• 대한원격탐사학회지
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• 제34권1호
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• pp.151-166
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• 2018

This paper compares the four selections of performance used in the application of genetic algorithms (GAs) to automatically optimize multispectral pixel cluster for unsupervised classification from KOMPSAT-3 data, since the selection among three main types of operators including crossover and mutation is the driving force to determine the overall operations in the clustering GAs. Experimental results demonstrate that the tournament selection obtains a better performance than the other selections, especially for both the number of generation and the convergence rate. However, it is computationally more expensive than the elitism selection with the slowest convergence rate in the comparison, which has less probability of getting optimum cluster centers than the other selections. Both the ranked-based selection and the proportional roulette wheel selection show similar performance in the average Euclidean distance using the pixel clustering, even the ranked-based is computationally much more expensive than the proportional roulette. With respect to finding global optimum, the tournament selection has higher potential to reach the global optimum prior to the ranked-based selection which spends a lot of computational time in fitness smoothing. The tournament selection-based clustering GA is used to successfully classify the KOMPSAT-3 multispectral data achieving the sufficient the matic accuracy assessment (namely, the achieved Kappa coefficient value of 0.923).

• 한국도서관정보학회지
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• 제48권2호
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• pp.323-344
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• 2017

독서동기 유발요인과 도서선택 요인은 서로 밀접하게 연관되어 있다. 그럼에도 불구하고 이러한 요인들을 포괄적으로 식별한 연구는 거의 없는 편이다. 따라서 본 연구에서는 고등학생들의 독서동기 및 도서선택에 영향을 미칠 수 있는 요인을 통합적으로 식별하고자 하였다. 이를 위하여 인문계 고등학교에 재학 중인 고등학생 15명을 대상으로 심층 면담을 실시하였고, 이를 바탕으로 독서동기 및 도서선택에 미치는 요인들을 분석하였다. 연구결과, 고등학생들의 독서 행태에 영향을 주는 요인의 유형은 정보원의 특성, 개인적 요인, 가정환경 요인, 학교 및 학습환경 요인, 사회환경 요인으로 나타났다. 특히 이러한 요인들은 복합적으로 작용하여 독서동기나 도서선택에 영향을 줄 수 있음을 알 수 있었다. 더 나아가 독서동기는 도서선택의 선행조건임을 알 수 있었고, 독서동기의 유발이 도서선택을 보장하는 것은 아닌 것으로 나타났다. 본 연구에서 식별된 각 요인들은 고등학생들의 독서행태를 보다 깊이 이해하여 독서지도에 활용하고 새로운 프로그램을 개발하는데 도움이 될 것이다.

• Journal of Yeungnam Medical Science
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• 제34권1호
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• pp.62-68
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• 2017

Background: This study was conducted to examine the academic achievements of first year medical students in one medical school based on their characteristics and student selection factors of admission. Methods: The admission scores of student selection factors (Medical Education Eligibility Test [MEET], grade point average [GPA], English test score and interview) and demographic information were obtained from 61 students who had interviewed (multiple mini interview [MMI]) for admission (38 graduate medical school students in 2014, 23 medical college-transfer students in 2015). T-tests and ANOVA were used to examine the differences in academic achievement according to the student characteristics. Correlations between admission criteria scores and academic achievements were examined. Results: MEET score was higher among graduate medical students than medical college transfer students among student selection factors for admission. There were no significant differences in academic achievement of first grade medical school between age, gender, region of high school, years after graduation and school system. The lowest interview score group showed significantly lower achievement in problem-based learning (PBL) (p=0.034). Undergraduate GPA score was positively correlated with first grade total score (r=0.446, p=0.001) among admission scores of student selection factors. Conclusion: Students with higher GPA scores tend to do better academically in their first year of medical school. In case of interview, academic achievement did not lead to differences except for PBL.

• 한국병원경영학회지
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• 제23권4호
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• pp.1-14
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• 2018

