• Nuclear Engineering and Technology
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• v.56 no.11
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• pp.4578-4586
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• 2024

Human reliability analysis (HRA) of nuclear research reactors often encounters a lack of human performance data, a challenge that is also faced by the TRIGA reactor of Thailand having no specific full method or human error database for HRA. To overcome this challenge, in 2023, HRA teams from the Korea Atomic Energy Research Institute (KAERI) and the Thailand Institute of Nuclear Technology (TINT) jointly developed an HRA framework for the TRIGA reactor. In the HRA framework development, the HRA practitioners applied KAERI's three main HRA tools, namely 1) the EMpirical data-Based crew Reliability Assessment and Cognitive Error analysis (EMBRACE) method, 2) the Human Reliability data EXtraction (HuREX) database, and 3) the TAsk COMplexity (TACOM) method. The HRA framework covers the overall process including the classification of documents and human error types as well as nominal human error extraction for estimating nominal human error probabilities. According to the results of the first use of the HRA framework on the TRIGA reactor in Thailand, the HRA framework provided an effective way to improve the procedures and systematically identify nominal human errors of actions in the emergency procedures.

• Journal of the Korean Society of Safety
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• v.21 no.3 s.75
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• pp.137-142
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• 2006

Quantification of error possibility, in an HRA process, should be performed so that the result of the qualitative analysis can be utilized in other areas in conjunction with overall safety estimation results. And also, the quantification is an essential process to analyze the error possibility in detail and to obtain countermeasures for the errors through screening procedures. In previous studies for the quantification of error possibility, nominal values were assigned by the experts' judgements and utilized as corresponding probabilities. The values assigned by experts' experiences and judgements, however, require verifications on their reliability. In this study, the validity of new error possibility values in new MCR design was verified by using the Onisawa's model which utilizes fuzzy linguistic values to estimate human error probabilities. With the model of error probabilities are represented as analyst's estimations and natural language expression instead of numerical values. As results, the experts' estimation values about error probabilities are well agreed to the existing error probability estimation model. Thus, it was concluded that the occurrence probabilities of errors derived from the human error analysis process can be assessed by nominal values suggested in the previous studies. It is also expected that our analysis method can supplement the conventional HRA method because the nominal values are based on the consideration of various influencing factors such as PSFs.

• Current Optics and Photonics
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• v.1 no.5
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• pp.538-543
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• 2017

Progressive addition lenses (PAL) have very wide application in the modern glasses market. The unique progressive surface can make a lens have progressive refractive power, which can meet the human eye's different needs for distance-vision and near-vision. According to the national glasses fabrication standard, the difference between actual optical power after fabrication and nominal design value should be less than 0.1D over the lens effective area. The optical power distribution of PAL is determined directly by the surface. Consequently, the surface processing accuracy requirement is proposed. Beginning from the surface expressions of progressive addition lenses, the relationship equations between the surface sag and optical power distribution are derived. They are demonstrated through tolerance analysis and test of an example progressive addition lens with addition of 2.09D (5.46D-7.55D). The example addition surface is fabricated under given accuracy by a single-point diamond ultra-precision machine. The optical power of the PAL example is tested with a focal-meter after fabrication. The optical power addition difference between test result and design nominal value is 0.09D, which is less than 0.1D. The derived relationship between the surface error and optical power is verified from the PAL example simulation and test result. It can provide theoretical tolerance analysis proof for the PAL surface fabricating process.

• Nuclear Engineering and Technology
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• v.52 no.1
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• pp.87-100
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• 2020

Performance shaping factors (PSFs) in a human reliability analysis (HRA) are one that may influence human performance in a task. Most currently applicable HRA methods for nuclear power plants (NPPs) use PSFs to highlight human error contributors and to adjust basic human error probabilities (HEPs) that assume nominal conditions of NPPs. Thus far, the effects of PSFs have been treated independently. However, many studies in the fields of psychology and human factors revealed that there may be relationships between PSFs. Therefore, the inter-relationships between PSFs need to be studied to better reflect their effects on operator errors. This study investigates these inter-relationships using two data sources and also suggests a context-based approach to treat the inter-relationships between PSFs. Correlation and factor analyses are performed to investigate the relationship between PSFs. The data sources are event reports of unexpected reactor trips in Korea and an experiment conducted in a simulator featuring a digital control room. Thereafter, context-based approaches based on the result of factor analysis are suggested and the feasibility of the grouped PSFs being treated as a new factor to estimate HEPs is examined using the experimental data.

• Nuclear Engineering and Technology
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• v.55 no.8
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• pp.2897-2904
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• 2023

The Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method is a widely used method in human reliability analysis (HRA). Performance shaping factors (PSFs) refer to the factors that may influence human performance and are used to adjust nominal human error probabilities (HEPs) in SPAR-H. However, the PSFs are assumed to be independent, which is unrealistic and can lead to unreasonable estimation of HEPs. In this paper, a new method is proposed to handle the dependencies among PSFs in SPAR-H to obtain more reasonable results. Firstly, the dependencies among PSFs are analyzed by using decision-making trial and evaluation laboratory (DEMATEL) method. Then, PSFs are assigned different weights according to their dependent relationships. Finally, multipliers of PSFs are modified based on the relative weights of PSFs. A case study is illustrated that the proposed method is effective in handling the dependent PSFs in SPAR-H, where the duplicate calculations of the dependent part can be reduced. The proposed method can deal with a more general situation that PSFs are dependent, and can provide more reasonable results.

• Journal of Korean Society for Quality Management
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• v.49 no.2
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• pp.145-159
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• 2021

Purpose: A statistical similarity evaluation to compare pharmacokinetics(PK) profile data between nonclinical and clinical experiments has become a significant issue on many drug development processes. This study proposes a new similarity index by considering important parameters, such as the area under the curve(AUC) and the time-series profile of various PK data. Methods: In this study, a new profile similarity index(PSI) by using the concept of a process capability index(Cp) is proposed in order to investigate the most similar animal PK profile compared to the target(i.e., Human PK profile). The proposed PSI can be calculated geometric and arithmetic means of all short term similarity indices at all time points on time-series both animal and human PK data. Designed simulation approaches are demonstrated for a verification purpose. Results: Two different simulation studies are conducted by considering three variances(i.e., small, medium, and large variances) as well as three different characteristic types(smaller the better, larger the better, nominal the best). By using the proposed PSI, the most similar animal PK profile compare to the target human PK profile can be obtained in the simulation studies. In addition, a case study represents differentiated results compare to existing simple statistical analysis methods(i.e., root mean squared error and quality loss). Conclusion: The proposed PSI can effectively estimate the level of similarity between animal, human PK profiles. By using these PSI results, we can reduce the number of animal experiments because we only focus on the significant animal representing a high PSI value.