• Journal of Digital Convergence
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• v.18 no.12
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• pp.469-479
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• 2020

This study was attemted to grasp the factors affecting the caring efficacy of senior nursing students. Data were collected from 173 nursing students at J university in G-do. Analysis was done using t-test, ANOVA, Pearson correlation coefficient, and Multiple regression with IBM SPSS WIN/25.0. Caring efficacy was positively correlated with clinical learning environment (r=.42, p<.001), field practice adaptation (r=.53, p<.001), nursing professionalism (r=.42, p<.001), and negatively correlated to clinical practice powerlessness (r=-.46, p<.001). The most influential factor on the subjects' caring efficacy was field practice adaptation (β=.330, p<.001), followed by nursing professionalism (β=.188, p=.005), clinical learning environment (β=.176, p=.015), introvert (β=-.146, p=.018), and extrovert (β=.134, p=.035). The explanatory power was 41.8%. Therefore, systematic nursing programs that can enhance caring efficacy are needed. Also, the following data can be utilized as basic data to help develop caring efficacy programs.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Wind and Structures
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• v.36 no.6
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• pp.405-421
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• 2023

Strong wind is the main factors of wind-damage of high-rise buildings, which often creates largely economical losses and casualties. Wind pressure plays a critical role in wind effects on buildings. To obtain the high-resolution wind pressure field, it often requires massive pressure taps. In this study, two traditional methods, including bilinear and bicubic interpolation, and two deep learning techniques including Residual Networks (ResNet) and Generative Adversarial Networks (GANs), are employed to reconstruct wind pressure filed from limited pressure taps on the surface of an ideal building from TPU database. It was found that the GANs model exhibits the best performance in reconstructing the wind pressure field. Meanwhile, it was confirmed that k-means clustering based retained pressure taps as model input can significantly improve the reconstruction ability of GANs model. Finally, the generalization ability of k-means clustering based GANs model in reconstructing wind pressure field is verified by an actual engineering structure. Importantly, the k-means clustering based GANs model can achieve satisfactory reconstruction in wind pressure field under the inputs processing by k-means clustering, even the 20% of pressure taps. Therefore, it is expected to save a huge number of pressure taps under the field reconstruction and achieve timely and accurately reconstruction of wind pressure field under k-means clustering based GANs model.

• Journal of The Korean Association For Science Education
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• v.28 no.5
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• pp.471-481
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• 2008

This study investigated connecting errors by students' field independence-dependence in learning chemistry concepts with multiple external representations in current science textbooks. Seventh graders (N=196) at a middle school were assigned to the BL and CL groups, which were respectively taught "Boyle's Law" and "Charles's Law." A field independence-dependence test was administered. After learning the target concept with text and picture emphasizing the particulate nature of matter, a connecting test was also administered. Five types of connecting errors were identified: Insufficient connection, misconnection, rash connection, impossible connection, and failing to connect. 'Failing to connect,' 'Misconnection,' and 'Rash connection' were found to be the frequent types of connecting errors regardless of the target concepts. The frequencies and percentages of the types of connecting errors were not significantly different between the field independent and field dependent students. Educational implications of these findings are discussed.

• Journal of the Korean Society of Earth Science Education
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• v.1 no.1
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• pp.28-40
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• 2008

This study is the analysis of the effects of Jigsaw II Cooperative Learning upon the academic achievement and upon the ability of self-directed learning, compared to lecturing. It made it experimental target for two male and two female classes of students in the 2nd grade of humanity high school in Ulsan metropolitan area. One male and one female classes conducted Jigsaw II cooperative learning by making up a small group heterogeneously from the aspect of learning ability, and the other male and female classes carried out the lecturing focusing on a teacher. As for the academic achievement of science, Jigsaw II cooperative learning was shown to be effective compared to the lecturing. As for the ability of self-directed learning, Jigsaw II cooperative learning was indicated to be effective compared to the lecturing. As for the ability of self-directed learning for Jigsaw II cooperative learning, it was indicated to be effective compared to the lecturing. Given seeing this, it was identified the necessity for Jigsaw II cooperative learning to be applied to a school field as an alternative plan for the lecturing.

• Journal of The Korean Association For Science Education
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• v.29 no.2
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• pp.156-167
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• 2009

This study investigated the effects of dynamic visual on students' field independence-dependence on connecting errors and conceptual understanding in learning chemistry concepts with multiple representations. Seventh graders (N=123) at a co-ed middle school were assigned to a static visual (SV) group learning with text and static visual, and a dynamic visual (DV) group learning with text and dynamic visual. The students then learned 'Boyle's Law' and 'Charles's Law' for two class periods. Results revealed that the percentages of the DV group were lower than those of the SV group on connecting errors. However, the percentages of the students' connecting errors were still high regardless of their field independence-dependence. There was a little different tendency in the percentages of connecting errors between the two groups by students' field independence-dependence according to the types of connecting errors. The scores of the DV group were significantly higher than those of the SV group in a test on conceptual understanding. However, there was no significant interaction between the instruction and the students' field independence-dependence. Educational implications of these findings are discussed.

