지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.2
- 수록면
- 5 - 12 (8page)
- DOI
- 10.17289/jkscs.36.1.202202.5
이용수
초록· 키워드
오류제보하기
Demands on the spot color printing, which uses not only CMYK (Cyan, Magenta, Yellow, and blacK) but also several special inks, increase rapidly in the printing industries. Therefore, the quality of printed output and the efficient usage of various ink colors have to be improved in the competitive printing market. However, the spot color printing system usually depends on the experiences of the skilled workers for the elaborate output, and it would cause lots of manufacturing time, cost, and/or quality gap of prints. Therefore, we suggest a deep neural network model based on the datasets from the real-world printing system. The input data set consists of various characteristics of primary color inks and papers used to print, and the output data set is reflexibility of the products. Total 31 ranges of visible light are used to measure the reflexibility, and those values are converted into the standard color coordinate, CIELAB. The neural network model is tested on all the primary color space, and the average difference between the real CIELAB value and the predicted value is less than the 8.13, which means that the difference cannot be identified by untrained people. Further researches based on the controlled date aggregation using better spectroscopes would be essential to apply the suggested model to the real-world printing systems.
목차
Abstract
1. Introduction
2. Methods
3. Results
4. Conclusion
References
참고문헌 (10)
참고문헌 신청
Andresen, S. L. (2002). JohnMcCarthy: 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 SIG KDD International Conference on Knowledge Discovery and Data Mining, 785-894.
Cho, G. J. (2020). 65% growthin digital print over the n ext 10 years. Printing Korea, http://www.printingkorea.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 SIG KDD International Conference on Knowledge Discovery and Data Mining, 785-894.
Cho, G. J. (2020). 65% growthin digital print over the n ext 10 years. Printing Korea, http://www.printingkorea.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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