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논문 기본 정보

자료유형
학술저널
저자정보
Chigozie Uzochukwu Udeogu (Kumoh National Institute of Technology) Angela C. Caliwag (Kumoh National Institute of Technology) Wansu Lim (Kumoh National Institute of Technology)
저널정보
한국통신학회 한국통신학회논문지 한국통신학회논문지 제47권 제3호
발행연도
2022.3
수록면
482 - 491 (10page)
DOI
10.7840/kics.2022.47.3.482

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초록· 키워드

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Remaining useful life (RUL) prediction for supercapacitors is particularly important to ensure the safety of the applied system and reduce the cost of operation. The existing RUL prediction method utilized health indicators (HIs) that are extracted by a conventional method. This method has the risk of dropping useful information in the supercapacitor data which leads to low accuracy because of poor quality features. To resolve this issue, this paper proposes an optimized end-to-end deep learning model for RUL prediction. Specifically, a genetic algorithm (GA) for automatic feature selection and long short-term memory (LSTM) network (GA-LSTM) for RUL prediction. GA is utilized for automatic feature extraction which ensures all important information in the supercapacitor data is considered during HI extraction. The combination of the best-selected features is used as the input to the LSTM model for final RUL prediction. Our proposed model achieved a root mean square error (RMSE) of 0.03 unlike the recurrent neural network, LSTM, and deep convolutional neural network with RMSE of 23.87, 0.51, and 0.38, respectively. When compared with other models, the overall results show that our model exhibits excellent performance for the RUL prediction of supercapacitors.

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ABSTRACT
Ⅰ. Introduction
Ⅱ. Related Works
Ⅲ. Methodology
Ⅳ. Results and Discussion
Ⅴ. Conclusion
References

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