• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.878-888
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• 2020

This paper presents the validation of UNIST in-house Monte Carlo code MCS used for the high-fidelity simulation of commercial pressurized water reactors (PWRs). Its focus is on the accurate, spatially detailed neutronic analyses of startup physics tests for the initial core of the Watts Bar Nuclear 1 reactor, which is a vital step in evaluating core phenomena in an operating nuclear power reactor. The MCS solutions for the Consortium for Advanced Simulation of Light Water Reactors (CASL) Virtual Environment for Reactor Applications (VERA) core physics benchmark progression problems 1 to 5 were verified with KENO-VI and Serpent 2 solutions for geometries ranging from a single-pin cell to a full core. MCS was also validated by comparing with results of reactor zero-power physics tests in a full-core simulation. MCS exhibits an excellent consistency against the measured data with a bias of ±3 pcm at the initial criticality whole-core problem. Furthermore, MCS solutions for rod worth are consistent with measured data, and reasonable agreement is obtained for the isothermal temperature coefficient and soluble boron worth. This favorable comparison with measured parameters exhibited by MCS continues to broaden its validation basis. These results provide confidence in MCS's capability in high-fidelity calculations for practical PWR cores.

• Journal of Veterinary Clinics
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• v.40 no.4
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• pp.243-259
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• 2023

Machine learning and deep learning (ML/DL) algorithms have been successfully applied in medical practice. However, their application in veterinary medicine is relatively limited, possibly due to a lack in the quantity and quality of relevant research. Because the potential demands for ML/DL applications in veterinary clinics are significant, it is important to note the current gaps in the literature and explore the possible directions for advancement in this field. Thus, a scoping review was conducted as a situation analysis. We developed a search strategy following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed and Embase databases were used in the initial search. The identified items were screened based on predefined inclusion and exclusion criteria. Information regarding model development, quality of validation, and model performance was extracted from the included studies. The current review found 55 studies that passed the criteria. In terms of target animals, the number of studies on industrial animals was similar to that on companion animals. Quantitative scarcity of prediction studies (n = 11, including duplications) was revealed in both industrial and non-industrial animal studies compared to diagnostic studies (n = 45, including duplications). Qualitative limitations were also identified, especially regarding validation methodologies. Considering these gaps in the literature, future studies examining the prediction and validation processes, which employ a prospective and multi-center approach, are highly recommended. Veterinary practitioners should acknowledge the current limitations in this field and adopt a receptive and critical attitude towards these new technologies to avoid their abuse.

• Nuclear Engineering and Technology
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• v.55 no.4
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• pp.1528-1539
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• 2023

Since the leakage of sodium in an SFR (sodium-cooled fast reactor) causes an explosion upon reaction with air and water, sodium leakages represent an important safety issue. In this study, a novel technique for improving the reliability of sodium leakage detection applying DDVR (dynamic data validation and reconciliation) is proposed and verified to resolve this technical issue. DDVR is an approach that aims to improve the accuracy of a target system in a dynamic state by minimizing random errors, such as from the uncertainty of instruments and the surrounding environment, and by eliminating gross errors, such as instrument failure, miscalibration, or aging, using the spatial redundancy of measurements in a physical model and the reliability information of the instruments. DDVR also makes it possible to estimate the state of unmeasured points. To validate this approach for supporting sodium leakage detection, this study applies experimental data from a sodium leakage detection experiment performed by the Korea Atomic Energy Research Institute. The validation results show that the reliability of sodium leakage detection is improved by cooperation between DDVR and hardware measurements. Based on these findings, technology integrating software and hardware approaches is suggested to improve the reliability of sodium leakage detection by presenting the expected true state of the system.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.74-92
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• 2023

The computer system validation consulting industry is growing as domestic GMP companies' overseas exports or advancements increase, and computer system validation has been mandatory in Korea since the 2010s, but domestic CSV consulting companies are small in size and have a short history, revealing many shortcomings in terms of service quality and customer satisfaction when conducting consulting. Research related to consulting has been mainly focused on management consulting and IT consulting, and research related to computer system validation is very insufficient. This study confirmed the effect of consultant competency and service quality on consulting performance, customer satisfaction, and intention to renew contract when performing computer system validation through empirical research on food, pharmaceutical, cosmetics, and medical device companies, which are representative companies in the GMP industry. As a result, it was confirmed that consultant competency and service quality had a significant effect on consulting performance, customer satisfaction, and intention to renew contract. In addition, it was confirmed that the reputation and expertise of consulting companies had a moderating effect on the relationship between consultant competency and consulting performance.

