• The Korean Journal of Nuclear Medicine
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• v.22 no.1
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• pp.39-45
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• 1988

To evaluate diagnostic accuracy of liver scintigraphy we analysed liver scans of 143 normal and 258 patients with various liver diseases. Three ROC curves for SOL, liver cirrhosis and diffuse liver disease were fitted using rating methods and areas under the ROC curves and their standard errors were calculated by the trapezoidal rule and the variance of the Wilcoxon statistic suggested by McNeil. We compared these results with that of National Institute of Radiological Science in Japan. 1) The sensitivity of liver scintigraphy was 74.2% in SOL, 71.8% in liver cirrhosis and 34.0% in diffuse liver disease. The specificity was 96.0% in SOL, 94.2% in liver cirrhosis and 87.6% in diffuse liver diasease. 2) ROC curves of SOL and liver cirrhosis approached the upper left-hand corner closer than that of diffuse liver disease. Area (${\pm}$ standard error). under the ROC curve was $0.868{\pm}0.024$ in SOL and $0.867{\pm}0.028$ in liver cirrhosis. These were significantly higher than $0.658{\pm}0.043$ in diffuse liver disease. 3) There was no interobserver difference in terms of ROC curves. But low sensitivty and high specificity of authors' SOL diagnosis suggested we used more strict decision threshold.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.91-91
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• 2023

General circulation models(GCMs)은 여러 국가 기관들의 물리적 기후 모의 프로세스를 기반으로 과거 및 미래 기후변화의 영향을 정량화하기 위해 개발되었으며 현재 미래 기후변화를 예측하는데 가장 효과적인 도구이다. 그러나 GCMs에 내포된 여러 불확실성 요소 및 넓은 격자형식의 기후 데이터는 GCMs 기후 데이터를 사용한 지역적 기후 모의 시 주요 걸림돌로 인식되어지고 있다. 편의보정 방법은 GCMs을 사용한 지역적 기후 모의 시 기후 모의 성능을 향상시키기 위해 여러 연구에서 사용되어져 왔으나 다른 연구에서는 이러한 편의보정 방법의 문제점을 언급했다. 따라서 본 연구는 편의보정 방법이 GCMs 기후 모의 결과에 미치는 영향을 정량화하고 더 나아가 GCMs 기후 변수에 따른 유량 모의 결과에 미치는 영향을 분석했다. 연구대상지 과거 기간 기후 모의를 위해 coupled model intercomparison project(CMIP)6의 GCMs을 사용했으며, 미래 기후 모의를 위해 shared socioeconomic pathway(SSP) 시나리오를 사용했다. 편의보정 방법으로는 분위사상법을 사용했으며, 편의보정 전후 GCMs 기후 모의 성능평가를 위해 5개 평가 지표를 사용했다. 연구대상지 장기 유출 모의를 위해 storm water management model(SWMM)이 사용되었으며, 기후 입력 자료로는 일 단위 강수량, 최고 및 최저온도를 고려했다. 미래 기후 및 유량 모의 결과의 불확실성은 square root of error variance(SREV) 방법을 통해 정량화됐다. 결과적으로 과거 기간 GCMs 기후 및 유량 모의성능은 편의보정 전보다 편의보정 후에서 향상되었으며 특히, 강수 및 유량 모의 성능이 크게 향상되었다. 미래 기간의 경우 편의보정 후에서 기후 및 유량의 극값을 더 잘 반영함을 확인했다. 본 연구의 결과는 GCMs 기후 변수를 사용한 지역적 기후 및 유량 모의 시 편의보정 방법이 미치는 영향에 대한 구체적인 정보를 제공할 수 있다.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.133-141
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• 2010

This research suggests a unified scheme for using stratified sampling and control variates method to improve the efficiency of estimation for parameters in simulation experiments. We utilize standardized concomitant variables defined during the course of simulation runs. We first use these concomitant variables to counteract the unknown error of response by the method of control variates, then use a concomitant variable not used in the controlled response and stratify the response into appropriate strata to reduce the variation of controlled response additionally. In case that the covariance between the response and a set of control variates is known, we identify the simulation efficiency of suggested method using control variates and stratified sampling. We conjecture the simulation efficiency of this method is better than that achieved by separated application of either control variates or stratified sampling in a simulation experiments. We investigate such an efficiency gain through simulation on a selected model.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.2
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• pp.61-75
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• 2023

This study aims to evaluate the usability of a banking app that is frequently used by the elderly. To this end, the usability PACMAD(People At the Center of Mobile Application Development) model that can be used in mobile was explained and the usability evaluation was empirically verified for the elderly over 60 years of age. For this study, descriptive statistics and variance analysis were conducted using SPSS 25.0 for 165 elderly people who had experience using banking apps. Looking at the analysis results of this study, efficiency, satisfaction, and effectiveness showed relatively high scores, and learnability, memorability, error, and cognitive load showed relatively low scores. In addition, in the verification of differences by age, it was confirmed that there were differences in all variables by age. These results suggest that the elderly's usability evaluation of banking apps and differences by age could be confirmed, but there is also a limitation that comparison with the general public is difficult because the age is limited to the elderly.

