• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.205-208
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• 2003

대학이 신입생을 선발하는 기준은 해당전공을 공부하는데 적합한 지원자를 선발하는 것이며, 해당모집단위에 많이 지원하게 하여 신입생 충원을 용이하게 하는 것 등이다. 대학은 입학전형을 거쳐서 신입생을 선발하게 되며, 선발기준으로 전형요소를 활용하게 된다. 전형요소 활용방법은 신입생 선발에 영향을 주기 때문에 전략적으로 중요하다. 2005학년도 신입생 선발에는 고등학교 7차 교육과정을 이수한 지원자들이 지원하게 되므로 전형요소에 있어서 변화가 있다. 대학입학수학능력 시험의 체제는 수험생들이 영역이나 과목을 선택하여 응시하는 방향으로 변화한다. 즉, 수리 영역을 가형 및 나형으로 응시하고, 하나의 탐구영역을 응시하되 사회탐구영역 및 과학탐구영역은 4과목 이내에서 선택하여 응시한다. 또한 수학능력시험의 성적표는 각 영역별 및 과목별 표준점수, 백분위점수 및 등급을 표시하여 통지한다. 본 연구는 변화된 수학능력시험의 체제와 고등학교 교육과정을 어떠한 방법으로 반영하여 학생을 선발하는 것이 바람직한가에 대한 전략을 도출하는 틀을 제시하고, 각 전형요소 활용의 대안과 문제점을 도출하고자 한다. 2005 수능시험 결과는 표준점수로 통지하기 때문에 만점 개념을 적용하기 어렵고, 표준점수를 전형요소로 활용할 때 전형총점 개념을 도입하기 어렵다. 또한 복수영역 및 과목의 선택에서 유리함과 불리함이 나타나게 된다. 과거의 수능시험결과를 분석하여 전형총점개념 도입의 방법과 불리함을 보정하여 주는 방법을 제시하고, 신입생을 선발하는 목적에 적합한 전형요소 결정전략을 도출하고자 한다.2; Learning Decisions, 2001) 연구모형을 설정하고 이를 근거로 실증연구를 수행 중에 있다.7.2 $e^{0.101}$x/, y = 70.01 $e^{0.030}$x/, 반감기는 12.0, 6.86, 23.0 일이고 폐장, 간장, 신장의 회복기간(x)별 크롬농도(y)의 소실속도 상관계수 (노출농도 0.50 mg/㎥군의 경우)는 y = 1808 $e^{0.004}$93x/, y = 12.02 $e^{0.029}$7x/, y = 67.61 $e^{0.029}$2x/ 반감기는 140.6, 23.3, 23.7 일로 평가되었다. 4. 고찰 : 실험동물의 전혈, 혈청, 뇨에서의 크롬농도와 시험물질 노출농도는 밀접한 상관을 가졌으나 농도에 정비례하지는 않았다. 뇨 중 흡수된 크롬의 경우 회복기간 초기 (12시간 내)에 대부분 배설이 일어나는 것으로 나타났다. 폐장이 간장, 신장 등 다른 장기에 비해 높은 축적량을 보였으며 축적된 크롬농도가 높을수록 크롬의 소실속도는 현저히 저하하는 경향을 보였다. 노출농도가 높을수록 각 장기조직 내 크롬의 소실속도 (clearance)는 크게 감소경향이 있었으며 이는 체내 과부하시 자정작용이 감소하는 것으로 판단되었다. 본 연구 결과 SD rat를 이용 반복흡입노출의 경우 생체의 무유해영향농도 (NOAEL)는 0.2mg/㎥이하이며 발암물질을 감안하여 안전계수를 100으로 할 경우 사람에 대한 NOAEL은 0.002mg/㎥이하로 판단되

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.421-427
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• 2016

This study was conducted to examine the effects of a gerontological nursing clinical practice course using action learning on undergraduate nursing students. The subjects were 75 undergraduate nursing students from Y University. Data were analyzed using SPSS/Win 21.0. There were significant improvements in problem solving (t=3.58, p<.001) and communication (t=4.15, p<.000) in the experimental group compared to the control group. This study provides evidence that gerontological nursing courses improve undergraduate nursing students' problem solving and communication skills. Accordingly, this course would be a useful teaching and learning method in nursing programs of outcome based curriculum.

