 확률분포의 계산을 이용한 베이지안 네트워크 모형이 범주형 자료 분석에서 분석절차를 줄이고 교호작용 및 인과관계를 분석할 수 있다는 것을 제시하였다.

• 응용통계연구
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• 제11권2호
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• pp.377-387
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• 1998

컴퓨터의 발전에 따른 마코브체인 몬테카를로방법을 소프트웨어 신뢰확률모형에 이용하였다. 베이지안 추론에서 조건부분포를 가지고 사후분포를 결정하는데 있어서의 계산문제와 이론적인 정당성을 고려, 마코프연쇄와 메트로폴리스방법의 관계를 고찰하였으며, 특히 Mus-Okumoto와 Erlang(2)의 중첩모형에 대하여 깁스샘플링 알고리즘과 메트로폴리스 알고리즘을 활용하며 베이지안 계산과 예측 우도기준에 의 한 모형선택을 제안하고 Cox-Lewis에 의해 계시된 Thing method를 이용한 모의실험자료를 이용하여 수치적인 계산을 시행하고 그 결과가 제시되었다.

• International Journal of Internet, Broadcasting and Communication
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• 제11권4호
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• pp.37-42
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• 2019

In this paper, we explore the details of three classic data augmentation methods and two generative model based oversampling methods. The three classic data augmentation methods are random sampling (RANDOM), Synthetic Minority Over-sampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN). The two generative model based oversampling methods are Conditional Generative Adversarial Network (CGAN) and Wasserstein Generative Adversarial Network (WGAN). In imbalanced data, the whole instances are divided into majority class and minority class, where majority class occupies most of the instances in the training set and minority class only includes a few instances. Generative models have their own advantages when they are used to generate more plausible samples referring to the distribution of the minority class. We also adopt CGAN to compare the data augmentation performance with other methods. The experimental results show that WGAN-based oversampling technique is more stable than other approaches (RANDOM, SMOTE, ADASYN and CGAN) even with the very limited training datasets. However, when the imbalanced ratio is too small, generative model based approaches cannot achieve satisfying performance than the conventional data augmentation techniques. These results suggest us one of future research directions.

• The Journal of Asian Finance, Economics and Business
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• 제8권5호
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• pp.913-921
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• 2021

The objective of the study is to investigate the impact of the COVID-19 pandemic on Saudi Arabia stock market. The study relied on the data of the daily closing stock market price index Tadawul All Share Index (TASI), and the number of daily cases infected with COVID-19 during the period from March 15, 2020, to August 10, 2020. The study employs the Vector Auto-Regressive (VAR) model, the Impulse Response Function (IRF) and Autoregressive Conditional Heteroscedasticity (ARCH) models. The results of the correlation matrix and the Impulse Response Function (IRF) show that stock market returns responded negatively to the growth in COVID-19 infected cases during the pandemic. The results of ARCH model confirmed the negative impact of COVID-19 pandemic on KSA stock market returns. The results also showed that the negative market reaction was strong during the early days of the COVID-19 pandemic. The study concluded that stock market in KSA responded quickly to the COVID-19 pandemic; the response varies over time according to the stage of the pandemic. However, the Saudi government's response time and size of the stimulus package have played an important role in alleviating the impacts of the COVID-19 pandemic on Saudi Arabia Stock Market.