首页|Compression of Battery X-Ray Tomography Data with Machine Learning

Compression of Battery X-Ray Tomography Data with Machine Learning

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With the increasing demand for high-resolution x-ray tomography in battery characterization,the challenges of storing,transmitting,and analyzing substantial imaging data necessitate more efficient solutions.Traditional data compression methods struggle to balance reduction ratio and image quality,often failing to preserve critical details for accurate analysis.This study proposes a machine learning-assisted compression method tailored for battery x-ray imaging data.Leveraging physics-informed representation learning,our approach significantly reduces file sizes without sacrificing meaningful information.We validate the method on typical battery materials and different x-ray imaging techniques,demonstrating its effectiveness in preserving structural and chemical details.Experimental results show an up-to-95 compression ratio while maintaining high fidelity in the projection and reconstructed images.The proposed framework provides a promising solution for managing large-scale battery x-ray imaging datasets,facilitating significant advancements in battery research and development.

颜子沛、王其钰、禹习谦、李济舟、吴国宝

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Department of Electronic Engineering,The Chinese University of Hong Kong,Hong Kong,China

Beijing National Laboratory for Condensed Matter Physics,and Institute of Physics,Chinese Academy of Sciences,Beijing 100190,China

Huairou Division,Institute of Physics,Chinese Academy of Sciences,Beijing 101400,China

Center of Materials Science and Optoelectronics Engineering,University of Chinese Academy of Sciences,Beijing 100049,China

CUHK Shenzhen Research Institute,Shenzhen 518057,China

Department of Mathematics,Hong Kong Baptist University,Hong Kong,China

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New Energy Vehicle Power Battery Life Cycle Testing and Verification Public Service Platform ProjectNational Natural Science Foundation of ChinaHKRGC GRFHKRGC GRFHKRGC GRFJoint NSFC-RGC

2022-235-224523033011720102017300021C7004-21GFN-HKU769/21

2024

10.1088/0256-307X/41/9/098901

中国物理快报(英文版)
中国科学院物理研究所,中国物理学会

中国物理快报(英文版)

CSTPCDEI
影响因子：0.515
ISSN：0256-307X
年,卷(期)：2024.41(9)