人工智能在锂离子电池研发中的应用
朱振威1, 邱景义1, 王莉2, 曹高萍1, 何向明2, 王京3, 张浩1,*(
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Application of Artificial Intelligence to Lithium-Ion Battery Research and Development
Zhen-Wei Zhu1, Jing-Yi Qiu1, Li Wang2, Gao-Ping Cao1, Xiang-Ming He2, Jing Wang3, Hao Zhang1,*()
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Figure 8. Over 650 unique particles of different sizes, shapes, positions, and degrees of cracking were successfully identified and automatically isolated from the imaging data in an automatic manner. (A) Workflow of the ML-based segmentation. (B) Comparison of conventional segmentation results and the machine- learning-assisted segmentation results for a few representative particles. Different colors denote different particle labels. (C) Schematic illustration of the herein developed ML model based on the Mask R-CNN for particle identification and segmentation. The scale bar in part A is 50 μm[