Purpose : Customers with loyalty are very important to hospitals for sustainable growth in their medical market. Individuals with loyalty are likely to visit same hospital repeatedly when they need medical services. This study was to identify factors associated with selection of specialty hospitals among customers with loyalty. Methods : The subjects of this study were 735 inpatients in 22 specialty hospitals in 6 designated fields(joints, spine, colorectal-anal, obstetrics and gynecology, ophthalmology, otolaryngology). Customer types classified as customers with high loyalty, neutral customers, and customers with low loyalty according to net promoter score(NPS). Factor analysis was conducted to classify 22 hospital selection factors into some similar properties. Logistic regression analysis was conducted to confirm the selection factors related to loyal customers. Findings : Most of specialty hospitals received high NPS of 8 points or higher in all the designated fields. Five factors associated with selection of specialty hospital are (1) hospital facilities and convenience, (2) trust in doctor and hospital, (3) rapidness of treatment, (4) hospital awareness, and (5) accessibility. As a result of logistic regression analysis, selection factors related to loyal customers were 'hospital facilities and convenience', 'trust in doctor and hospital' and 'rapidness of treatment'. Differences in the degree of importance of three selection factors by customer types appeared for each designated field. Practical Implications : This study confirms the high level of patient experience among inpatients of specialty hospitals. Factors associated with selection of hospital among inpatients with loyalty are 'facilities and convenience of hospitals', 'trust of doctor and hospital' and 'rapidness of treatment'. This study will be meaningful as basic data to systematically enhance the roles and functions of the health care system and to provide securing competitiveness according to designated fields in the management aspect of specialty hospitals.

• 과학교육연구지
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• 제39권2호
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• pp.278-289
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• 2015

연구의 목적은 영재교육대상자의 선발 방법이다. 즉, 시험 선발과 자동 진급에 따라 선발된 영재학생간의 학업정서, 메타인지, 자기효능감을 차이를 비교, 분석함으로써 선발 방법에 따른 영재 학생의 특성을 이해하고 효과적인 영재교육을 위한 영재교육대상자 판별 및 선발에 있어서의 타당한 근거를 마련해 보고자 한다. 본 연구의 결과는 다음과 같다. 첫째, 영재선발방법에 따라 선발된 영재 학생간의 학업정서, 메타인지능력 및 자기효능감의 연구에서는 시험 선발 영재학생과 자동 진급 영재 학생 간에는 유의한 차이가 없었다. 영재라는 동질 집단 조건에서는 선발방법의 차이에 따른 영향은 없는 것으로 판단된다. 둘째, 동일한 선발된 방법에 따른 비교에서, 영재학생의 학업정서를 살펴보면, 시험선발의 경우 초등과 중등 모두에서 유의한 차이가 없었으나, 자동진급에서는 중등의 영재교육기관별 분석에서 영재교육원 학생이 영재학급 학생보다 높았음을 알 수 있었다(p<.05). 메타인지능력에서 있어서는 초등에서는 차이가 없었으나, 중등의 경우 시험선발에서 남학생이, 영재교육원 학생이 높았고(p<.05), 자동진급에서는 영재교육원 학생이 높았음을 알았다(p<.05). 또한 자기효능감에서는 선발 방식에 따라 차이가 없었고 단지 자동진급에서 영재교육원 학생이 영재학급학생보다 높았음을 알았다(p<.05).

• Journal of Chest Surgery
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• 제55권4호
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• pp.277-282
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• 2022

Lung transplantation is a life-saving procedure in patients with end-stage lung disease. However, it inherently depends on the availability of donor organs. The selection of suitable lungs for transplantation, management of donors to minimize further injury and improve organ function, and safe procurement remain critical for successful transplantation. In this review, we provide an update on the current understanding of donor selection, management, and lung procurement.

• 한국비블리아학회지
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• 제34권3호
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• pp.297-318
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• 2023

이 연구는 중학생을 위한 독서교육의 자료로써 그림책을 선정하기 위한 기준을 제안하고 이를 도서 선정에 적용이 가능한가를 확인하는 것을 목적으로 한다. 먼저 선행연구를 통하여 독서자료로써 그림책의 교육적 가치와 학문 분야별 그림책 선정 기준을 확인하였다. 선행연구에서 제시한 다양한 그림책 선정 기준의 공통점을 일러스트와 텍스트, 그 외로 구분해 범주화하여 통합한 후, 중학생을 독자로 선정해 적용할 수 있는 선정 기준을 구안하였다. 통합된 그림책 선정 기준을 내용으로 현직 사서교사에게 중학생을 대상으로 그림책을 선정할 때 고려할 주요한 기준을 묻는 설문조사와 그림책 교육 경험이 있는 전문가를 대상으로 집중 인터뷰를 실시하였다. 그 결과로 글과 관련된 2개의 기준, 그림과 관련된 4개의 기준, 그리고 그 외 5개 기준으로 구성된 중학생을 위한 그림책 선정 기준을 제시하였다. 이 선정 기준의 실제성을 확인하기 위하여 어린이도서연구회 추천 그림책(13세 이상)을 대상으로 범주별 기준의 적용 여부를 확인하였다. 중학생 대상 22권의 그림책 가운데 선정 기준의 모든 범주별 적용이 가능한 도서는 15권으로 유의미한 실제성을 확인할 수 있었다.