• Journal of Korean Medical Science
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• v.33 no.43
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• pp.239.1-239.12
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• 2018

Background: We described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system. Methods: A 5-step retinal fundus image reading tool was developed that rates image quality, presence of abnormality, findings with location information, diagnoses, and clinical significance. Each image was evaluated by 3 different graders. Agreements among graders for each decision were evaluated. Results: The 234,242 readings of 79,458 images were collected from 55 licensed ophthalmologists during 6 months. The 34,364 images were graded as abnormal by at-least one rater. Of these, all three raters agreed in 46.6% in abnormality, while 69.9% of the images were rated as abnormal by two or more raters. Agreement rate of at-least two raters on a certain finding was 26.7%-65.2%, and complete agreement rate of all-three raters was 5.7%-43.3%. As for diagnoses, agreement of at-least two raters was 35.6%-65.6%, and complete agreement rate was 11.0%-40.0%. Agreement of findings and diagnoses were higher when restricted to images with prior complete agreement on abnormality. Retinal/glaucoma specialists showed higher agreements on findings and diagnoses of their corresponding subspecialties. Conclusion: This novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.781-791
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• 2022

It is crucial to provide forest fire risk forecast information to minimize forest fire-related losses. In this research, forecast models of forest fire risk at a mid-range (with lead times up to 7 days) scale were developed considering past, present and future conditions (i.e., forest fire risk, drought, and weather) through random forest machine learning over South Korea. The models were developed using weather forecast data from the Global Data Assessment and Prediction System, historical and current Fire Risk Index (FRI) information, and environmental factors (i.e., elevation, forest fire hazard index, and drought index). Three schemes were examined: scheme 1 using historical values of FRI and drought index, scheme 2 using historical values of FRI only, and scheme 3 using the temporal patterns of FRI and drought index. The models showed high accuracy (Pearson correlation coefficient >0.8, relative root mean square error <10%), regardless of the lead times, resulting in a good agreement with actual forest fire events. The use of the historical FRI itself as an input variable rather than the trend of the historical FRI produced more accurate results, regardless of the drought index used.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.891-907
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• 2023

Compact Advanced Satellite 500-4 (CAS500-4) is scheduled to be launched to collect high spatial resolution data focusing on vegetation applications. To achieve this goal, accurate surface reflectance retrieval through atmospheric correction is crucial. Therefore, a machine learning-based atmospheric correction algorithm was developed to simulate atmospheric correction from a radiative transfer model using Sentinel-2 data that have similarspectral characteristics as CAS500-4. The algorithm was then evaluated mainly for forest areas. Utilizing the atmospheric correction parameters extracted from Sentinel-2 and GEOKOMPSAT-2A (GK-2A), the atmospheric correction algorithm was developed based on Random Forest and Light Gradient Boosting Machine (LGBM). Between the two machine learning techniques, LGBM performed better when considering both accuracy and efficiency. Except for one station, the results had a correlation coefficient of more than 0.91 and well-reflected temporal variations of the Normalized Difference Vegetation Index (i.e., vegetation phenology). GK-2A provides Aerosol Optical Depth (AOD) and water vapor, which are essential parameters for atmospheric correction, but additional processing should be required in the future to mitigate the problem caused by their many missing values. This study provided the basis for the atmospheric correction of CAS500-4 by developing a machine learning-based atmospheric correction simulation algorithm.

• Journal of the Korean GEO-environmental Society
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• v.14 no.9
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• pp.31-37
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• 2013

In this study installation diameter, interval, area replacement ratio and ground hardness of applicable ground in C.G.S method should be mastered through surrounding ground by conducting modeling. Optimum artificial neural network was selected through the study of the parameter of artificial neural network and prediction model was developed by the relationship with numerical analysis and artificial neural network. As this result, C.G.S pile settlement and ground settlement were found to be equal in terms of diameter, interval, area replacement ratio and ground hardness, presented in a single curve, which means that the behavior pattern of applied ground in C.G.S method was presented as some form, and based on such a result, learning the artificial neural network for 3D behavior was found to be possible. As the study results of artificial neural network internal factor, when using the number of neural in hidden layer 10, momentum constant 0.2 and learning rate 0.2, relationship between input and output was expressed properly. As a result of evaluating the ground behavior of C.G.S method which was applied to using such optimum structure of artificial neural network model, is that determination coefficient in case of C.G.S pile settlement was 0.8737, in case of ground settlement was 0.7339 and in case of ground heaving was 0.7212, sufficient reliability was known.

