• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1201-1208
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• 2019

The government has operated financing fund under the National Finance Act for the smooth conduct of national policy. But, It is exposed to problems such as the possibility of abuse of fund and the lack of after-loan management. In this paper, It uses fintech such as the blockchain and machine learning to solve these problems. The fund operation procedure is designed as a consortium blockchain, and it suggests the application of PBFT negotiation algorithm and the smart contract. In case of the fund management, it suggests utilizing multilayer artificial neural network model of machine learning and a module of result interpretation. The introduction of this research approach will improve the transparency and efficiency of the financing fund, ensure the credibility and also contribute to the improvement of the fund management and the establishment of the fund policy.

• The Journal of Korean Institute for Practical Engineering Education
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• v.2 no.2
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• pp.16-22
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• 2010

Accounting is a practical study as a fundamental of a corporate management. In recent years, the necessity of 'Engineering Accounting' and 'Accounting and Society' for non management and accounting major is on the rise. To accelerate the learning effect of accounting, the individual learning and learning by repetition is required. And ERP practice is on the rise as a new trend. The e-learning will be helpful to the individual learning and learning by repetition. The learner study repeatedly and individually by e-learning, and then practice the accounting process by ERP practice. It's also adoptable for Engineering Accounting to use repeated and individual study using e-learning and ERP practice. This paper is focused on the case study of Engineering Accounting using e-learning and ERP practice in Korea University of Technology and Education. To review this case, the development direction of the accounting education and engineering accounting education will be introduced.

• Korean Journal of Construction Engineering and Management
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• v.20 no.5
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• pp.31-41
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• 2019

The purpose of this study is to investigate the participation of vocational training program of construction engineers and the impact of workplace learning (formal learning and informal learning) on human resource performances of construction engineers. The data of 306 construction engineers were extracted from 10,069 workers in various industries those were collected by 6th human resource company panel survey done by Korea Research Institute of Vocational Education & Training. This study found that, compared with workers in other industries, participation rate of construction engineers in workplace learning (formal learning, informal learning) was relatively low, and especially the participation rate of informal learning was significantly low. Regression analysis showed that participation in formal learning did not affect positive job performance and job satisfaction. On the other hand, informal learning has a positive effect on job capability, job satisfaction, and organizational commitment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.312-320
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• 2023

Recently, there has been an increasing attempt to replace defect detection inspections in the manufacturing industry using deep learning techniques. However, obtaining substantial high-quality labeled data to enhance the performance of deep learning models entails economic and temporal constraints. As a solution for this problem, semi-supervised learning, using a limited amount of labeled data, has been gaining traction. This study assesses the effectiveness of semi-supervised learning in the defect detection process of manufacturing using the MixMatch algorithm. The MixMatch algorithm incorporates three dominant paradigms in the semi-supervised field: Consistency regularization, Entropy minimization, and Generic regularization. The performance of semi-supervised learning based on the MixMatch algorithm was compared with that of supervised learning using defect image data from the metal casting process. For the experiments, the ratio of labeled data was adjusted to 5%, 10%, 25%, and 50% of the total data. At a labeled data ratio of 5%, semi-supervised learning achieved a classification accuracy of 90.19%, outperforming supervised learning by approximately 22%p. At a 10% ratio, it surpassed supervised learning by around 8%p, achieving a 92.89% accuracy. These results demonstrate that semi-supervised learning can achieve significant outcomes even with a very limited amount of labeled data, suggesting its invaluable application in real-world research and industrial settings where labeled data is limited.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.115-136
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• 2014

The game industry has grown steadily and the online game has become one of the most attractive game segments for its remarkable growth. Customer management in the online game industry, however, has received little attention from the academic field. The purpose of this study is to analyze the drivers of customer defection in the online game setting and suggest not only theoretical but also managerial insights into increasing customer retention rates. Prior to empirical analysis, the authors hypothesized that 3 variables of interests (Learning, Playing, Achievement) would explain the customer defection according to preceeding researches. To demonstrate these hypotheses, the authors obtained data from one of the biggest game publishers in Korea, and the empirical analysis model was developed considering context of research settings. The results of analyses provide the following insights. First, the key behavioral variables of Learning, Playing, and Achievement play substantial roles in explaining the customer defection. Next, the effects of these variables vary between customer types: novice and experienced customers. The defection decisions by novice customers are predicted by all key behavioral variables and Playing serves as the most influential indicator of the defection decisions. However, experienced customers are influenced by Playing and Achievement, while Learning has no impact on the defection decisions. Finally, the authors investigated hypothetical customer retention strategies, using the empirical results. The market outcomes indicate that the customer retention strategies work well with novice customers and it is hard-to-impossible to prevent experienced customers from defection using their behavioral data. These findings together deliver several meaningful insights to management as follow. First, the management should support customers to get involved in Learning activities at the very first stage. Second, customer's Achievement and appropriate compensation for it would work as defection barriers. Last, to optimize the outcomes of firm's marketing investments, it is better to focus on retention of novice users not experienced ones.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.196-202
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• 2020

