Results of comparison experiments. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Architecture of Swin Transformer Block. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Disease distribution map of the GZDL-BD. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Token merging module. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

Comparative results of the ablation experiments. by Wen-Qing Huang (5258126)

“…However, traditional methods heavily rely on low-level image analysis, handcrafted features, and classical classifiers, leading to limited effectiveness and poor generalization in complex scenarios. Although significant progress has been made with deep learning methods, challenges persist in handling high-resolution images and diverse disease types. …”

The aging parameters of each group of batteries. by Peijian Jin (22265437)

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The peak frequency of the two waveforms of the acoustic emission signal of batteries. by Peijian Jin (22265437)

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Connection of lithium-ion battery acoustic emission experimental equipment. by Peijian Jin (22265437)

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Waveform time interval in the 0.5C discharge stage of each group. by Peijian Jin (22265437)

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Capacity curve of lithium-ion battery cyclic aging. by Peijian Jin (22265437)

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Waveform time interval in the 1C discharge stage of each group. by Peijian Jin (22265437)

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Flowchart of the extraction of the peak frequencies of the two waveforms. by Peijian Jin (22265437)

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Minimal data set. by Peijian Jin (22265437)

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Battery parameters. by Peijian Jin (22265437)

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Battery voltage changes during discharge and charge. by Peijian Jin (22265437)

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Burst acoustic emission signal recorded in continuous acquisition mode. by Peijian Jin (22265437)

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Signal time interval in the 0.5C discharge stage of each group. by Peijian Jin (22265437)

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Experimental lithium-ion batteries. by Peijian Jin (22265437)

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Schematic diagram of two time intervals. by Peijian Jin (22265437)

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Acoustic emission experimental scheme for lithium-ion batteries. by Peijian Jin (22265437)

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