• Journal of Korea Water Resources Association
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• v.43 no.4
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• pp.337-351
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• 2010

There is a growing dissatisfaction with use of conventional statistical methods for the prediction of extreme events. Conventional methodology for modeling extreme event consists of adopting an asymptotic model to describe stochastic variation. However asymptotically motivated models remain the centerpiece of our modeling strategy, since without such an asymptotic basis, models have no rational for extrapolation beyond the level of observed data. Also, this asymptotic models ignored or overestimate the uncertainty and finally decrease the reliability of uncertainty. Therefore this article provide the research example of the extreme rainfall event and the methodology to reduce the uncertainty. In this study, the Bayesian MCMC (Bayesian Markov Chain Monte Carlo) and the MLE (Maximum Likelihood Estimation) methods using a quadratic approximation are applied to perform the at-site rainfall frequency analysis. Especially, the GEV distribution and Gumbel distribution which frequently used distribution in the fields of rainfall frequency distribution are used and compared. Also, the results of two distribution are analyzed and compared in the aspect of uncertainty.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.16 no.3
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• pp.331-337
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• 2018

The decommissioning of nuclear power plants is generally executed in five steps, including preparation, decontamination, cutting/demolition, waste disposal and environmental restoration. So, for efficient decommissioning of nuclear power plants, worker safety, effects compared to cost, minimization of waste, possibility of reuse, etc., shall be considered. Worker safety and measurement technology shall be secured to exert optimal efficiency of nuclear power plant decommissioning work, for which accurate measurement technology for systems and devices is necessary. Typical In-Situ methods for decommissioning of nuclear plants are CZT, Gamma Camera and ISOCS. This study used ISOCS, which can be applied during the decommissioning of a nuclear power plant site without collecting representative samples, to take measurements of the S/G Water Chamber. To validate the measurement values, Microshield and the GEANT4 code was used as the actual method were used for modeling, respectively. The comparison showed a difference of $1.0{\times}10^1Bq$, which indicates that it will be possible to reduce errors due to the influence of radiation in the natural environment and the precision of modeling. Based on the research results of this paper, accuracy and reliability of measurement values will be analyzed and the applicability of the direct measurement method during the decommissioning of NPPs will be assessed.

• Journal of Powder Materials
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• v.31 no.3
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• pp.213-219
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• 2024

Additive Manufacturing (AM) is a process that fabricates products by manufacturing materials according to a three-dimensional model. It has recently gained attention due to its environmental advantages, including reduced energy consumption and high material utilization rates. However, controlling defects such as melting issues and residual stress, which can occur during metal additive manufacturing, poses a challenge. The trial-and-error verification of these defects is both time-consuming and costly. Consequently, efforts have been made to develop phenomenological models that understand the influence of process variables on defects, and mechanical/ electrical/thermal properties of geometrically complex products. This paper introduces modeling techniques that can simulate the powder additive manufacturing process. The focus is on representative metal additive manufacturing processes such as Powder Bed Fusion (PBF), Direct Energy Deposition (DED), and Binder Jetting (BJ) method. To calculate thermal-stress history and the resulting deformations, modeling techniques based on Finite Element Method (FEM) are generally utilized. For simulating the movements and packing behavior of powders during powder classification, modeling techniques based on Discrete Element Method (DEM) are employed. Additionally, to simulate sintering and microstructural changes, techniques such as Monte Carlo (MC), Molecular Dynamics (MD), and Phase Field Modeling (PFM) are predominantly used.

• Proceedings of the IEEK Conference
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• 2000.06b
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• pp.111-114
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• 2000

Single Electron Transistor Logic (SETL) can be characterized by HSPICE simulation using a SPICE macro model. First, One unit SET is characterized by Monte-carlo simulation and then we fit SPICE macro-modeling equations to its characteristics. Second, using this unit SET, we simulate the transient characteristics of two-input NAND gate in both the static and dynamic logic schemes. The dynamic logic scheme shows more stable operation in terms of logic-swing and on/off current ratio. Also, there is a merit that we can use the SET only as current on-off switch without considering the voltage gain.

