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入选国家级领军人才,于2000年获得国防科技大学计算机专业的学士学历,2006年获得清华大学计算机系工学硕士和博士学位,2006至2008年在新加坡南洋理工大学完成博士后研究工作,智能系统中心担任研究员(Research Fellow) 6. 国家自然科学基金重点项目子课题:土壤有机碳储量的人工神经网络模拟方法与模型(编号:42130716 ), 2022-2026(78万) 7. 湖南创新型省份建设专项高新技术产业科技创新引领计划:基于人工智能技术的茶叶关键装备研发与推广应用 (编号:2020NK2008),2020-2022(100万)
(1) Tang Z, Li Z, Yang J, et al. P &GGD: A Joint-Way Model Optimization Strategy Based on Filter Pruning and Filter Grafting For Tea Leaves Classification[J]. Neural Processing Letters, 2022: 1-17. (SCI二区) (2) Qi F, Wang Y, Tang Z. Lightweight Plant Disease Classification Combining GrabCut Algorithm, New Coordinate Attention, and Channel Pruning[J]. Neural Processing Letters, 2022: 1-15. (SCI二区) (3) Qi F, Xie Z, Tang Z, et al. Related study based on Otsu watershed algorithm and new squeeze-and-excitation networks for segmentation and level classification of tea buds[J]. Neural Processing Letters, 2021, 53(3): 2261-2275. (SCI二区) (4) Zhe Tang, Yuancheng Su , Meng Joo Er,Fang Qi*, A Local Binary Pattern Based Texture Descriptors for Classification of Tea Leaves, Neurocomputing, 168 (2015), pp1011-1023(SCI一区). (5) Vulnerabilities, Attacks, and Countermeasures in Balise-Based TrainControl Systems Yongdong Wu; Jian Weng; Zhe Tang; Xin Li; Robert H. Deng, IEEE Transactions on Intelligent Transportation Systems Year: 2016, Volume: PP, Issue: 99, Pages: 1 - 10, DOI: 10.1109/TITS.2016.2590579 (6) Quanyun Wei, Fang Qi, Zhe Tang,Remove key escrow from the BF and Gentry identity-based encryption with non-interactive key generation, Telecommunication Systems (2018) 69:253–262 (7) Zhe Tang, Fang Qi, Yi Zhou, Tea Leaves Classification Based on Texture Analysis, Lecture Notes in Electrical Engineering 336, pp353-360, 2015.(EI) (8) Zhe Tang, Jialing Yang , Zhe Li,Fang Qi*,Grape disease image classification based on lightweight convolution neural networks and channelwise attention, Computers and Electronics in Agriculture , 178 (2020), 105735 (SCI一区) (9) Fang Qi, Zuoqi Xie, Zhe Tang*, Huarong Chen, Related Study Based on Otsu Watershed Algorithm and New Squeeze-and-Excitation Networks for Segmentation and Level Classification of Tea Buds, Neural Processing Letters, 53(2021), pp.2261-2275. (SCI二区) (10) Zhe Tang, Zhengyun Chen, Fang Qi, Lingyan Zhang, and Shuhong Chen, Pest-YOLO: Deep Image Mining and Multi-Feature Fusion for Real-Time Agriculture Pest Detection, 21st IEEE International Conference on Data Mining. (11) Fang Qi , Gangming Chen , Jieyuan Liu, Zhe Tang, End-to-end pest detection on an improved deformable DETR with multihead criss cross attention, Ecological Informatics, 72 (2022) 101902 (SCI二区) (12) Zhe Tang, Lin Zhou, Fang Qi, Huarong Chen, An improved lightweight and real‑time YOLOv5 network for detection of surface defects on indocalamus leaves, Journal of Real-Time Image Processing (2023) 20:14 (SCI二区)
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