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Jing Zhang - IEEE Xplore Author Profile
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Jing Zhang

Publications
2
Citations
0
Publications by Year
20242025
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Jing Zhang

Affiliation

Automation College
Jiangsu University of Science and Technology
Zhenjiang, China

Publication Topics

Biography

Jing Zhang received the B.S. degree from Nantong Institute of Technology, Nantong, China, in 2001, and the M.S. degree from Nanjing University of Science and Technology, Nanjing, China, in 2008. She is currently an Associate Professor with the School of Computer Science, Jiangsu University of Science and Technology. Her research interests include computer architecture and machine learning.(Based on document published on 19 December 2024).
Publications
2
Citations
0
Publications by Year
20242025
Co-Authors:
Show All Co-Authors (8)
Author's Published Works

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Wavelet-Transform-Based Neural Network for Tidal Flat Remote Sensing Image Deblurring

Denghao Yang;Zhiyu Zhu;Huilin Ge;Cheng Xu;Jing Zhang

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year: 2025 | Volume: 18 | Journal Article |
In response to the challenge of image degradation caused by strong sea breezes during drone surveillance over tidal flats, conventional methodologies have predominantly employed iterative upsampling and downsampling techniques to augment the receptive field of the network. However, this approach is prone to the loss of critical texture data within the tidal flat imagery throughout the sampling pro...Show More

Wavelet-Transform-Based Neural Network for Tidal Flat Remote Sensing Image Deblurring

Denghao Yang;Zhiyu Zhu;Huilin Ge;Cheng Xu;Jing Zhang

Year: 2025 | Volume: 18 | Journal Article |

A Lightweight Transformer Model for Defect Detection in Electroluminescence Images of Photovoltaic Cells

Yang Yang;Jing Zhang;Xin Shu;Lei Pan;Ming Zhang

IEEE Access
Year: 2024 | Volume: 12 | Journal Article |
Solar panels play a crucial role in converting solar energy into electricity, with PhotoVoltaic (PV) modules being their core components. To ensure solar panels function well, efficient and accurate defect detection of PV modules is essential. Visual-based deep learning detection methods, such as Transformer and Convolutional Neural Network (CNN) models, provide a cost-effective and adaptable solu...Show More

A Lightweight Transformer Model for Defect Detection in Electroluminescence Images of Photovoltaic Cells

Yang Yang;Jing Zhang;Xin Shu;Lei Pan;Ming Zhang

Year: 2024 | Volume: 12 | Journal Article |

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