• 대한공업교육학회지
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• v.32 no.2
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• pp.87-103
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• 2007

The purpose of this study was to develop of the smoke flow visualization device of learning wind tunnel, teaching-learning materials in order to demonstrate air-flow around the fluid-flow field qualitatively and understand the resistance concepts of fluid-flow in secondary school. The contents of this study were consisted of the development and experiment of smoke flow visualization for learning wind tunnel. The main results of this study were as follows: First, this developed teaching-learning material here will help students understand the fundamental physical phenomena related with the resistance of fluid and the various patterns of air-flow in the field of transportation technology. Second, flow visualization has shown the same tendency in both of theoretical and experimental patterns. Third, the airfoil model has the smallest wake region meaning resistance against air-flow of circular cylinder and square rod model. Forth, flow separation point at leading edge and wide wake region began to show under the angle of attack of airfoil model ${\alpha}$ is $20^{\circ}$. Fifth, the wake width of the flow field behind a golf ball with dimple became slightly narrower than that without dimple. Sixth, the developed device was made to apply the teaching and learning materials for the experiment and practice in order to increase students' interest and attitude.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.500-520
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• 2010

The purposes of this case study were (1) to explore one experienced teacher's views on Earth Systems Education and (2) to describe and document the characteristics of the Earth Systems Education (ESE) curriculum provided by an exemplary middle school science teacher, Dr. J. All the essential pieces of evidence were collected from observations, interviews with the experienced teacher and his eighth grade students, informal conversations, document analysis, and field notes. The $NUD^*IST$ for MS Windows was used for an initial data reduction process and to narrow down the focus of an analysis. All transcriptions and written documents were reviewed carefully and repeatedly to find rich evidence through inductive and content analysis. The findings revealed that ESE provided a conceptual focus and theme for organizing his school curriculum. The curriculum offered opportunities for students to learn relevant local topics and to connect the classroom learning to the real world. The curriculum also played an important role in developing students' value and appreciation of Earth systems and concern for the local environment. His instructional strategies were very compatible with recommendations from a constructivist theory. His major teaching methodology and strategies were hands-on learning, authentic activities-based learning, cooperative learning, project-based learning (e.g., mini-projects), and science field trips. With respect to his views about benefits and difficulties associated with ESE, the most important benefit was that the curriculum provided authentic-based, hands-on activities and made connections between students and everyday life experiences. In addition, he believed that it was not difficult to teach using ESE. However, the lack of time devoted to field trips and a lack of suitable resource materials were obstacles to the implementation of the curriculum. Implications for science education and future research are suggested.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.49-56
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• 2020

Detecting shadows in images and restoring or removing them was a very challenging task in computer vision. Traditional researches used color information, edges, and thresholds to detect shadows, but there were errors such as not considering the penumbra area of shadow or even detecting a black area that is not a shadow. Deep learning has been successful in various fields of computer vision, and research on applying deep learning has started in the field of shadow detection and removal. However, it was very difficult and time-consuming to collect data for network learning, and there were many limited conditions for shooting. In particular, it was more difficult to obtain shadow data from buildings and satellite images, which hindered the progress of the research. In this paper, we propose a method for generating shadow data from buildings and satellites using Unity3D. In the virtual Unity space, 3D objects existing in the real world were placed, and shadows were generated using lights effects to shoot. Through this, it is possible to get all three types of images (shadow-free, shadow image, shadow mask) necessary for shadow detection and removal when training deep learning networks. The method proposed in this paper contributes to helping the progress of the research by providing big data in the field of building or satellite shadow detection and removal research, which is difficult for learning deep learning networks due to the absence of data. And this can be a suboptimal method. We believe that we have contributed in that we can apply virtual data to test deep learning networks before applying real data.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.192-201
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• 2020

This study commercialization of 5G will enable transformation of university education. This study identifies five attributes (device type, learning place, learning content, learning field and expense payment) and corresponding levels to study the impact of 5G in the future of university education. The attributes and the levels are then combined into few 5G education service alternatives for respondents to rank. 102 students ranked the alternatives based on their preferences and intent to use. Results indicate that the intent to use 5G-based education service was high with 86% and the most important factor was expense payment (37%), followed by learning field (26%), learning content (24%), device type (8%) and learning place (5%). Specifically, students preferred smart device, practical and experiential content, ubiquitous (no limitation of space and time) learning, practical education and free rate when adopting 5G-based education service. These will provide implications to accelerate adoption of and exploitation of 5G for innovating university education.