• Journal of Digital Convergence
• /
• v.17 no.9
• /
• pp.259-269
• /
• 2019

This study analyzed convergent factors related to self-leadership of female freshmen in health majors studying TOEIC. The survey was conducted from April 29, 2019 to May 10, 2019 using unregistered self-administered questionnaire for 201 female freshmen in health majors and they were randomly selected from TOEIC class in college located in J city. The results of hierarchical multiple regression analysis show the following. The self-leadership of respondents turned out to be significantly higher in following groups: a group in which self-competence is higher, a group in which subdivision task self-efficacy and coping self-efficacy is higher, and a group in which subdivision chance of locus control from locus of control is lower. Their explanatory power was 49.7%. The results of the study indicate that the efforts to manage self-competence, self-efficacy, and locus of control are required to improve the self-leadership of female freshmen in health majors studying TOEIC. These results can be used for academic counseling guidance to enhance self-leadership of female freshmen in health majors studying TOEIC. In the future research, it is necessary to establish and analyze a structural equation model that affects self-leadership of male and female college students in health majors studying TOEIC.

• Communications of Mathematical Education
• /
• v.33 no.3
• /
• pp.163-180
• /
• 2019

This study introduces a development of calculus contents which makes to understand the main concepts of calculus in a short period of time and to enhance problem solving and computational thinking for complex problems encountered in the real world for college freshmen with diverse backgrounds. As a concrete measure, we developed 'Teaching and Learning' contents and Python-based code for Calculus I and II which was used in actual classroom. In other words, the entire process of teaching and learning, action plan, and evaluation method for calculus class with Python based coding are reported and shared. In anytime and anywhere, our students were able to freely practice and effectively exercise calculus problems. By using the given code, students could gain meaningful understanding of calculus contents and were able to expand their computational thinking skills. In addition, we share a way that it motivated student activities, and evaluated students fairly based on data which they generated, but still instructor's work load is less than before. Therefore, it can be a teaching and learning model for college mathematics which shows a possibility to cover calculus concepts and computational thinking at once in a innovative way for the 21st century.

• Journal of IKEEE
• /
• v.23 no.1
• /
• pp.112-119
• /
• 2019

In this paper, we propose a deep learning structure suitable for embedded system. The flame detection process of the proposed deep learning structure consists of four steps : flame area detection using flame color model, flame image classification using deep learning structure for flame color specialization, $N{\times}N$ cell separation in detected flame area, flame image classification using deep learning structure for flame shape specialization. First, only the color of the flame is extracted from the input image and then labeled to detect the flame area. Second, area of flame detected is the input of a deep learning structure specialized in flame color and is classified as flame image only if the probability of flame class at the output is greater than 75%. Third, divide the detected flame region of the images classified as flame images less than 75% in the preceding section into $N{\times}N$ units. Fourthly, small cells divided into $N{\times}N$ units are inserted into the input of a deep learning structure specialized to the shape of the flame and each cell is judged to be flame proof and classified as flame images if more than 50% of cells are classified as flame images. To verify the effectiveness of the proposed deep learning structure, we experimented with a flame database of ImageNet. Experimental results show that the proposed deep learning structure has an average resource occupancy rate of 29.86% and an 8 second fast flame detection time. The flame detection rate averaged 0.95% lower compared to the existing deep learning structure, but this was the result of light construction of the deep learning structure for application to embedded systems. Therefore, the deep learning structure for flame detection proposed in this paper has been proved suitable for the application of embedded system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.14 no.2
• /
• pp.95-104
• /
• 2019

