• Nuclear Engineering and Technology
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• v.56 no.11
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• pp.4770-4781
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• 2024

Spent nuclear fuel (SNF) requires accurate and effective supervision to prevent radiation release to the public and the environment. Gamma emission tomography (GET) has been used to inspect partial-defects at the pin-by-pin level through the acquisition of tomographic images. However, due to the high density of nuclear fuel rods, GET shows relatively low accuracy for the central region. This study proposes an image reconstruction method for the internal fuels based on Monte Carlo simulation and machine learning algorithm. The gammas from the SNF were measured with the GET device and the sinograms were reconstructed with Maximum-Likelihood Expectation-Maximization (MLEM) with prior-aware information of SNF, and image quality was improved with the machine learning technique. Our results show that the MLEM with prior-aware information could dramatically improve the image quality and the denoising technique with the machine learning could clearly correct the over-and under-estimation. Based on the results of quantitative validation of the proposed reconstruction method, the pattern classification accuracy was calculated to be about 100 %. These techniques enabled the image acquisition of the central fuel rod and increased accuracy in partial defect detection. For further study, the performance of the proposed reconstruction method will be evaluated based on the experiment data.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.237-240
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• 2002

Design rainfall using LH-moments following the consecutive duration were derived by the regional and at-site analysis using the observed and simulated data resulted from Monte Carlo techniques. RRMSE, RBIAS and RR in RRMSE for the design rainfall were computed and compared in the regional and at-site frequency analysis. Consequently, it was shown that the regional analysis can substantially more reduce the RRMSE, RBIAS and RR in RRMSE than at-site analysis in the prediction of design rainfall. RE for an optimal order of L-moments was also computed by the methods of L, L1, L2, L3 and L4-moments for GEV distribution. It was found that the method of L-moments is more effective than the others for getting optimal design rainfall according to the regions and consecutive durations in the regional frequency analysis. Diagrams for the design rainfall derived by the regional frequency analysis using L-moments were drawn according to the regions and consecutive durations by GIS techniques.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.602-607
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• 2013

Financial risks associated with capital investments are often measured with different feasibility indicators such as the net present value (NPV), the internal rate of return (IRR), the payback period (PBP), and the benefit-cost ratio (BCR). This paper aims at demonstrating practical applications of probabilistic feasibility analysis techniques for an integrated feasibility evaluation of the IRR and PBP. The IRR and PBP are concurrently analyzed in order to measure the profitability and liquidity, respectively, of a cash flow. The cash flow data of a real wind turbine project is used in the study. The presented approach consists of two phases. First, two newly reported analysis techniques are used to carry out a series of what-if analyses for the IRR and PBP. Second, the relationship between the IRR and PBP is identified using Monte Carlo simulation. The results demonstrate that the integrated feasibility evaluation of stochastic cash flows becomes a more viable option with the aide of newly developed probabilistic analysis techniques. It is also shown that the relationship between the IRR and PBP for the wind turbine project can be used as a predictive model for the actual IRR at the end of the service life based on the actual PBP of the project early in the service life.

• Macromolecular Research
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• v.9 no.4
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• pp.185-196
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• 2001

The self-assembly of block copolymers can lead to a variety of ordered structures on a nanometer scale. In this article, the self-assembling behaviors of triblock copolymers in the melt and the selective solvent are described with the results obtained from the computer simulations. With the advances of computing power, computer simulations using molecular dynamics and Monte Carlo techniques make it possible to study very complicated phenomena observed in the self-assembly of triblock copolymer. 13king full advantage of the computer simulation based on well-defined model, the effects of various structural and thermodynamic parameters such as the copolymer composition, the block sequence, the pairwise interaction energies, and temperature on the self-assembly are discussed in some detail. Some simulation results are compared with experimental ones End analyzed by comparing them with the theoretical treatment.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.195-209
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• 2014

In this paper, we derive the estimators of the location parameter and the scale parameter in a logistic distribution based on multiply type-II censored samples by the approximate maximum likelihood estimation method. We use four modified empirical distribution function (EDF) types test for the logistic distribution based on multiply type-II censored samples using proposed approximate maximum likelihood estimators. We also propose the modified normalized sample Lorenz curve plot for the logistic distribution based on multiply type-II censored samples. For each test, Monte Carlo techniques are used to generate the critical values. The powers of these tests are also investigated under several alternative distributions.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.265-267
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• 2004

A blind separation problem in a multiple-input-multiple-output (MIMO) linear time-invariant (LTI) system with finite-alphabet inputs is considered. A discrete-time matrix equation model is used to describe the input-output relation of the system in order to make full use of the advantages of modern digital signal processing techniques. At first, ambiguity problem is investigated. Then, based on the results of the investigation, a new identifiability condition is proposed for the case of an input-data set which is widely used in digital communication. A probability bound such that an arbitrary input matrix satisfies the identifiability condition is derived. Finally, Monte-Carlo simulation is performed to demonstrate the validity of our suggestions.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.367-376
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• 2003

Two explicit estimators of the scale parameter in an exponential distribution based on Type-II censored samples are proposed by appropriately approximating the likelihood function. Then two type tests, including the modified Cramer-von Mises test and Kolmogorov-Smirnov test are developed for the exponential distribution based on Type-II censored samples by using the proposed estimators. For each test, Monte Carlo techniques are used to generate critical values. The powers of these tests are investigated under several alternative distributions.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.327-332
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• 2001

An experimental design technique is developed for estimating the moments of system response functions. It is easy to implement and provides accurate results compared with other traditional methods. It is based on the work of Taguchi, later improved by D'Errico and Zaino. The existing experimental techniques, however, is applicable only for normally distributed cases. In this article the three-level Taguchi method is extended to obtain optimum choice for levels and weights to handle nonnormal distributions. A systematic procedure for reliability analysis is then proposed by using the Pearson system and the narrow system reliability bounds. Illustrative examples including a tolerance optimization problem are shown very accurate comparing with those by Monte-Carlo simulations and the first-order reliability method.

• Journal of the Korean Statistical Society
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• v.14 no.2
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• pp.63-75
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• 1985

This paper considers a statistical calibration problem in which the standard as wel as the nonstandard measurement is subject to error. Since the classicla approach cannot handle this situation properly, a functional relationship model with additional feature of prediction is proposed. For the analysis of the problem four different approaches-two estimation techniques (ordinary and grouping least squares) combined with two prediction methods (classical and inverse prediction)-are considered. By Monte Carlo simulation the perromance of each approach is assessed in term of the probability of concentration. The simulation results indicate that the ordinary least squares with inverse prediction is generally preferred in interpolation while the grouping least squares with classical prediction turns out to be better in extrapolation.

• Communications for Statistical Applications and Methods
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• v.24 no.3
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• pp.193-209
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• 2017

The power Lindley distribution with some of its properties is considered in this article. Maximum likelihood, least squares, maximum product spacings, and Bayes estimators are proposed to estimate all the unknown parameters of the power Lindley distribution. Lindley's approximation and Markov chain Monte Carlo techniques are utilized for Bayesian calculations since posterior distribution cannot be reduced to standard distribution. The performances of the proposed estimators are compared based on simulated samples. The waiting times of research articles to be accepted in statistical journals are fitted to the power Lindley distribution with other competing distributions. Chi-square statistic, Kolmogorov-Smirnov statistic, Akaike information criterion and Bayesian information criterion are used to access goodness-of-fit. It was found that the power Lindley distribution gives a better fit for the data than other distributions.