• Journal of the Korean Society of Visualization
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• v.5 no.1
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• pp.12-15
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• 2007

Macromolecule diffusion in cells and tissues is important for cell signaling, metabolism and locomotion. Biophysical methods, including non-invasive or minimally invasive in-vivo photobleaching techniques and single quantum-dot tracking, have been used to measure the rates of macromolecule diffusion in living cells and tissues, including central nervous system and tumors. Mathematical modeling and statistical analysis of experimental data revealed various modes of diffusion, which are strongly coupled with spatiotemporal changes in nanoscale structures and material properties.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.852-856
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• 2015

Demand side management (DSM) program has been frequently used for reducing the system peak load because it gives utilities and independent system operator (ISO) a convenient way to control and change amount of electric usage of end-use customer. Planning and operating methods are needed to efficiently manage a DSM program. This paper presents a planning method for DSM program. A planning method for DSM program should include an electric load forecasting, because this is the most important factor in determining how much to reduce electric load. In this paper, load forecasting with the temperature stochastic modeling and the sensitivity to temperature of the electric load is used for improving load forecasting accuracy. The proposed planning method can also estimate the required day, hour and total capacity of DSM program using Monte-Carlo simulation. The results of case studies are presented to show the effectiveness of the proposed planning method.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.815-825
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• 2016

Marginalized random effects models (MREMs) are often used to analyze longitudinal categorical data. The models permit direct estimation of marginal mean parameters and specify the serial correlation of longitudinal categorical data via the random effects. However, it is not easy to estimate the random effects covariance matrix in the MREMs because the matrix is high-dimensional and must be positive-definite. To solve these restrictions, we introduce two modeling approaches of the random effects covariance matrix: partial autocorrelation and the modified Cholesky decomposition. These proposed methods are illustrated with the real data from Korean genomic epidemiology study.

• Journal of Integrative Natural Science
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• v.1 no.2
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• pp.67-75
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• 2008

행위자 기초 모형을 이용하여 행위자의 시장에 대한 불균일한 영향력에 대한 연구를 수행하였다. 이때 가중치를 금융시장에서 행위자 간의 공유하는 정보의 영향력의 크기로 사용하였으며 가중치의 크기와 분포가 수익의 변동에 기여하는 것을 관찰하였다. 행위자들의 가중치의 크기가 평균적으로 클수록 가격의 변동의 크기도 같이 증가함을 알 수 있었으며 가중치의 크기뿐만 아니라 가중치의 분포에 따라서도 수익의 분포가 변하게 된다. 이는 신흥시장과 성숙한 시장에서 관찰되는 분포의 차이와 관련하여 유사성을 찾아볼 수 있을 것이라는 가능성을 제공한다. 행위자의 정보의 영향력은 항상 일정하지 않고 그 영향력이 행위자의 시장 예측에 대한 적중률에 따라 변하게 된다. 이렇게 변화하는 행위자들의 정보의 영향력의 분포는 결국 소수의 큰 영향력을 갖는 행위자와 다수의 영향을 거의 끼치지 못하는 행위자들로 분포되게 된다. 그 분포는 초기의 행위자들의 영향력 분포가 어떻게 되었든 간에 충분히 시간이 흐르면 모두 멱법칙을 따르는 분포를 갖게 된다.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05b
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• pp.249-252
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• 2005

Chloride ingress is a common cause of deterioration of reinforced concrete structures. Modeling the chloride ingress is an important basis for designing reinforced concrete structures and for assessing the reliability of an existing structure. The modelling is also needed for predicting the deterioration of a reinforced structure. This paper presents an approach for the probabilistic modeling of the chloride-induced corrosion of reinforcement steel in concrete structures that takes into account the uncertainties in the physical models. The parameters of the models are modeled as random variables and the distribution of the corrosion time and probability of corrosion are determined by using Monte Carlo simulation. The predictions of the proposed model is very effective to do the decision-making about initiation time and deterioration degree.

• Journal of Korean Society for Quality Management
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• v.27 no.1
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• pp.101-110
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• 1999

Products are often evaluated on rating scales to measure and quantify their attributes of interest. In case that one wishes to compare multiple rating datasets simultaneously, there must be a standardized scale with which one can discriminate relative differences among corresponding scale means. In this regard, the concept of Thurstonian modeling applied to various discrimination tests including the triangular method has been recently being reconsidered. In this paper we extend previous researches on the triangular method and evaluate the effect of unequal variances and correlated variables upon the probability of correct response using Monte-Carlo simulation. We observed that the probability of correct response depends on dimensionality, variances, and correlation structure of stimulus sets. But it does not depend on the relative orientation in a multidimensional space.