Fear to start-up failures has been known to have a negative impact on entrepreneurial intention. This is one of the reasons why the government adopts a policy to help university students overcome their fear of start-ups. Setting educational goals to foster innovative and progressive entrepreneurs, universities have been conducting entrepreneurship education, but it is hard to say that constructive results have been achieved so far. Rather than adopting the practice of optional entrepreneurship education, there is a need to have all university freshmen mandatorily take the course of entrepreneurship education. This study aims to uncover the impact of more aggressive entrepreneurship education position in the university by analyzing empirical data. The relationship between an entrepreneurship level and entrepreneurial intention was tested, and start-up fear was also considered. In the research model, self-leadership and self-efficacy were included as regressors to entrepreneurship levels. Especially, this study tested moderate effects of start-up community during the course. The results from the sample of 2,500 freshmen indicate that entrepreneurship level is significantly improved by taking the course; however, fear to start-up failures remains still. In addition, empirical findings show that putting start-up communities in the entrepreneurship education helps students by moderating self-leadership and self-efficacy. This study extends our knowledge of entrepreneurship education in university by analyzing university freshmen data empirically.

• Journal of Korean Home Economics Education Association
• /
• v.33 no.3
• /
• pp.173-187
• /
• 2021

The purpose of this research was to develop instructions for making upcycled clothing accessories related to the 'clothing management and recycling' unit of middle school home economics applying the design thinking technique. Teaching and learning process plans were developed according to the ADDIE model which includes the following process: analysis, design, development, implementation, and evaluation. The design thinking process includes understanding the related knowledge, sympathizing, problem identification(sharing perspectives) and idea development, making prototypes, testing, and making the actual product. Thirteen home economics teachers served as critics. Student feedbacks were collected to evaluate whether the course objectives were attained after the implementation. As a result, teaching and learning process plans, course materials, and evaluation rubrics for ten class sessions were developed. Student feedbacks confirmed the attainment of following five course objectives: improvement of ethical responsibilities through the exploration of various clothing recycling techniques, practice of creative and eco-friendly clothing culture, acquisition of the skills to use sewing tools safely, improvement of abilities to think, sympathize, and communicate, and exploration of aesthetic activities and fashion careers.

• Journal of Digital Convergence
• /
• v.17 no.11
• /
• pp.269-278
• /
• 2019

This study ascertained convergent influence on self-leadership and its association with self-competence, self-efficacy and locus of control among female freshmen in health majors studying TOEIC. Data collection was carried out using a self-administered questionnaire from April 29, 2019 to May 10, 2019 and the target was randomly selected 201 female freshmen in health majors in TOEIC class from college located in J city. Self-leadership was positively correlated with self-competence, self-efficacy and locus of control. The covariance structure analysis showed that the higher self-competence, the higher self-efficacy and the lower locus of control tend to increase self-leadership. The results of the study indicate that the efforts, to increase self-competence and self-efficacy, to decrease locus of control, are required to improve self-leadership of female freshmen in health majors studying TOEIC. These results are expected to be used for educational counseling and intervention efforts to enhance self-leadership among female freshmen in health majors studying TOEIC. In future studies, further research on additional factors affecting self-leadership is needed.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.22 no.3
• /
• pp.362-373
• /
• 2021

This is an action research study of mixed methodology design to confirm the implementation process and effects of applying visual thinking and window paning on improving nursing ethics and professional courses. Based on the conceptual model for action research, a quantitative and qualitative approach was taken. The data was collected and analyzed in an integrated manner. The survey analysis was done using the SPSS WIN 23.0 program. The participants were interviewed after experiencing the techniques in class and content analysis was used on the answers. As a result of applying visual thinking and window paning, ethical decision-making confidence (t=6.748, p<.001) and nursing professional intuition (t=-3.52, p<.001) showed statistically significant changes. There was, however, no significant change in biomedical ethics consciousness (t=1.291, p=.199). Qualitative analysis found that they had fresh experience, an unfamiliar but comfortable feeling, feeling of being mine, insufficient time, systematic case study approach based on theory, were able to cultivate cooperation and coordination ability through discussion and experience in various professional fields, pride, ethical responsibility consciousness and were able to apply learning content in the field. Visual thinking and window paning foster diverse competencies in nursing education and help integrative learning. Therefore, based on the results it is proposed that visual thinking and window paning are applied to the improvement of instruction in other courses to develop core nursing competency.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.38 no.6
• /
• pp.623-633
• /
• 2020

