• Journal of Radiation Protection and Research
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• v.46 no.4
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• pp.204-212
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• 2021

Background: Identification of radioisotopes for plastic scintillation detectors is challenging because their spectra have poor energy resolutions and lack photo peaks. To overcome this weakness, many researchers have conducted radioisotope identification studies using machine learning algorithms; however, the effect of data normalization on radioisotope identification has not been addressed yet. Furthermore, studies on machine learning-based radioisotope identifiers for plastic scintillation detectors are limited. Materials and Methods: In this study, machine learning-based radioisotope identifiers were implemented, and their performances according to data normalization methods were compared. Eight classes of radioisotopes consisting of combinations of ²²Na, ⁶⁰Co, and ¹³⁷Cs, and the background, were defined. The training set was generated by the random sampling technique based on probabilistic density functions acquired by experiments and simulations, and test set was acquired by experiments. Support vector machine (SVM), artificial neural network (ANN), and convolutional neural network (CNN) were implemented as radioisotope identifiers with six data normalization methods, and trained using the generated training set. Results and Discussion: The implemented identifiers were evaluated by test sets acquired by experiments with and without gain shifts to confirm the robustness of the identifiers against the gain shift effect. Among the three machine learning-based radioisotope identifiers, prediction accuracy followed the order SVM > ANN > CNN, while the training time followed the order SVM > ANN > CNN. Conclusion: The prediction accuracy for the combined test sets was highest with the SVM. The CNN exhibited a minimum variation in prediction accuracy for each class, even though it had the lowest prediction accuracy for the combined test sets among three identifiers. The SVM exhibited the highest prediction accuracy for the combined test sets, and its training time was the shortest among three identifiers.

• Journal of the Korean Society of Safety
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• v.28 no.1
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• pp.158-163
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• 2013

While job stress evaluations are reported in the recent surveys upon the nuclear power plants(NPPs), any significant advance in the types of questionnaires is not currently found. There are limitations to their usefulness as analytic tools for the management of safety resources in NPPs. Data mining(DM) has emerged as one of the key features for data computing and analysis to conduct a survey analysis. There are still limitations to its capability such as dimensionality associated with many survey questions and quality of information. Even though some survey methods may have significant advantages, often these methods do not provide enough evidence of causal relationships and the statistical inferences among a large number of input factors and responses. In order to address these limitations on the data computing and analysis capabilities, we propose an advanced procedure of survey analysis incorporating the DM method into a statistical analysis. The DM method can reduce dimensionality of risk factors, but DM method may not discuss the robustness of solutions, either by considering data preprocesses for outliers and missing values, or by considering uncontrollable noise factors. We propose three steps to address these limitations. The first step shows data mining with response surface method(RSM), to deal with specific situations by creating a new method called response surface data mining(RSDM). The second step follows the RSDM with detailed statistical relationships between the risk factors and the response of interest, and shows the demonstration the proposed RSDM can effectively find significant physical, psycho-social, and environmental risk factors by reducing the dimensionality with the process providing detailed statistical inferences. The final step suggest a robust stress management system which effectively manage job stress of the workers in NPPs as a part of a safety resource management using the surrogate variable concept.

• Nuclear Engineering and Technology
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• v.41 no.5
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• pp.617-648
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• 2009

Under the government supported long-term nuclear R&D program, the severe accident research program at KAERI is directed to investigate unresolved severe accident issues such as core debris coolability, steam explosions, and hydrogen combustion both experimentally and numerically. Extensive studies have been performed to evaluate the in-vessel retention of core debris through external reactor vessel cooling concept for APR1400 as a severe accident management strategy. Additionally, an improvement of the insulator design outside the vessel was investigated. To address steam explosions, a series of experiments using a prototypic material was performed in the TROI facility. Major parameters such as material composition and void fraction as well as the relevant physics affecting the energetics of steam explosions were investigated. For hydrogen control in Korean nuclear power plants, evaluation of the hydrogen concentration and the possibility of deflagration-to-detonation transition occurrence in the containment using three-dimensional analysis code, GASFLOW, were performed. Finally, the integrated severe accident analysis code, MIDAS, has been developed for domestication based on MELCOR. The data transfer scheme using pointers was restructured with the modules and the derived-type direct variables using FORTRAN90. New models were implemented to extend the capability of MIDAS.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2004.02a
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• pp.130-139
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• 2004

The cladding materials remaining after reprocessing process of the nuclear fuel, generally called as hulls, are classified as a high-level radioactive waste. They are usually packaged in the container for disposal after being compacted, melted, or solidified into the matrix. The efforts to fabricate a better ingot for a more favorable disposal to the environment have failed due to the technical difficulties encountered in the chemical decontamination method. In the early 1990s, the accumulation of radio-chemical data on hulls and the advent of new technology such as a laser or plasma have made the pre-treatment of the hulls more efficient. This paper summarizes the information regarding the radio-chemical analysis of the hull through a literature survey and determines the characteristics of the hull and depth profile of the radio-nuclides within the hull thickness. The feasibility study was carried out to evaluate the reduction of the radioactivity by peeling off the surface of the hull with the application of laser technology.

