• Nuclear Engineering and Technology
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• v.50 no.6
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• pp.854-859
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• 2018

Calculating minimal cut sets is a typical quantification method used to evaluate the top event probability for a fault tree. If minimal cut sets cannot be calculated or if the accuracy of the quantification result is in doubt, the Monte Carlo method can provide an alternative for fault tree quantification. The Monte Carlo method for fault tree quantification tends to take a long time because it repeats the calculation for a large number of samples. Herein, proposal is made to improve the quantification algorithm of a fault tree with circular logic. We developed a top-down iteration algorithm that combines the characteristics of the top-down approach and the iteration approach, thereby reducing the computation time of the Monte Carlo method.

• Journal of the Korean Society of Safety
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• v.15 no.4
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• pp.134-138
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• 2000

Signal words such as DANGER, WARNING, CAUTION, etc. have been used in order to transmit a potential hazard easily and quickly. But they were applied to a number of the sites without consistency. Thus, this study took Quantification Method and Cluster Analysis in order to judge the signal words corresponding to the urgency of situations, and to analyze whether signal words are used properly or not. According to the result of Quantification Method II signal words were most affected by Understanding, Severity and Likelihood in both student group and industrial worker group. And in Quantification Method III CAUTION corresponded to Immediacy and Understanding whereas NOTICE did to Receptivity, WARNING, DEADLY and DANCER did to Likelihood, Dangerousness and Severity. Finally, Cluster Analysis showed that CAUTION and NOTICE were recognized as similar words.

• The Plant Pathology Journal
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• v.39 no.1
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• pp.141-148
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• 2023

Black shoot blight disease caused by Erwinia pyrifoliae has serious impacts on quality and yield in pear production in Korea; therefore, rapid and accurate methods for its detection are needed. However, traditional detection methods require a great deal of time and fail to achieve absolute quantification. In the present study, we developed a droplet digital polymerase chain reaction (ddPCR) method for the detection and absolute quantification of E. pyrifoliae using a pair of species-specific primers. The detection range was 10³-10⁷ copies/ml (DNA templates) and cfu/ml (cell culture templates). This new method exhibited good linearity and repeatability and was validated by absolute quantification of E. pyrifoliae DNA copies from samples of artificially inoculated immature pear fruits. Here, we present the first study of ddPCR assay for the detection and quantification of E. pyrifoliae. This method has potential applications in epidemiology and for the early prediction of black shoot blight outbreaks.

• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.97-106
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• 2020

The objective of this paper is to suggest a new quantification method for multi-unit probabilistic safety assessment (PSA) that removes the overestimation error caused by the existing delete-term approximation (DTA) based quantification method. So far, for the actual plant PSA model quantification, a fault tree with negates have been solved by the DTA method. It is well known that the DTA method induces overestimated core damage frequency (CDF) of nuclear power plant (NPP). If a PSA fault tree has negates and non-rare events, the overestimation in CDF drastically increases. Since multi-unit seismic PSA model has plant level negates and many non-rare events in the fault tree, it should be very carefully quantified in order to avoid CDF overestimation. Multi-unit PSA fault tree has normal gates and negates that represent each NPP status. The NPP status means core damage or non-core damage state of individual NPPs. The non-core damage state of a NPP is modeled in the fault tree by using a negate (a NOT gate). Authors reviewed and compared (1) quantification methods that generate exact or approximate Boolean solutions from a fault tree, (2) DTA method generating approximate Boolean solution by solving negates in a fault tree, and (3) probability calculation methods from the Boolean solutions generated by exact quantification methods or DTA method. Based on the review and comparison, a new intersection removal by probability (IRBP) method is suggested in this study for the multi-unit PSA. If the IRBP method is adopted, multi-unit PSA fault tree can be quantified without the overestimation error that is caused by the direct application of DTA method. That is, the extremely overestimated CDF can be avoided and accurate CDF can be calculated by using the IRBP method. The accuracy of the IRBP method was validated by simple multi-unit PSA models. The necessity of the IRBP method was demonstrated by the actual plant multi-unit seismic PSA models.