• Steel and Composite Structures
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• v.49 no.3
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• pp.337-348
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• 2023

Successive wetting and drying cycles of concrete due to weather changes can endanger the safety of engineering structures over time. Considering wetting and drying cycles in concrete tests can lead to a more correct and reliable design of engineering structures. This study aims to provide a model that can be used to estimate the resistance properties of concrete under different wetting and drying cycles. Complex sample preparation methods, the necessity for highly accurate and sensitive instruments, early sample failure, and brittle samples all contribute to the difficulty of measuring the strength of concrete in the laboratory. To address these problems, in this study, the potential ability of six machine learning techniques, including ANN, SVM, RF, KNN, XGBoost, and NB, to predict the concrete's tensile strength was investigated by applying 240 datasets obtained using the Brazilian test (80% for training and 20% for test). In conducting the test, the effect of additives such as glass and polypropylene, as well as the effect of wetting and drying cycles on the tensile strength of concrete, was investigated. Finally, the statistical analysis results revealed that the XGBoost model was the most robust one with R2 = 0.9155, mean absolute error (MAE) = 0.1080 Mpa, and variance accounted for (VAF) = 91.54% to predict the concrete tensile strength. This work's significance is that it allows civil engineers to accurately estimate the tensile strength of different types of concrete. In this way, the high time and cost required for the laboratory tests can be eliminated.

• Journal of Korean Academy of Nursing
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• v.53 no.6
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• pp.652-665
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• 2023

Purpose: This study aimed to develop a nursing clinical judgment scale (NCJS) and verify its validity and reliability in assessing the clinical judgment of nurses. Methods: A preliminary instrument of the NCJS comprising 38 items was first developed from attributes and indicators derived from a literature review and an in-depth/focus interview with 12 clinical nurses. The preliminary tool was finalized after 7 experts conducted a content validity test based on a data from a preliminary survey of 30 hospital nurses in Korea. Data were collected from 443 ward, intensive care unit, emergency room nurses who voluntarily participated in the survey through offline and online for the verification of the construct validity and reliability of the scale. Results: The final scale comprised 23 items scored on a 5-point Likert scale. Six factors - integrated data analysis, evaluation and reflection on interventions, evidence on interventions, collaboration among health professionals, patient-centered nursing, and collaboration among nurse colleagues - accounted for 64.9% of the total variance. Confirmatory factor analysis supported the fit of the measurement model, comprising six factors (root mean square error of approximation = .07, standardized root mean square residual = .04, comparative fit index = .90). Cronbach's α for all the items was .92. Conclusion: The NCJS is a valid and reliable tool that fully reflects the characteristics of clinical practice, and it can be used effectively to evaluate the clinical judgment of Korean nurses. Future research should reflect the variables influencing clinical judgment and develop an action plan to improve it.

• Geomechanics and Engineering
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• v.36 no.2
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• pp.111-119
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• 2024

In the realm of rock excavation projects, precise estimation of the drilling rate index stands as a pivotal factor in strategic planning and cost assessment. This study introduces and evaluates two pioneering computational intelligence models designed for the prognostication of the drilling rate index, a pivotal parameter with direct implications for cost estimation in rock excavation projects. These models, denoted as the Relevance Vector Regression (RVR) optimized with the Invasive Weed Optimization algorithm (IWO) (RVR-IWO model) and the RVR integrated with the Shuffled Frog Leaping algorithm (SFL) (RVR-SFL model), represent a groundbreaking approach to forecasting drilling rate index. The RVR-IWO and RVR-SFL models were meticulously devised to harness the capabilities of computational intelligence and optimization techniques for drilling rate index estimation. This research pioneers the integration of IWO and SFL with RVR, constituting an unprecedented effort in forecasting drilling rate index. The primary objective of this study was to gauge the precision and dependability of these models in forecasting the drilling rate index, revealing significant distinctions between the two. In terms of predictive precision, the RVR-IWO model emerged as the superior choice when compared to the RVR-SFL model, underscoring the remarkable efficacy of the Invasive Weed Optimization algorithm. The RVR-IWO model delivered noteworthy results, boasting a Variance Account for (VAF) of 0.8406, a Mean Squared Error (MSE) of 0.0114, and a Squared Correlation Coefficient (R²) of 0.9315. On the contrary, the RVR-SFL model exhibited slightly lower precision, yielding an MSE of 0.0160, a VAF of 0.8205, and an R2 of 0.9120. These findings serve to highlight the potential of the RVR-IWO model as a formidable instrument for drilling rate index prediction, particularly within the framework of rock excavation projects. This research not only makes a significant contribution to the realm of drilling engineering but also underscores the broader adaptability of the RVR-IWO model in tackling an array of challenges within the domain of rock engineering. Ultimately, this study advances the comprehension of drilling rate index estimation and imparts valuable insights into the practical implementation of computational intelligence methodologies within the realm of engineering projects.

