• 한국학교ㆍ지역보건교육학회지
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• 제12권3호
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• pp.17-28
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• 2011

Backgroud & Objectives: The purpose of the study was to develop an innovative blended learning method on life style and health promotion and evaluate the educational effects for university students. Methods: The blended learning was developed to combine face-to-face lecture(off-line lecture) and on-line lecture that applied the subject of life style and health promotion. This course is a coordinated effort towards providing 5 topics of lifestyle such as smoking, alcohol, exercise, diet, and stress management. This has been verified by an expert in the field of nursing, education, e-learning technician and students. Participants were different part of university students (n=28) with major enrolled in a general culture course for 2 credits which composed of 8 sessions of each 2-hour in the first semester of 2010. The study was a one group posttest design. A self-report about health knowledge, attitude, and health behavior was organized by content analysis after the sessions. Results: Positive feedbacks from students were reflected in the outcome. Student regarded good lifestyle as being the most important. Student concerned those on-line lectures are not only available at most time and site, but also good for individualization, visual understanding and interest. Face-to-face lecture provided student a chance to integrate with knowledge and experience and had desire to improve good lifestyle and health promotion. Conclusions: The blended learning method on good lifestyle and health could make a best use of improvement for knowledge, attitude and behavior concerning. It is needed to identify the long term effects of a blended learning for further study.

• 대한수학교육학회지:학교수학
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• 제5권2호
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• pp.191-208
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• 2003

본 연구는 수학 및 과학 영재 아동과 일반 아동의 성격유형과 학습양식의 차이를 알아보는 것을 목적으로 하였다. 이를 위해 수학 및 과학 영재교육을 받고 있는 부산광역시 소재의 초등학교 5, 6학년 135명과 일반아동 66명을 대상으로 하여 MMTIC과 학습양식검사를 실시하였다. 성격유형의 분석은 선호지표와 기능별, 기질별 분포를 중심으로 하였고, 학습양식은 독립형, 의존형, 협동형, 경쟁형, 참여형, 회피형의 유형으로 분류되었다. 연구결과에 의하면, 수학 및 과학 영재 아동은 성격유형, 학습양식 그리고 성격유형에 따른 학습양식에서 큰 차이가 없었으나, 일반 아동과는 유의한 차이를 나타냈다. 또한 연구대상의 성격유형에 따라 선호하는 학습양식에는 차이가 있는 것으로 나타났다.

• Geomechanics and Engineering
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• 제38권5호
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• pp.517-528
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• 2024

For numerous tunnelling projects implemented in urban areas due to limited space, it is crucial to take into account the interaction between the foundation, ground, and tunnel. In predicting the deformation of piled foundations and the ground during twin tunnel excavation, it is essential to consider various factors. Therefore, this study derived a prediction model for pile group settlement using machine learning to analyze the importance of various factors that determine the settlement of piled foundations during twin tunnelling. Laboratory model tests and numerical analysis were utilized as input data for machine learning. The influence of each independent variable on the prediction model was analyzed. Machine learning techniques such as data preprocessing, feature engineering, and hyperparameter tuning were used to improve the performance of the prediction model. Machine learning models, employing Random Forest (RF), eXtreme Gradient Boosting (XGB), and Light Gradient Boosting Machine (LightGBM, LGB) algorithms, demonstrate enhanced performance after hyperparameter tuning, particularly with LGB achieving an R² of 0.9782 and RMSE value of 0.0314. The feature importance in the prediction models was analyzed and P_N was the highest at 65.04% for RF, 64.81% for XGB, and P_CTC (distance between the center of piles) was the highest at 31.32% for LGB. SHAP was utilized for analyzing the impact of each variable. P_N (the number of piles) consistently exerted the most influence on the prediction of pile group settlement across all models. The results from both laboratory model tests and numerical analysis revealed a reduction in ground displacement with varying pillar spacing in twin tunnels. However, upon further investigation through machine learning with additional variables, it was found that the number of piles has the most significant impact on ground displacement. Nevertheless, as this study is based on laboratory model testing, further research considering real field conditions is necessary. This study contributes to a better understanding of the complex interactions inherent in twin tunnelling projects and provides a reliable tool for predicting pile group settlement in such scenarios.

