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커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.11
- 수록면
- 47 - 55 (9page)
- DOI
- 10.17289/jkscs.34.4.202011.47
이용수
이 논문의 연구 히스토리 (3)
2020
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- 연관 학술논문 또는 학술발표를 통해 보다 발전된 연구결과를 확인하실 수 있습니다.
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초록· 키워드
오류제보하기
The most important step in packaging printing is determining the spot color blending rate. Currently, in most processes, spot colors are produced based on the operator"s experience. Accordingly, the process is managed inefficiently and unnecessary resources are consumed. To solve this problem, we intend to establish an artificial intelligence model that can predict the color coordinates of spot colors. An optimal algorithm is constructed to predict the color coordinates of the spot color. Based on the stacking ensemble, four models are firstly generated, and secondly a single model that receives outputs of the generated four models is generated to develop an algorithm. As a result of learning the developed model by gradually increasing the number of data to check the performance of the model according to the number of data, it can be seen that the performance of the model increases as the number of data increases. In addition, as a result of training the model using all the data, it can be seen that it predicts the CIE L*, a*, and b* of the output well. In conclusion, beyond the mathematical model that can predict color coordinates well only in a specific structure, the artificial intelligence model implemented through data learning predicts the color coordinates of spot colors considerably. This suggests that artificial intelligence can also be used in color-related fields. In this study, effective process management can be carried out by reducing time and minimizing resources by applying the learned artificial intelligence model to the process. In addition, it is expected that artificial intelligence that can accurately predict colors by acquiring a lot of data in the future will be developed.
목차
Abstract
1. Introduction
2. Related Theories
3. Result
4. Conclusion and Future Research
References
참고문헌 (10)
참고문헌 신청
Andresen, S. L. (2002). John McCarthy: father of AI. IEEE Intelligent Systems, 17(5), 84-85.
Cao, M. T., Hoang, N. D., Nhu, V. H., & Bui, D. T. (2020). An advanced meta-learner based on artificial electric field algorithm optimized stacking ensemble techniques for enhancing prediction accuracy of soil shear strength. Engineering with Computers, 1-23.
Chen, T. & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785-894.
Cho, G. J. (2020). 65% growth in digital print over the next 10 years. PRINTING KOREA, http://www.printing korea.or.kr. (2020-01).
Fonseca, R., Leeuwenburgh, O., Van den Hof, P., & Jansen, J. D. (2014). Improving the ensembleoptimization method through covariance-matrix adaptation. SPE Journal, 20(01), 155-168.
Cao, M. T., Hoang, N. D., Nhu, V. H., & Bui, D. T. (2020). An advanced meta-learner based on artificial electric field algorithm optimized stacking ensemble techniques for enhancing prediction accuracy of soil shear strength. Engineering with Computers, 1-23.
Chen, T. & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785-894.
Cho, G. J. (2020). 65% growth in digital print over the next 10 years. PRINTING KOREA, http://www.printing korea.or.kr. (2020-01).
Fonseca, R., Leeuwenburgh, O., Van den Hof, P., & Jansen, J. D. (2014). Improving the ensembleoptimization method through covariance-matrix adaptation. SPE Journal, 20(01), 155-168.
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