• Journal of Animal Science and Technology
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• v.66 no.2
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• pp.366-373
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• 2024

This study aims to estimate the connectedness rating (CR) of Korean swine breeding herds. Using 104,380 performance and 83,200 reproduction records from three swine breeds (Yorkshire, Landrace and Duroc), the CR was estimated for two traits: average daily gain (ADG) and number born alive (NBA) in eight breeding herds in the Republic of Korea (hereafter, Korea). The average CR for ADG in the Yorkshire breed ranges from 1.32% to 28.5% depending on the farm. The average CR for NBA in the Yorkshire herd ranges from 0% to 12.79%. A total of 60% of Yorkshire and Duroc herds satisfied the preconditions suggested for genetic evaluation among the herds. The precondition for the genetic evaluation of CR for ADG, as a productive trait, was higher than 3% and that of NBA, as a reproductive trait, was higher than 1.5%. The ADG in the Yorkshire herds showed the highest average CR. However, the average CR of ADG in the Landrace herds was lower than the criterion of the precondition. The prediction error variance of the difference (PEVD) was employed to assess the validation of the CR, as PEVDs exhibit fluctuations that are coupled with the CR across the herds. A certain degree of connectedness is essential to estimate breeding value comparisons between pig herds. This study suggests that it is possible to evaluate the genetic performance together for ADG and NBA in the Yorkshire herds since the preconditions were satisfied for these four herds. It is also possible to perform a joint genetic analysis of the ADG records of all Duroc herds since the preconditions were also satisfied. This study provides new insight into understanding the genetic connectedness of Korean pig breeding herds. CR could be utilized to accelerate the genetic progress of Korean pig breeding herds.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1047-1061
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• 2015

Stratified random sampling is a powerful sampling strategy to reduce variance of the estimators by incorporating useful auxiliary information to stratify the population. Sample allocation is the one of the important decisions in selecting a stratified random sample. There are two common methods, the proportional allocation and Neyman allocation if we could assume data collection cost for different observation units equal. Theoretically, Neyman allocation considering the size and standard deviation of each stratum, is known to be more effective than proportional allocation which incorporates only stratum size information. However, if the information on the standard deviation is inaccurate, the performance of Neyman allocation is in doubt. It has been pointed out that Neyman allocation is not suitable for multi-purpose sample survey that requires the estimation of several characteristics. In addition to sampling error, non-response error is another factor to evaluate sampling strategy that affects the statistical precision of the estimator. We propose new sample allocation methods using the available information about stratum response rates at the designing stage to improve stratified random sampling. The proposed methods are efficient when response rates differ considerably among strata. In particular, the method using population sizes and response rates improves the Neyman allocation in multi-purpose sample survey.

• Korean journal of applied entomology
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• v.24 no.1 s.62
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• pp.29-33
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• 1985

Every plant in $990m^2$ onion field was inspected for damages by the onion maggot. Maps were constructed every ten days to show which plants were infested and which were not from April 11 to May 21, 1984. The maps were sectioned into squares one of which contains 80 onion plants and the counts of damaged onions in each square were fitted to poisson and negative binomial distribution and tested by chi-square. We argue that the satisfactory fitness of the expected negative binomial $[P(x^2)>0.05]$ provided a useful description of the spatial distribution patterns of the damaged onions. Edge effect was tested by the differences of damage ratio and variance/mean ratio (${\sigma}^2/m$) between edge and center part. The result showed that the damage ratioes and variances of all the periods, ${\sigma}^2/m$ values after May 1 were greater in edge part than in center part. Again, the maps were sectioned into four blocks and the squares (sample units) were sectioned into quadrants. By application of the variance component technique, it was suggested that $2{\sim}8$ sample units for 5% sampling error and $1{\sim}2$ sample units for 10% error should be sampled randomly to estimate the damage ratio when $2{\sim}3$ quadrants were inspected.