• 성인간호학회지
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• 제20권5호
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• pp.778-790
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• 2008

Purpose: This study was conducted to develop Web-based multimedia contents for supporting student nurses' clinical practice on critical care, and to evaluate learners' responses. Methods: Based on the steps of Assessment, Design, Development, Implementation, & Evaluation(ADDIE) model, a total of 13 self-directed learning modules including live lectures and real video clips were developed through faculty collaboration of nine nursing colleges in Gwangju and Chonnam province. The finally developed multimedia contents were published on the Web of the learning management system at a local e-learning center. Results: The Web contents were evaluated after self-learning by 81 junior college nursing students who were encouraged to study it at their own pace during their two-week clinical practice at a medical or surgical intensive care unit of a university hospital and two hospitals. The knowledge (t = -27.66, p < .001) and self-evaluated clinical performance level(t = 7.54, p < .001) were significantly increased after learning of the Web contents and clinical practice, and satisfaction level that measured post-test only was 4.0 out of 5 point. Conclusion: The use of Web contents for critical care need to be extended as a complimentary material in a class room lecture or clinical practice of students to increase their self-learning ability and understandings of clinical knowledge and situation.

• 한국물환경학회지
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• 제38권6호
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• pp.292-305
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• 2022

The Yeongsan River, one of the four largest rivers in South Korea, has been facing difficulties with water quality management with respect to algal bloom. The algal bloom menace has become bigger, especially after the construction of two weirs in the mainstream of the Yeongsan River. Therefore, the prediction and factor analysis of Chlorophyll-a (Chl-a) concentration is needed for effective water quality management. In this study, Chl-a prediction model was developed, and the performance evaluated using machine and deep learning methods, such as Deep Neural Network (DNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Moreover, the correlation analysis and the feature importance results were compared to identify the major factors affecting the concentration of Chl-a. All models showed high prediction performance with an R² value of 0.9 or higher. In particular, XGBoost showed the highest prediction accuracy of 0.95 in the test data.The results of feature importance suggested that Ammonia (NH₃-N) and Phosphate (PO₄-P) were common major factors for the three models to manage Chl-a concentration. From the results, it was confirmed that three machine learning methods, DNN, RF, and XGBoost are powerful methods for predicting water quality parameters. Also, the comparison between feature importance and correlation analysis would present a more accurate assessment of the important major factors.

• 농촌지도와개발
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• 제8권2호
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• pp.179-190
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• 2001

The purpose of this study was to analysis the education effects of web-based on-line cyber program mesaured by Kirkpatrick’s evaluation process. The average score on satisfaction of the program was 4.28(.59), which was designed to evaluate the level 1, reaction. To test level 2, learning, the average score that students achieved was calculated and it was 86.87(std.=7.05) in the term examinations. The level 3, job months. It was reported that most employees who took the course are utilizing the knowledge that they acquired from the course(mean=3.80, std.=.77). To identify the level 4, business results, the mean score of the number of accidents and near misses that happened in their factories for 3 months before and after the course were compared. There was statistically significant difference between the number of accidents that happened 3 months before the course and 3 months after the course, at the significance level of .01, which was tested by Paired t-test.

• 한국멀티미디어학회논문지
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• 제21권10호
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• pp.1182-1194
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• 2018

The Dynamic Adaptive Streaming over HTTP(DASH) is envisioned to evolve to meet an increasing demand on providing seamless video streaming services in the near future. The DASH performance heavily depends on the client's adaptive quality selection algorithm that is not included in the standard. The existing conventional algorithms are basically based on a procedural algorithm that is not easy to capture and reflect all variations of dynamic network and traffic conditions in a variety of network environments. To solve this problem, this paper proposes a novel quality selection mechanism based on the Deep Q-Network(DQN) model, the DQN-based DASH Adaptive Bitrate(ABR) mechanism. The proposed mechanism adopts a new reward calculation method based on five major performance metrics to reflect the current conditions of networks and devices in real time. In addition, the size of the consecutive video segment to be downloaded is also considered as a major learning metric to reflect a variety of video encodings. Experimental results show that the proposed mechanism quickly selects a suitable video quality even in high error rate environments, significantly reducing frequency of quality changes compared to the existing algorithm and simultaneously improving average video quality during video playback.

