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What is LSTM-CRF Framework for Battery Capacity Prediction about?

This paper presents a novel framework for predicting lithium-ion battery capacity using a combination of long short-term memory (LSTM) and conditional random field (CRF) models. The proposed model enhances prediction accuracy by effectively extracting temporal features and utilizing a transfer matrix for long serialization prediction. Experimental results on the NASA PCOE lithium battery dataset demonstrate improved performance compared to existing methods, as measured by root mean square error (RMSE) and m

Author

wdw139130

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EN