• Korean Journal of Comparative Education
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• v.27 no.1
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• pp.247-284
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• 2017

The purpose of this study is to analyze firm's motivation of participating and investing in apprenticeship in Germany and Korea, and to investigate institutional factors influencing firm's motivation. By comparing institutional factors of the two countries, it aims to drawing out policy implications for improving Korean apprenticeship. The main method for data collection was comprehensive literature review on international organizations, each countries' government and research institutes' policy materials, statistical data, research outputs and media resources related to each countries' apprenticeship. Considering whether firm's motivation for participating and investing in apprenticeship is production-oriented or investment-oriented, Germany is more inclined to investment motivation with firm's covering net cost during apprenticeship period. On the other hand, Korea is more inclined toward production orientation with firm's expectation of gaining net profit during the training period. Why is firm's training motivation different in these two countries? The author tried to find the reason from the difference of institutional factors of the countries by dividing institutional factors into 4 categories: context(tripartite relations, legal framework), input (flexibility of the system, government incentive), process(training contents, training duration, quality assurance), and output(completion/retention rate, apprentice's productivity). The key implication from the comparative analysis of institutional factors is that it is necessary to enforce companies to have "accountability" on the minimum critical elements, but also to ensure them to have "autonomy" on the rest of the elements.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.199-205
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• 2024

Purpose: This study aims to improve the recognition rate of Auto People Counting (APC) in accurately identifying and providing information on remaining evacuees in disaster-vulnerable facilities such as nursing homes to firefighting and other response agencies in the event of a disaster. Methods: In this study, a baseline model was established using CNN (Convolutional Neural Network) models to improve the algorithm for recognizing images of incoming and outgoing individuals through cameras installed in actual disaster-vulnerable facilities operating APC systems. Various algorithms were analyzed, and the top seven candidates were selected. The research was conducted by utilizing transfer learning models to select the optimal algorithm with the best performance. Results: Experiment results confirmed the precision and recall of Densenet201 and Resnet152v2 models, which exhibited the best performance in terms of time and accuracy. It was observed that both models demonstrated 100% accuracy for all labels, with Densenet201 model showing superior performance. Conclusion: The optimal algorithm applicable to APC among various artificial intelligence algorithms was selected. Further research on algorithm analysis and learning is required to accurately identify the incoming and outgoing individuals in disaster-vulnerable facilities in various disaster situations such as emergencies in the future.

• Journal of the Korean Institute of Gas
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• v.16 no.6
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• pp.55-66
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• 2012

The practical way to minimize the greenhouse gas is to reduce the emission of carbon dioxide. For this reason, CCS(Carbon Capture and Storage) technology, which could reduce carbon dioxide emission, has risen as a realistic alternative in recent years. In addition, the researcher is recently working into ways of applying CCS technologies with deep saline aquifer. In this study, the evaluation model on the feasibility of $CO_2$ sequestration in the deep saline aquifer using ANN(Artificial Neural Network) was developed. In order to develop the efficiency-evaluation model, basic model was created in the deep saline aquifer and sensitivity analysis was performed for the aquifer characteristics by utilizing the commercial simulator of GEM. Based on the sensitivity analysis, the factors and ranges affecting $CO_2$ sequestration in the deep saline aquifer were chosen. The result from ANN training scenario were confirmed $CO_2$ sequestration by solubility trapping and residual trapping mechanism. The result from ANN model evaluation indicated there is the increase of correlation coefficient up to 0.99. It has been confirmed that the developed model can be utilized in feasibility of $CO_2$ sequestration at deep saline aquifer.