• Nuclear Engineering and Technology
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• 제56권4호
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• pp.1372-1384
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• 2024

The discrimination of the source for xenon gases' release can provide an important clue for detecting the nuclear activities in the neighboring countries. In this paper, three machine learning techniques, which are logistic regression, support vector machine (SVM), and k-nearest neighbors (KNN), were applied to develop the predictive models for discriminating the source for xenon gases' release based on the xenon isotopic activity ratio data which were generated using the depletion codes, i.e., ORIGEN in SCALE 6.2 and Serpent, for the probable sources. The considered sources for the neighboring countries of South Korea include PWRs, CANDUs, IRT-2000, Yongbyun 5 MWe reactor, and nuclear tests with plutonium and uranium. The results of the analysis showed that the overall prediction accuracies of models with SVM and KNN using six inputs, all exceeded 90%. Particularly, the models based on SVM and KNN that used six or three xenon isotope activity ratios with three classification categories, namely reactor, plutonium bomb, and uranium bomb, had accuracy levels greater than 88%. The prediction performances demonstrate the applicability of machine learning algorithms to predict nuclear threat using ratios of xenon isotopic activity.

• The Korean journal of internal medicine
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• 제39권4호
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• pp.555-562
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• 2024

This study reviews the recent progress of artificial intelligence for colonoscopy from detection to diagnosis. The source of data was 27 original studies in PubMed. The search terms were "colonoscopy" (title) and "deep learning" (abstract). The eligibility criteria were: (1) the dependent variable of gastrointestinal disease; (2) the interventions of deep learning for classification, detection and/or segmentation for colonoscopy; (3) the outcomes of accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1, intersection of union (IOU), Dice and/or inference frames per second (FPS); (3) the publication year of 2021 or later; (4) the publication language of English. Based on the results of this study, different deep learning methods would be appropriate for different tasks for colonoscopy, e.g., Efficientnet with neural architecture search (AUC 99.8%) in the case of classification, You Only Look Once with the instance tracking head (F1 96.3%) in the case of detection, and Unet with dense-dilation-residual blocks (Dice 97.3%) in the case of segmentation. Their performance measures reported varied within 74.0-95.0% for accuracy, 60.0-93.0% for sensitivity, 60.0-100.0% for specificity, 71.0-99.8% for the AUC, 70.1-93.3% for precision, 81.0-96.3% for F1, 57.2-89.5% for the IOU, 75.1-97.3% for Dice and 66-182 for FPS. In conclusion, artificial intelligence provides an effective, non-invasive decision support system for colonoscopy from detection to diagnosis.

• 정보교육학회논문지
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• 제14권3호
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• pp.291-300
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• 2010

협동 학습은 학습자들을 소그룹으로 나누어 상호 협력하여 학습하게 함으로써 학습자의 학업 성취감을 향상시키는 것을 그 목적으로 한다. 그러므로 효과적인 소그룹 생성을 위한 몇몇 기존 연구들이 존재하며, 대부분 연구에서는 교과목, 교수자, 그리고 학습자의 정보로부터 변인 요소들을 추출하고 이를 기반으로 소그룹을 생성한다. 그러나 아직까지 많은 연구들은 특정 교과목에 의존적인 그룹 생성 방법을 제안하고 있을 뿐 다양한 교과목을 대상으로 그룹을 생성하기위한 방법을 제안한 연구는 많지 않다. 더욱이 그룹 생성을 위해 자동화된 시스템을 제안한 연구는 찾아보기 힘들다. 본 논문에서는 다양한 교과목 상황에 따라 그에 맞는 소그룹을 자동으로 생성하는 시스템을 제안한다. 제안된 시스템은 교과목의 기본 정보만을 입력받아 자동으로 그룹을 생성하거나 사용자가 필요하다고 판단되면 추가로 변인 요소를 입력받아 자동으로 소그룹을 생성한다. 본 논문에서는 다양한 변인 요소를 반영하기 위하여 규칙(Rule)을 정의하고 규칙을 기반으로 소그룹을 생성하는 방법을 제안한다. 또한 본 논문은 제안한 그룹 생성 시스템의 사용성을 평가하여 다양한 교과목이 존재하는 대학교육을 대상으로 실제 응용에서 활용 가능함을 보인다.