• The Korean Journal of Applied Statistics
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• v.25 no.1
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• pp.225-237
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• 2012

This study proposes a quantification algorithm for a PLS method with several sets of variables. We called the quantification method for PLS with more than 2 sets of data a generalization. The basis of the quantification for PLS method is singular value decomposition. To derive the form of singular value decomposition in the data with more than 2 sets more easily, we used the constraint, $a^ta+b^tb+c^tc=3$ not $a^ta=1$, $b^tb=1$, and $c^tc=1$, for instance, in the case of 3 data sets. However, to prove that there is no difference, we showed it by the use of 2 data sets case because it is very complicate to prove with 3 data sets. The keys of the study are how to form the singular value decomposition and how to get the coordinates for the plots of variables and observations.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2523-2533
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• 2021

In the last decade, the best estimate plus uncertainty methodologies in nuclear technology and nuclear power plant design have become a trending topic in the nuclear field. Since BEPU was allowed for licensing purposes by the most important regulator bodies, different uncertainty assessment methods have become popular, overall non-parametric methods. While non-parametric tolerance regions can be well stated and used in uncertainty quantification for licensing purposes, the propagation of the uncertainty through different codes (multi-scale, multiphysics) in cascade needs a better depiction of uncertainty than the one provided by the tolerance regions or a probability distribution. An alternative method based on the parametric or distributional probability boxes is used to perform uncertainty quantification and propagation regarding statistic uncertainty from one code to another. This method is sample-size independent and allows well-defined tolerance intervals for uncertainty quantification, manageable for uncertainty propagation. This work characterizes the distributional p-boxes behavior on uncertainty quantification and uncertainty propagation through nested random sampling.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.5
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• pp.281-288
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• 2009

The purpose of this paper is to provide the statistical models for landslide prediction through quantification and AHP methods. Quantification method is a statistical method of providing quantity to qualitative variables by analyzing the observed data. In this paper, we suggest the quantification process based on the results of cannonical correlation analysis. In contrast with the quantification method which is based on given data the AHP(Analytic Hierarchy Process) technique is a kind of method based on questionaire data which is usually taken from professionals. We analyze both the real data(provided from KIGAM) and questionaire data collected from professionals of various related area. We developed two kinds of evaluation table which provide the scores of land slide possibility and the logistic model providing the probability of occurring landslide. Finally we compare the performance and evaluate the stability of the suggested two models.

• Biomedical Science Letters
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• v.26 no.4
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• pp.275-287
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• 2020

Since its introduction in the forensic field, quantitative PCR (qPCR) has played an essential role in DNA analysis. Quality of DNA should be evaluated before short tandem repeat (STR) profiling to obtain reliable results and reduce unnecessary costs. To this end, various human DNA quantification kits have been developed. Among these kits, the PowerQunat® System was designed not only to determine the total amount of human DNA and human male DNA from a forensic evidence item, but also to offer data about degradation of DNA samples. However, a crucial limitation of the PowerQunat® System is its high cost. Therefore, to minimize the cost of DNA quantification, we evaluated kit performance using a reduced volume of reagents (1/2-volume) using DNA samples of varying types and concentrations. Our results demonstrated that the low-volume method has almost comparable performance to the manufacturer's method for human DNA quantification, human male DNA quantification, and DNA degradation index. Furthermore, using a reduced volume of regents, it is possible to run 2 times more reactions per kit. We expect the proposed low-volume method to cut costs in half for laboratories dealing with large numbers of DNA samples.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.668-671
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• 1997

This paper describes a new process-plan selection method using a modified Fuzzy Quantification Theory(FQT). The problem for process-plan selection can be characterized by multiple attributes and used subjective, uncertain information. Fuzzy Quantification Theory is used for handling such informations because it is a useful tool when human judgment or evaluation is quantified via linguistic variables and the proposed method is concerned with the selection of a process plan by derivation of the values of categories for each attribute. In this paper, a modified Fuzzy Quantification Theory(FQT) is described and the procedure of this approach is explained and examples are illustrated.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.5
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• pp.490-496
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• 1997

This paper describes a new process-plan selection method using a modified Fuzzy Quantification Theory(FQT). The problem of process-plan selection can be characterized by multiple attributes and used subjective, uncertain information. Fuzzy Quantification Theory is used for handling such information because it is a useful tool when human judgment or evaluation is quantified via linguistic variables, and the proposed method is concerned with the selection of a process plan by derivation of the values of categories for each attribute. In this paper, a modified Fuzzy Quantification Theory(FQT) is described and the procedure of this approach is explained and examples illustrated.