• 한국경영과학회지
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• 제41권3호
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• pp.1-21
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• 2016

The Markowitz portfolio selection model uses estimators to deduce input parameters. However, the estimation errors of input parameters negatively influence the performance of portfolios. Therefore, this model cannot be reliably applied to real-world investments. To overcome this problem, we suggest an algorithm that can exclude stocks with large estimation error from the portfolio by applying a tracking signal to the Markowitz portfolio selection model. By calculating the tracking signal of each stock, we can monitor whether unexpected departures occur on the outcomes of the forecasts on rate of returns. Thereafter, unreliable stocks are removed. By using this approach, portfolios can comprise relatively reliable stocks that have comparatively small estimation errors. To evaluate the performance of the proposed approach, a 10-year investment experiment was conducted using historical stock returns data from 6 different stock markets around the world. Performance was assessed and compared by the Markowitz portfolio selection model with additional constraints and other benchmarks such as minimum variance portfolio and the index of each stock market. Results showed that a portfolio using the proposed approach exhibited a better Sharpe ratio and rate of return than other benchmarks.

• Asian-Australasian Journal of Animal Sciences
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• 제31권1호
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• pp.19-25
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• 2018

Objective: In practical breeding, selection is often performed by ignoring the accuracy of evaluations and applying economic weights directly to the selection index coefficients of genetically standardized traits. The denominator of the standardized component trait of estimated genetic evaluations in practical selection varies with its reliability. Whereas theoretical methods for calculating the selection index coefficients of genetically standardized traits account for this variation, practical selection ignores reliability and assumes that it is equal to unity for each trait. The purpose of this study was to clarify the effects of ignoring the accuracy of the standardized component trait in selection criteria on selection responses and economic weights in retrospect. Methods: Theoretical methods were presented accounting for reliability of estimated genetic evaluations for the selection index composed of genetically standardized traits. Results: Selection responses and economic weights in retrospect resulting from practical selection were greater than those resulting from theoretical selection accounting for reliability when the accuracy of the estimated breeding value (EBV) or genomically enhanced breeding value (GEBV) was lower than those of the other traits in the index, but the opposite occurred when the accuracy of the EBV or GEBV was greater than those of the other traits. This trend was more conspicuous for traits with low economic weights than for those with high weights. Conclusion: Failure of the practical index to account for reliability yielded economic weights in retrospect that differed from those obtained with the theoretical index. Our results indicated that practical indices that ignore reliability delay genetic improvement. Therefore, selection practices need to account for reliability, especially when the reliabilities of the traits included in the index vary widely.

• Geomechanics and Engineering
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• 제25권1호
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• pp.1-16
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• 2021

Machine learning models have been widely used for landslide susceptibility assessment (LSA) in recent years. The large number of inputs or conditioning factors for these models, however, can reduce the computation efficiency and increase the difficulty in collecting data. Feature selection is a good tool to address this problem by selecting the most important features among all factors to reduce the size of the input variables. However, two important questions need to be solved: (1) how do feature selection methods affect the performance of machine learning models? and (2) which feature selection method is the most suitable for a given machine learning model? This paper aims to address these two questions by comparing the predictive performance of 13 feature selection-based machine learning (FS-ML) models and 5 ordinary machine learning models on LSA. First, five commonly used machine learning models (i.e., logistic regression, support vector machine, artificial neural network, Gaussian process and random forest) and six typical feature selection methods in the literature are adopted to constitute the proposed models. Then, fifteen conditioning factors are chosen as input variables and 1,017 landslides are used as recorded data. Next, feature selection methods are used to obtain the importance of the conditioning factors to create feature subsets, based on which 13 FS-ML models are constructed. For each of the machine learning models, a best optimized FS-ML model is selected according to the area under curve value. Finally, five optimal FS-ML models are obtained and applied to the LSA of the studied area. The predictive abilities of the FS-ML models on LSA are verified and compared through the receive operating characteristic curve and statistical indicators such as sensitivity, specificity and accuracy. The results showed that different feature selection methods have different effects on the performance of LSA machine learning models. FS-ML models generally outperform the ordinary machine learning models. The best FS-ML model is the recursive feature elimination (RFE) optimized RF, and RFE is an optimal method for feature selection.