• Analytical Science and Technology
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• v.37 no.2
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• pp.114-122
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• 2024

A transmission Raman spectroscopy-based quantitative model, which can analyze the content of a drug product containing naproxen sodium as its active pharmaceutical ingredient (API), was developed. Compared with the existing analytical method, i.e., high-performance liquid chromatography (HPLC), Raman spectroscopy exhibits high test efficiency owing to its shorter sample pre-treatment and measurement time. Raman spectroscopy is environmentally friendly since samples can be tested rapidly via a nondestructive method without sample preparation using solvent. Through this analysis method, rapid on-site analysis was possible and it could prevent the production of defective tablets with potency problems. The developed method was applied to the assays of the naproxen sodium of coated tablets that were manufactured in commercial scale and the content of naproxen sodium was accurately predicted by Raman spectroscopy and compared with the reference analytical method such as HPLC. The method validation of the new approach was also performed. Further, the specificity, linearity, accuracy, precision, and robustness tests were conducted, and all the results were within the criteria. The standard error of cross-validation and standard error of prediction values were determined as 0.949 % and 0.724 %, respectively.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.2
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• pp.75-82
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• 2002

It is useful to detect the tracking error with an optimal view in the presence of measurement origin uncertainty. In this paper, after the investigation of the targer error dependent on the detection threshold as well as the detection and false alarm probabilities in a clutter environment, a new algorothm that optimizes the threshold of validation region for target trackinf is proposed. The performance of the algorithm is demonstrated through computer simulations.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.549-557
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• 2016

In this paper we propose multiclassification method for large data sets by ensembling least squares support vector machines (LS-SVM) with principal components instead of raw input vector. We use the revised one-vs-all method for multiclassification, which is one of voting scheme based on combining several binary classifications. The revised one-vs-all method is performed by using the hat matrix of LS-SVM ensemble, which is obtained by ensembling LS-SVMs trained using each random sample from the whole large training data. The leave-one-out cross validation (CV) function is used for the optimal values of hyper-parameters which affect the performance of multiclass LS-SVM ensemble. We present the generalized cross validation function to reduce computational burden of leave-one-out CV functions. Experimental results from real data sets are then obtained to illustrate the performance of the proposed multiclass LS-SVM ensemble.

• Journal of the Korean Data and Information Science Society
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• v.19 no.3
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• pp.789-800
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• 2008

Current credit evaluation models based on financial data make use of smoothing estimated default ratios which are transformed from each financial variable. In this work, some problems of the credit evaluation models developed by financial experts are discussed and we propose improved credit evaluation models based on the stepwise variable selection method and Box-Cox transformed data whose distribution is much skewed to the right. After comparing goodness-of-fit tests of these models, the validation of the credit evaluation models using statistical methods such as the stepwise variable selection method and Box-Cox transformation function is explained.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.2
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• pp.114-120
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• 2000

Fossil power plant availability is significantly affected by gradual degradations of equipment as operation of the plant continues. It is quite important to determine whether or not to replace some equipment and when to replace the equipment. Performance calculation and analysis can provide the information. Robustness in the performance calculation can be increased by using verification ＆ validation of measured input data. We suggest new algorithm in which estimation relation for validated measurement can be obtained using correlation between measurements. Input estimation model is obtained using design data and acceptance measurement data of domestic 16 fossil power plant. The model consists of finding mostly correlated state variable in plant state and mapping relation based on the model and current state of power plant.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.4
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• pp.242-249
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• 2016

The railway system is being converted to the computer system from the existing mechanical device, and the dependency on software is being increased rapidly. Though the size and degree of complexity of software for railway system are slower than the development speed of hardware, it is expected that the size will be grown bigger gradually and the degree of complexity will be increased also. Accordingly, the validation of reliability and safety of embedded software for railway system was started to become influential as the important issue. Accordingly, various software test and validation activities are highly recommended in the international standards related railway software. In this paper, we presented a software coding analysis tool using symbolic execution for railway system, and presented its result of implementation.