• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.2119-2129
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• 2020

Flow-accelerated corrosion (FAC), liquid droplet impingement erosion (LDIE), cavitation and flashing can cause continuous wall-thinning in nuclear secondary pipes. In order to prevent pipe rupture events resulting from the wall-thinning, most NPPs (nuclear power plants) implement their management programs, which include periodic thickness inspection using UT (ultrasonic test). Meanwhile, it is well known in field experiences that the thickness measurement errors (or deviations) are often comparable with the amount of thickness reduction. Because of these errors, it is difficult to estimate wall-thinning exactly whether the significant thinning has occurred in the inspected components or not. In the previous study, the authors presented an approximate estimation procedure as the first step for thickness measurement deviations at each inspected component and the statistical & quantitative characteristics of the measurement deviations using plant experience data. In this study, statistical significance was quantified for the current methods used for wall-thinning determination. Also, the authors proposed new estimation procedures for determining local wall-thinning to overcome the weakness of the current methods, in which the proposed procedure is based on analysis of variance (ANOVA) method using subgrouping of measured thinning values at all measurement grids. The new procedures were also quantified for their statistical significance. As the results, it is confirmed that the new methods have better estimation confidence than the methods having used until now.

• Journal of Veterinary Clinics
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• v.32 no.1
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• pp.73-77
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• 2015

There has been increasing attention on sample size requirements in peer reviewed medical literatures. Accordingly, a statistically-valid sample size determination has been described for a variety of medical situations including diagnostic test accuracy studies. If the sample is too small, the estimate is too inaccurate to be useful. On the other hand, a very large sample size would yield the estimate with more accurate than required but may be costly and inefficient. Choosing the optimal sample size depends on statistical considerations, such as the desired precision, statistical power, confidence level and prevalence of disease, and non-statistical considerations, such as resources, cost and sample availability. In a previous paper (J Vet Clin 2012; 29: 68-77) we briefly described the statistical theory behind sample size calculations and provided practical methods of calculating sample size in different situations for different research purposes. This review describes how to calculate sample sizes when assessing diagnostic test performance such as sensitivity and specificity alone. Also included in this paper are tables and formulae to help researchers for designing diagnostic test studies and calculating sample size in studies evaluating test performance. For complex studies clinicians are encouraged to consult a statistician to help in the design and analysis for an accurate determination of the sample size.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.11
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• pp.1099-1108
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• 2017

Data points obtained by conducting repetitive experiments under identical environmental conditions are, theoretically, required to correspond. However, experimental data often display variations due to generated errors or noise resulting from various factors and inherent uncertainties. In this study, an algorithm aiming to determine valid bounds of input variables, representing uncertainties, was developed using probabilistic and statistical methods. Furthermore, a reliability assessment was performed to verify and validate applications of this algorithm using bolt-fastening friction coefficient data in a sample application.

• Journal of The Korean Astronomical Society
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• v.27 no.2
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• pp.103-117
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• 1994

To extend the work of Gott, Park, and Lee (1989), statistical properties of gravitational lensing in a wide variety of cosmological models involving non-zero cosmological constant is investigated, using the redshifts of both lens and source and observed angular separation of images for gravitational lens systems. We assume singular isothermal sphere as lensing galaxy in homogenous and isotropic Friedmann­Lemaitre-Robertson- Walker universe, Schechter luminosity function, standard angular diameter distance formula and other galaxy parameters used in Fukugita and Turner (1991). To find the most adequate flat cosmological model and put a limit on the value of dimensionless cosmological constant $\lambda_0$, the mean value of the angular separation of images, probability distribution of angular separation and cumulative probability are calculated for given source and lens redshifts and compared with the observed values through several statistical methods. When there is no angular selection effect, models with highest value of $\lambda_0$ is preferred generally. When the angular selection effects are considered, the preferred model depends on the shape of the selection functions and statistical methods; yet, models with large $\lambda_0$ are preferred in general. However, the present data can not rule out any of the flat universe models with enough confidence. This approach can potentially select out best model. But at the moment, we need more data.

• Korean Journal of Metals and Materials
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• v.47 no.12
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• pp.819-827
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• 2009

This work is concerned with a statistical analysis of factors affecting the irradiation-assisted stress corrosion cracking (IASCC) of austenitic stainless steels for core internals of pressurized water reactors (PWR). The microstructural and environmental factors were reviewed and critically evaluated by the statistical analysis. The Cr depletion at grain boundary was determined to have no significant correlation with the IASCC susceptibility. The threshold irradiation fluence of IASCC in a PWR was statistically calculated to decrease from 5.799 to 1.914 DPA with increase of temperature from 320 to $340^{\circ}C$. From the analysis of the relationship between applied stress and time-to-failure of stainless steel components based on an accelerated life testing model, it was found that B2 life of a baffle former bolt exposed to neutron fluence of 20 and 75 DPA was at least 2.5 and 0.4 year, respectively, within 95% confidence interval.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.8 no.2
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• pp.123-133
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• 2010

A statistical evaluation methodology was developed to determine the compliance of candidate waste stream with clearance criteria based upon distribution of radionuclide in a waste stream at a certain confidence level. For the cases where any information on the radionuclide distribution is not available, the relation between arithmetic mean of radioactivity concentration and its acceptable maximum standard deviation was demonstrated by applying widely-known Markov Inequality and One-side Chebyshev Inequality. The relations between arithmetic mean and its acceptable maximum standard deviation were newly derived for normally or lognormally distributed radionuclide in a waste stream, using probability density function, cumulative density function, and other statistical relations. The evaluation methodology was tested for a representative case at 95% of confidence level and 100 Bq/g of clearance level of radioactivity concentration, and then the acceptable range of standard deviation at a given arithmetic mean was quantitatively shown and compared, by varying the type of radionuclide distribution. Furthermore, it was statistically demonstrated that the allowable range of clearance can be expanded, even at the same confidence level, if information on the radionuclide distribution is available.

