메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색
질문

논문 기본 정보

자료유형
학술저널
저자정보
Miracle Udurume (Kumoh National Institute of Technology) 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
492 - 500 (9page)
DOI
10.7840/kics.2022.47.3.492

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색
질문

초록· 키워드

오류제보하기
The remaining useful life (RUL) prediction of supercapacitors is an important part of supercapacitors management system. To accurately predict the RUL of supercapacitor, a large amount of capacity data is required which can be difficult to acquire due to privacy restrictions and limited access. Previous works have employed the use of deep learning models to synthetically generate data. However, a prerequisite ensuring the success of these models depends on their ability to preserve the temporal dynamics of the data. This paper presents a generative adversarial network (GAN) for synthetic data generation and a long short-term memory (LSTM) network for accurate RUL prediction. Firstly, the GAN model is employed for synthetic data generation and LSTM for RUL prediction. We show that the GAN model is capable of preserving the temporal dynamics of the original data and also prove that the generated data can be used to accurately carry out RUL prediction. Our proposed GAN model was able to achieve an accuracy of 85% after 500 epochs. The performance of the generated data set with the LSTM model achieved an RMSE of 0.29. The overall results show that synthetic data can be used to achieve excellent performance for RUL prediction.

목차

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

참고문헌 (20)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

이 논문과 함께 이용한 논문

최근 본 자료

전체보기

댓글(0)

0

UCI(KEPA) : I410-ECN-0101-2022-567-001069916