• Journal of The Korean Association For Science Education
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• v.38 no.1
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• pp.57-68
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• 2018

The purpose of this study is to categorize learning style groups and to analyze students' learning interest and self-regulated learning abilities, according to their learning style and giftedness. One hundred and twenty-three (123) science-gifted student and 296 regular students participated in this study, responding to learning style, self-regulated learning, and learning interest questionnaires. Data were analyzed, using 2-stage cluster analysis, $x^2$ test, two way-MANOVA test, and $Scheff{\acute{e}}$ test. The results are as follows: First, by 2-stage cluster analysis, four groups were categorized: 'high-score thinking style,' 'external-liberal,' 'executive-conservative,' and 'low-score thinking style.' In the gifted group, high-score thinking style (51.2%) was the most popular, then executive-conservative (30.2%), external-liberal (17.1%), and low-score thinking style (1.6%); in the regular student group, the executive-conservative group was the biggest, then high-score thinking style (20.6%), external-liberal (11.6%), and then the low-score thinking style (8.7%). Second, in terms of learning interest, the analysis by thinking style showed that the high-score thinking style group had higher learning interest compared to the executive-conservative and the low-thinking style group. The high-thinking style group's thoughtful interest also scored the highest compared with the others. The gifted students' thoughtful interest and investigative interest also were higher than regular students '. Third, in terms of the self-regulated learning, the analysis by thinking style showed that the high-score thinking style group showed higher scores on all sub-variances than other groups, especially having highest control-belief scores. Also, gifted students had higher scores on control-belief and searching information. Based on these results, the ways for effective education and support were discussed.

• Journal of Gastric Cancer
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• v.18 no.2
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• pp.172-181
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• 2018

Purpose: This study aimed to evaluate the surgical outcomes and investigate the feasibility of reduced-port laparoscopic gastrectomy using learning curve analysis in a small-volume center. Materials and Methods: We reviewed 269 patients who underwent laparoscopic distal gastrectomy (LDG) for gastric carcinoma between 2012 and 2017. Among them, 159 patients underwent reduced-port laparoscopic gastrectomy. The cumulative sum technique was used for quantitative assessment of the learning curve. Results: There were no statistically significant differences in the baseline characteristics of patients who underwent conventional and reduced-port LDG, and the operative time did not significantly differ between the groups. However, the amount of intraoperative bleeding was significantly lower in the reduced-port laparoscopic gastrectomy group (56.3 vs. 48.2 mL; P<0.001). There were no significant differences between the groups in terms of the first flatus time or length of hospital stay. Neither the incidence nor the severity of the complications significantly differed between the groups. The slope of the cumulative sum curve indicates the trend of learning performance. After 33 operations, the slope gently stabilized, which was regarded as the breakpoint of the learning curve. Conclusions: The surgical outcomes of reduced-port laparoscopic gastrectomy were comparable to those of conventional laparoscopic gastrectomy, suggesting that transition from conventional to reduced-port laparoscopic gastrectomy is feasible and safe, with a relatively short learning curve, in a small-volume center.

• Communications of Mathematical Education
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• v.29 no.2
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• pp.215-239
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• 2015

A goal of this study is figuring out how fraction learning centered on various representation activities influences the fraction comprehension and mathematical attitudes. The study focused on 33 4th-grade students of B elementary school in Seoul. In the study, 15 fraction learning classes comprising enactive, iconic, and symbolic representations took place over 6 weeks. After the classes, the ratio of the students who achieved relational understanding increased and the students averagely recorded 90 pt or more on the fraction comprehension test I, II and III. Two-dependent samples t-test was conducted to analyze a significant difference in mathematical attitudes between pre-test and post-test. On the test result, there was the meaningful difference with 0.01 level of significance. To conclude, the fraction learning centered on various representation activities improves students' relational understanding and fraction understanding. In addition, the fraction learning centered on various representation activities gives positive influences on mathematical attitudes since it increases learning orientation, self-control, interests, value cognition, and self-confidence of the students and decreases fears of the students.

• Journal of Animal Science and Technology
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• v.65 no.2
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• pp.365-376
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• 2023

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.