• Bulletin of the Korean Chemical Society
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• v.26 no.4
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• pp.569-574
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• 2005

A coupled column liquid chromatography system was applied for the separation of the burnup monitors in spent nuclear fuel sample solutions. A reversed phase column was studied for the adsorption behavior of uranyl ions using alpha-hydroxyisobutyric acid as an eluent and used for the separation of plutonium and uranium. A cation exchange column prepared by coating 1-eicosylsulfate onto the reversed phase column was used for the separation of the lanthanides. In addition, retention of Np was checked with the reversed phase column and cation exchange column, respectively, according to the oxidation states to observe the interference effect for the separation of burnup monitors. This chromatography system showed a great reduction in separation time compared to a conventional anion exchange method. A good agreement from the burnup data was obtained between for this method and a conventional anion exchange method to within 1% of a difference for the spent nuclear fuel samples of about 40 GWD/MTU.

• Nuclear Engineering and Technology
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• v.41 no.6
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• pp.797-806
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• 2009

The Multichannel Analyzer for Transient and steady-state in Rod Array - Liquid Metal Reactor for Flow Blockage analysis (MATRA-LMR-FB) code for the analysis of a subchannel blockage has been developed and evaluated through several experiments. The current version of the code is improved here by the implementation of a distributed resistance model which accurately considers the effect of flow resistance on wire spacers, by the addition of a turbulent mixing model, and by the application of a hybrid scheme for low flow regions. Validation calculations for the MATRA-LMR-FB code were performed for Oak Ridge National Laboratory (ORNL) 19-pin tests with wire spacers and Karlsruhe 169-pin tests with grid spacers. The analysis of the ORNL 19-pin tests conducted using the code reveals that the code has sufficient predictive accuracy, within a range of 5 $^{\circ}C$, for the experimental data with a blockage. As for the results of the analyses, the standard deviation for the Karlsruhe 169-pin tests, 0.316, was larger than the standard deviation for the ORNL 19-pin tests, 0.047.

• Nuclear Engineering and Technology
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• v.36 no.5
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• pp.450-459
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• 2004

For the evaluation of emission sources by air sampling, airborne particulate matter for fine (<2.5 ${\mu}m2$ EAD : $PM_{2.5}$) and coarse partical (2.5-10 ${\mu}m2$ EAD : $PM_{2.5-10}$ fractions were collected using a Gent stacked filter unit low volume sampler and two types of polycarbonate filters. Air samples were collected twice monthly at two regions in and around Daejeon city in the Republic of Korea from January to December 2002. Monthly mass concentration of $PM_{2.5}$ and $PM_{2.5-10}$ were measured and the concentrations of 10 marker elements (Al, Sc, Ti ; Na, Cl ; As, V. Sb, Br, Se) were determined by an instrumental neutron activation analysis. Analytical quality control was corried out using certified reference materials. Enrichment factors were also calculated from the monitoring data to classify the anthropogenic and crustal origins.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1199-1209
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• 2021

The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

• 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 Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.18 no.4
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• pp.517-536
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• 2020

The radionuclide inventory in radioactive waste from nuclear power plants should be determined to secure the safety of final repositories. As an alternative to time-consuming, labor-intensive, and destructive radiochemical analysis, the indirect scaling factor (SF) method has been used to determine the concentrations of difficult-to-measure radionuclides. Despite its long history, the original SF methodology remains almost unchanged and now needs to be improved for advanced SF implementation. Intense public attention and interest have been strongly directed to the reliability of the procedures and data regarding repository safety since the first operation of the low- and intermediate-level radioactive waste disposal facility in Gyeongju, Korea. In this review, statistical methodologies for SF implementation are described and evaluated to achieve reasonable and advanced decision-making. The first part of this review begins with an overview of the current status of the scaling factor method and global experiences, including some specific statistical issues associated with SF implementation. In addition, this review aims to extend the applicability of SF to the characterization of large quantities of waste from the decommissioning of nuclear facilities.