• Journal of the Korean Dietetic Association
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• v.13 no.1
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• pp.38-49
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• 2007

The purpose of this study was to determine the attributes and factors of brand personality for contract foodservice management companies. Self-administered questionnaires were distributed to five students at universities operating under the top five companies in contract foodservice management. The following statistical analyses were conducted for the data assessment: descriptive analysis, t-test, ANOVA, reliability analysis, and factor analysis, using the SPSS Win(12.0) package program. From these analyses we divided a company's brand personality into the following five functional and emotional elements: sensibility, sincerity, confidence, competence, and excitement. Based on these five elements a total of 26 scales were developed to measure brand attributes of the companies. The variance was explained by 19.29% of sensibility, 17.65% of sincerity, 15.71% of confidence, 14.06% of competence, and 13.62% of excitement. The calculated Cronbach's alpha was more than .90 for all the scales measuring the five attributes, indicating good internal consistency. There were significant differences in sensibility(p<.01), sincerity(p<.001), creditability(p<.01), competence (p<.001), and excitement(p<.001) among the companies. In regards to overall brand personality, company a had a higher mean score for sincerity, while the other companies had higher mean scores for competence. Among the brand personalities, 'confidence' had the highest mean score with 3.36, followed by 'cooperation' (3.17), 'successful'(3.12), 'leadership'(3.11), and 'down-to-earth'(3.02).

• The Korean Journal of Emergency Medical Services
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• v.9 no.2
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• pp.169-180
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• 2005

The purpose of this research was to evaluate retention of cognitive knowledge, psychomotor skills and self-confidence on CPR 3 months after CPR training program. The sample consisted of 39 nursing students. We provided one rescuer CPR training program for nursing college students on the basis of AHA. The questionnaires for knowledge of CPR were developed 50 items based on AHA guidelines. Self-confidence were checked by 11 items questionnaires. The accuracy of CPR skills were checked by Skillreporter CPR training manikin and by researcher's evaluation based on CPR skill checklist. The results were as follows ; 1. The majority of participants didn't have any previously experiences of CPR training (76.9%). Only 15.1% previously took the CPR training with CPR practice. 2. In terms of self-confidence of CPR. The score were increased for 2 days (p>.001) but retention of self confidence was significantly statistical decreased in 3 months after training (p<.001). 3. There was a statistically significant decrement in mean of knowledge of CPR between 2 days and 3 months after CPR training (p<.001). 4. There was a statistically significant decrement in cognitive knowledge of CPR based on CPR skills checklist(p<.001). 5. Retention scores of psychomotor skills of CPR 3 months after training were 42.10% in numbers of adequate ventilation, 52.81% in numbers of adequate chest compression (p<.001) respectively. 6. Retention of passing rate on chest compressions of CPR 3 months after training was 27% (p<.001), on ventilation was 2.63% (p>.001). The error items with statistically significant differences 3 months after CPR training were too little ventilation (74.36%) and too little chest compressions (92.31%). The results of the study suggest that we need further evaluation of course components which could improve retention of CPR for all trainees.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.99-109
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• 2019

We compared the confidence intervals of estimators using various bootstrap methods for a Random Coefficient Autoregressive(RCA) model. We consider a Quasi score estimator and M-Quasi score estimator using Huber, Tukey, Andrew and Hempel functions as bounded functions, that do not have required assumption of distribution. A standard bootstrap method, percentile bootstrap method, studentized bootstrap method and hybrid bootstrap method were proposed for the estimations, respectively. In a simulation study, we compared the asymptotic confidence intervals of the Quasi score and M-Quasi score estimator with the bootstrap confidence intervals using the four bootstrap methods when the underlying distribution of the error term of the RCA model follows the normal distribution, the contaminated normal distribution and the double exponential distribution, respectively.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1308-1308
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• 2024

Computer vision techniques have been widely employed in automated construction management to enhance safety and prevent accidents at construction sites. However, previous research in the field of vision-based approaches has often overlooked small-sized construction tools. These tools present unique challenges in data collection due to their diverse shapes and sizes, as well as in improving model performance to accurately detect and classify them. To address these challenges, this study aimed to enhance the performance of vision-based classifiers for small-sized construction tools, including bucket, cord reel, hammer, and tacker, by leveraging synthetic images generated from a 3D virtual environment. Three classifiers were developed using the YOLOv8 algorithm, each differing in the composition of the training dataset: (i) 'Real-4000', trained on 4,000 authentic images collected through web crawling methods (1,000 images per object); (ii) 'Hybrid-4000', consisting of 2,000 authentic images and 2,000 synthetic images; and (iii) 'Hybrid-8000', incorporating 4,000 authentic images and 4,000 synthetic images. To validate the performance of the classifiers, 144 directly-captured images for each object were collected from real construction sites as the test dataset. The mean Average Precision at an IoU threshold of 0.5 (mAP_0.5) for the classifiers was 79.6%, 90.8%, and 94.8%, respectively, with the 'Hybrid-8000' model demonstrating the highest performance. Notably, for objects with significant shape variations, the use of synthetic images led to the enhanced performance of the vision-based classifiers. Moreover, the practical applicability of the proposed classifiers was validated through confidence scores, particularly between the 'Hybrid-4000' and 'Hybrid-8000' models. Statistical analysis using t-tests indicated that the performance of the 'Hybrid-4000' model would either matched or exceeded that of the 'Hybrid-8000'model based on confidence scores. Thus, employing the 'Hybrid-4000' model may be preferable in terms of data collection efficiency and processing time, contributing to enhanced safety and real-time automation and robotics in construction practices.