This study explores conceptions of learning management and examines the effects on study approach and critical thinking. Undergraduates majoring or minoring in management were gathered through convenient sampling and participated in the online survey. A total of 88 responses were analyzed. The results reveal that there is no significant gender or grade difference on conceptions of learning management. Conceptions of "test" and "seeing in a new way" are associated with deep study approach, whereas conceptions of 'test' is associated with surface study approach. Conceptions of "seeing in a new way" has a significant effect on critical thinking. The findings may inform redesign of instruction or curriculum especially focusing on improving high-quality thinking skills as the learning outcomes.

• Journal of Digital Convergence
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• v.18 no.7
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• pp.289-297
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• 2020

The purpose of this study is to examine the college life adaptation that is affected by major satisfaction, learning commitment, and time management behavior of the college of Health students. The data were collected from 482 College of Health students at U city and analyzed using descriptive statistics, t-test, ANOVA, Scheffe test, Pearson's correlation analysis, and multiple regression. As a result of this study, major satisfaction, learning commitment, and time management behavior of College of Health students showed a positive relation with college life adaptation, and those factors affected college life adaptation. Therefore, it is necessary to make and apply the programs to enhance the college life adaptation for the college of Health students, and the supports such as environmental improvement and teaching methods are required to increase the learning commitment.

• Journal of Korean Society of Water and Wastewater
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• v.34 no.1
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• pp.35-43
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• 2020

Turbidity has various effects on the water quality and ecosystem of a river. High turbidity during floods increases the operation cost of a drinking water supply system. Thus, the management of turbidity is essential for providing safe water to the public. There have been various efforts to estimate turbidity in river systems for proper management and early warning of high turbidity in the water supply process. Advanced data analysis technology using machine learning has been increasingly used in water quality management processes. Artificial neural networks(ANNs) is one of the first algorithms applied, where the overfitting of a model to observed data and vanishing gradient in the backpropagation process limit the wide application of ANNs in practice. In recent years, deep learning, which overcomes the limitations of ANNs, has been applied in water quality management. LSTM(Long-Short Term Memory) is one of novel deep learning algorithms that is widely used in the analysis of time series data. In this study, LSTM is used for the prediction of high turbidity(>30 NTU) in a river from the relationship of turbidity to discharge, which enables early warning of high turbidity in a drinking water supply system. The model showed 0.98, 0.99, 0.98 and 0.99 for precision, recall, F1-score and accuracy respectively, for the prediction of high turbidity in a river with 2 hour frequency data. The sensitivity of the model to the observation intervals of data is also compared with time periods of 2 hour, 8 hour, 1 day and 2 days. The model shows higher precision with shorter observation intervals, which underscores the importance of collecting high frequency data for better management of water resources in the future.

• Journal of Digital Convergence
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• v.6 no.2
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• pp.135-143
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• 2008

The remarkable growth of the Internet since mid-l990s has expanded the e-learning market and brought the transformation of educational environments and methodology. It can be said that the e-learning has changed the educational paradigm. Korean government is firmly determined to support the diffusion of e-learning because of the benefits of e-learning. People seem to accept the e-learning when its contents have high quality. A lot of research have been conducted on e-learning, however, it was mostly about user's usage intention, satisfaction and educational effect. It can't seem that sufficient research efforts have been put into figuring out the role of e-learning contents quality in the expansion of e-learning. In this paper, we present the empirical study on the influence of e-learning contents quality on user's satisfaction and educational effect. We conducted an questionnaire survey on college students to collect data and found that the quality of e-learning contents has significant influence on the users' satisfaction.

• The Journal of the Korea Contents Association
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• v.16 no.7
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• pp.640-650
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• 2016

This study was conducted in order to develop an education program based on simulation with problem-based learning, to apply it to nursing students, and to examine its effects on the students' learning motivation, learning strategy, and academic achievement. The subjects of this study were 69 seniors majoring in nursing. Education based on simulation with problem-based learning was applied to the students from September to October in 2015, and then a questionnaire survey was conducted on their learning motivation, learning strategy, and academic achievement. According to the results of this study, the education based on simulation with problem-based learning reduced the nursing students' other-directed motivation (external motivation), increased their self-regulation motivation (identified motivation, intrinsic motivation), and improved their use of resource management strategies. In addition, academic achievement (academic performance, and educational satisfaction) was in a positive correlation with identified motivation and learning strategies (cognitive strategy, meta cognitive strategy, and resource management strategy). In conclusion, education based on simulation with problem-based learning was found to be an effective education strategy for enhancing nursing students' autonomous motivation and improving their use of resource management strategies. Thus, it is necessary to promote the application of simulation with problem-based learning in various care situations and to study factors and parameters influencing learning related variables.