• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.19 no.3
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• pp.367-386
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• 2021

The overestimation and underestimation of the radioactivity concentration of difficult-to-measure radionuclides can occur during the implementation of the scaling factor (SF) method because of the uncertainties associated with sampling, radiochemical analysis, and application of SFs. Strict regulations ensure that the SF method as an indirect method does not underestimate the radioactivity of nuclear wastes; however, there are no clear regulatory guidelines regarding the overestimation. This has been leading to the misuse of the SF methodology by stakeholders such as waste disposal licensees and regulatory bodies. Previous studies have reported instances of overestimation in statistical implementation of the SF methodology. The analysis of the two most popular linear models of the SF methodology showed that severe overestimation may occur and radioactivity concentration data must be dealt with care. Since one major source of overestimation is the use of minimum detectable activity (MDA) values as true activity values, a comparative study of instrumental techniques that could reduce the MDAs was also conducted. Thermal ionization mass spectrometry was recommended as a suitable candidate for the trace level analysis of long-lived beta-emitters such as iodine-129. Additionally, the current status of the United States and Korea was reviewed from the perspective of overestimation.

• Journal of IKEEE
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• v.26 no.3
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• pp.355-363
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• 2022

Korea is pursuing a plan to switch and expand energy sources with a focus on renewable energy with the goal of becoming carbon neutral by 2050. As the instability of energy supply increases due to the intermittent nature of renewable energy, accurate prediction of the amount of renewable energy generation is becoming more important. Therefore, the government has opened a small-scale power brokerage market and is implementing a system that pays settlements according to the accuracy of renewable energy prediction. In this paper, a prediction model was implemented using a statistical model and an artificial intelligence model for the prediction of solar power generation. In addition, the results of prediction accuracy were compared and analyzed, and the revenue from the settlement amount of the renewable energy generation forecasting system was estimated.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.457-480
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• 2017

We develop a random partition procedure based on a Dirichlet process prior with Laplace distribution. Gibbs sampling of a Laplace mixture of linear mixed regressions with a Dirichlet process is implemented as a random partition model when the number of clusters is unknown. Our approach provides simultaneous partitioning and parameter estimation with the computation of classification probabilities, unlike its counterparts. A full Gibbs-sampling algorithm is developed for an efficient Markov chain Monte Carlo posterior computation. The proposed method is illustrated with simulated data and one real data of the energy efficiency of Tsanas and Xifara (Energy and Buildings, 49, 560-567, 2012).

• The Korean Journal of Applied Statistics
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• v.8 no.1
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• pp.39-50
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• 1995

This article deals with the problem that all the statistical clustering methods do not supply the clustering rule after the analysis. We modify the Coulomb Energy Network model basically developed in physics and suggest one model appropriate for our purpose and show the implementation using an actual data. Finally the method suggested is compared with one of the well known methods, K-means algorithm using Rand C.

• Wind and Structures
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• v.31 no.5
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• pp.419-430
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• 2020

Weibull distribution is a conspicuous distribution known for its accuracy and its usage for wind energy analysis. The two and three parameter Weibull distributions are adopted in this study to fit wind speed data. The daily mean wind speed data of Ennore, Tamil Nadu, India has been used to validate the procedure. The parameters are estimated using maximum likelihood method, least square method and moment method. Four statistical tests namely Root mean square error, R2 test, Kolmogorov-Smirnov test and Anderson-Darling test are employed to inspect the fitness of Weibull probability density functions. The value of shape factor, scale factor, wind speed and wind power are determined at a height of 100m using extrapolation of numerical equations. Also, the value of capacity factor is calculated mathematically. This study provides a way to evaluate feasible locations for wind energy assessment, which can be used at any windy site throughout the world.

• The Bulletin of The Korean Astronomical Society
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• v.36 no.2
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• pp.108.1-108.1
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• 2011

Turbulence is ubiquitous in astrophysical fluids such as the interstellar medium(ISM) and the intracluster medium(ICM). There are many driving mechanisms which can inject energy into the fluid in variety driving scales, But the plausible driving scale of ISM/ICM turbulence are yet unknown. Therefore, understanding different statistical properties between turbulence with single driving scale and turbulence with double driving scale is required. In this work, we performed 3-dimensional isothermal compressible, magnetohydrodynamic(MHD) turbulence simulations. We drive turbulence in the Fourier space in two ranges, 2

• Journal of the Korean Society for Precision Engineering
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• v.15 no.9
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• pp.15-24
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• 1998

In this research. we have proposed a new technique to determine .the combination of design parameters for the process design of a hot forged product using artificial neural network(ANN) and statistical design of experiments(DOE). The investigated problem involves the adequate selection of the aspect ratio of billet, the ram velocity and the friction factor as design parameters. An optimal billet satisfying the forming limitation, die filling, load and energy as well as more uniform distribution of effective strain, is determined by applying the ability of artificial neural network and considering the analysis of mean and variation on the functional requirement. This methodology will be helpful in designing and controlling parameters on the shop floor which would yield the best design solution.

• Nuclear Engineering and Technology
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• v.8 no.4
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• pp.219-229
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• 1976

A statistical approach is employed to investigate the relative advantages of several alternative fuel cycles suitable for a hypothetical 1125 MWe plant in Korea. All the fuel cost parameters are treated as statistical variables, each being associated with an appropriate probability distribution function. Through a random sampling procedure, the probability histograms on both capital requirements and break-even costs of various fuel cycle components are obtained. The histograms are then utilized to quantify the cost-benefit of the fuel cycle with reprocessing or the plutonium recycle over the throwaway cycle.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.6 no.4
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• pp.257-263
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• 2008

We analyzed the statistical properties for the conductive background fractures in the Low and Intermediate Level Waste(LILW) disposal site to conceptualize of its groundwater flow system. The background fractures were classified to fracture sets based on their trends and plunges that were obtained from the borehole logging data, and then the fracture transmissivity distribution was inferred from the fixed interval hydraulic test results. The fracture size distribution of each fracture set was estimated using the fracture density and fracture mapping data. To verify the analyzed results, we compared observed field data to simulated one from the DFN model that was constructed with the analyzed statistical properties of the background fractures, and they showed a good agreement.

• KIEAE Journal
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• v.14 no.2
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• pp.3-10
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• 2014

Impact by estimation error of hourly horizontal global solar radiation in a weather file on building energy performance was investigated in this study. There are a number of weather parameters in a given weather file, such as dry-bulb, wet-bulb, dew-point temperatures; wind speed and direction; station pressure; and solar radiation. Most of them except for solar radiation can be easily obtained from weather stations located on the sites worldwide. However, most weather stations, also including the ones in South Korea, do not measure solar radiation because the measuring equipment for solar radiation is expensive and difficult to maintain. For this reason, many researchers have studied solar radiation estimation models and suggested to apply them to predict solar radiation for different weather stations in South Korea, where the solar radiation is not measured. However, only a few studies have been conducted to identify the impact caused by estimation errors of various solar radiation models on building energy performance analysis. Therefore, four different weather files using different horizontal global solar radiation data, one using measured global solar radiation, and the other three using estimated global solar radiation models, which are Cloud-cover Radiation Model (CRM), Zhang and Huang Model (ZHM), and Meteorological Radiation Model (MRM) were packed into TRY formatted weather files in this study. These were then used for office building energy simulations to compare their energy consumptions, and the results showed that there were differences in the energy consumptions due to these four different solar radiation data. Additionally, it was found that using hourly solar radiation from the estimation models, which had a similar hourly tendency with the hourly measured solar radiation, was the most important key for precise building energy simulation analysis rather than using the solar models that had the best of the monthly or yearly statistical indices.