• Proceedings of the Zoological Society Korea Conference
• /
• 2001.10a
• /
• pp.123.2-123
• /
• 2001

No Abstract, See Full Text

• Proceedings of the Zoological Society Korea Conference
• /
• 1994.10a
• /
• pp.185.1-185
• /
• 1994

No Abstract, See Full Text

• Proceedings of the Korean Society for Cognitive Science Conference
• /
• 2000.01a
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• pp.63-67
• /
• 2000

• Proceedings of the Optical Society of Korea Conference
• /
• 2001.08a
• /
• pp.164-165
• /
• 2001

No Abstract.See Full-text

• Proceedings of the Zoological Society Korea Conference
• /
• 1996.10a
• /
• pp.108.1-108
• /
• 1996

No Abstract, See Full Text

• The International Journal of Costume Culture
• /
• v.4 no.1
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• pp.1-8
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• 2001

No Abstract.See Full-text

• Proceedings of the Zoological Society Korea Conference
• /
• 2003.08a
• /
• pp.22-22
• /
• 2003

No Abstract, See Full Text

• Proceedings of the Zoological Society Korea Conference
• /
• 1999.10b
• /
• pp.72.2-72
• /
• 1999

No Abstract, See Full Text

• Journal of Broadcast Engineering
• /
• v.25 no.2
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• pp.275-278
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• 2020

In this paper, we proposed a deep learning based character recognition system through virtual data generation. In order to secure the learning data that takes the largest weight in supervised learning, virtual data was created. Also, after creating virtual data, data generalization was performed to cope with various data by using augmentation parameter. Finally, the learning data composition generated data by assigning various values to augmentation parameter and font parameter. Test data for measuring the character recognition performance was constructed by cropping the text area from the actual image data. The test data was augmented considering the image distortion that may occur in real environment. Deep learning algorithm uses YOLO v3 which performs detection in real time. Inference result outputs the final detection result through post-processing.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.133-145
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• 2022

As a result of the study, the poorest keywords were information, big data, management, policy, government, law, and smart. In addition, as a result of network analysis, related research was being conducted on topics such as data industry policy, data governance performance, defense, governance, and data public. The four topics derived through topic modeling were "DG policy," "DG platform," "DG in laws," and "DG implementation," of which research related to "DG platform" showed an increasing trend, and "DG implementation" tended to shrink. This study comprehensively summarized data governance-related studies. Data governance needs to expand research areas from various perspectives and related fields such as data management and data integration policies at the organizational level, and related technologies. In the future, we can expand the analysis targets for overseas data governance and expect follow-up studies on research directions and policy directions in industries that require data-based future industries such as Industry 4.0, artificial intelligence, and Metaverse.