As the time spent in indoor space has increased, the demand for services targeting indoor spaces also continues to increase. To provide indoor spatial information services, the construction of indoor spatial information should be done first. In the study, a method of generation IndoorGML, which is the international standard data format for Indoor space, from existing BIM data. The characteristics of IFC objects were investigated, and objects that need to be converted to IndoorGML were selected and classified into objects that restrict the expression of Indoor space and internal passages. Using the proposed method, a part of data set provided by the BIMserver github and the IFC model of the 21st Century Building in University of Seoul were used to perform experiments to generate PrimalSpaceFeatures of IndoorGML. As a result of the experiments, the geometric information of IFC objects was represented completely as IndoorGML, and it was shown that NavigableBoundary, one of major features of PrimalSpaceFeatures in IndoorGML, was accurately generated. In the future, the proposed method will improve to generate various types of objects such as IfcStair, and additional method for automatically generating MultiLayeredGraph of IndoorGML using PrimalSpaceFeatures should be developed to be sure of completeness of IndoorGML.

• Clean Technology
• /
• v.27 no.1
• /
• pp.61-68
• /
• 2021

Selective catalytic reduction (SCR) is widely used as a method of removing nitrogen oxide in large-capacity thermal power generation systems. Uniform mixing of the injected ammonia and the inlet flue gas is very important to the performance of the denitrification reduction process in the catalyst bed. In the present study, a computational analysis technique was applied to the ammonia injection system design process of a denitrification facility. The applied model is the denitrification facility of an 800 MW class coal-fired power plant currently in operation. The flow field to be solved ranges from the inlet of the ammonia injection system to the end of the catalyst bed. The flow was analyzed in the two-dimensional domain assuming incompressible. The steady-state turbulent flow was solved with the commercial software named ANSYS-Fluent. The nozzle arrangement gap and injection flow rate in the ammonia injection system were chosen as the design parameters. A total of four (4) cases were simulated and compared. The root mean square of the NH3/NO molar ratio at the inlet of the catalyst layer was chosen as the optimization parameter and the design of the experiment was used as the base of the optimization algorithm. The case where the nozzle pitch and flow rate were adjusted at the same time was the best in terms of flow uniformity.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.3
• /
• pp.133-140
• /
• 2022

We investigate the performance of deep learning-based Korean language models on a task of predicting the score range of Korean essays written by foreign students. We construct a data set containing a total of 304 essays, which include essays discussing the criteria for choosing a job ('job'), conditions of a happy life ('happ'), relationship between money and happiness ('econ'), and definition of success ('succ'). These essays were labeled according to four letter grades (A, B, C, and D), and a total of eleven essay score range prediction experiments were conducted (i.e., five for predicting the score range of 'job' essays, five for predicting the score range of 'happiness' essays, and one for predicting the score range of mixed topic essays). Three deep learning-based Korean language models, KoBERT, KcBERT, and KR-BERT, were fine-tuned using various training data. Moreover, two traditional probabilistic machine learning classifiers, naive Bayes and logistic regression, were also evaluated. Experiment results show that deep learning-based Korean language models performed better than the two traditional classifiers, with KR-BERT performing the best with 55.83% overall average prediction accuracy. A close second was KcBERT (55.77%) followed by KoBERT (54.91%). The performances of naive Bayes and logistic regression classifiers were 52.52% and 50.28% respectively. Due to the scarcity of training data and the imbalance in class distribution, the overall prediction performance was not high for all classifiers. Moreover, the classifiers' vocabulary did not explicitly capture the error features that were helpful in correctly grading the Korean essay. By overcoming these two limitations, we expect the score range prediction performance to improve.