인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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.
피인용 0
초록·키워드
Real-time state-of-health (SOH) estimation requires reliable performance with low computational burden. Bayesian model averaging (BMA) provides point estimates and predictive uncertainty, but multiple health indicators and candidate models increase computational cost in resource-constrained embedded systems. In this study, an existing BMA-based SOH estimation method was implemented on a Nucleo-H755ZI-Q board, and the effects of input dimensionality reduction on SOH estimation error, predictive uncertainty, and embedded execution time were analyzed. Nine input-dimensionality conditions were constructed by sequentially excluding health indicators with lower absolute Pearson correlation coefficients. When six input features were used, the mean execution time was 7.266 ms, approximately 19.9 % lower than the 9.070 ms obtained with nine input features, while the RMSE remained comparable to that of the seven-input condition at 0.030. A further reduction from six to five input features shortened the mean execution time by only approximately 1.5 %, whereas the RMSE increased by 46.7 % to 0.044. When four or fewer input features were used, the execution time remained near 6.6 ms, and no substantial additional reduction was observed. These results indicate that six inputs provide a balanced trade-off between SOH estimation performance and execution time under the considered dataset, feature-removal order, and embedded hardware conditions, rather than a generally optimal input dimension.
본문·목차
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