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http://dx.doi.org/10.24225/kjai.2022.10.2.7

A Study on the Generation of Datasets for Applied AI to OLED Life Prediction  

CHUNG, Myung-Ae (Dept. of Medical Bigdata, Eulji University)
HAN, Dong Hun (Dept. of Medical IT & Marketing, Eulji University)
AHN, Seongdeok (Reality Device Research Division, ETRI)
KANG, Min Soo (Dept. of Medical IT, Eulji University)
Publication Information
Korean Journal of Artificial Intelligence / v.10, no.2, 2022 , pp. 7-11 More about this Journal
Abstract
OLED displays cannot be used permanently due to burn-in or generation of dark spots due to degradation. Therefore, the time when the display can operate normally is very important. It is close to impossible to physically measure the time when the display operates normally. Therefore, the time that works normally should be predicted in a way other than a physical way. Therefore, if you do computer simulations based on artificial intelligence, you can increase the accuracy of prediction by saving time and continuous learning. Therefore, if we do computer simulations based on artificial intelligence, we can increase the accuracy of prediction by saving time and continuous learning. In this paper, a dataset in the form of development from generation to diffusion of dark spots, which is one of the causes related to the life of OLED, was generated by applying the finite element method. The dark spots were generated in nine conditions, such as 0.1 to 2.0 ㎛ with the size of pinholes, the number was 10 to 100, and 50% with water content. The learning data created in this way may be a criterion for generating an artificial intelligence-based dataset.
Keywords
OLED; Dataset; Bigdata; AI; Machine learning;
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Times Cited By KSCI : 2  (Citation Analysis)
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