| Lightweight residual convolutional neural network for soybean classification combined with electronic nose H Lin, H Chen, C Yin, Q Zhang, Z Li, Y Shi, H Men IEEE Sensors Journal 22 (12), 11463-11473, 2022 | 54 | 2022 |
| Determination of the quality of tea from different picking periods: An adaptive pooling attention mechanism coupled with an electronic nose S Kang, Q Zhang, Z Li, C Yin, N Feng, Y Shi Postharvest Biology and Technology 197, 112214, 2023 | 46 | 2023 |
| An adaptive learning method for the fusion information of electronic nose and hyperspectral system to identify the egg quality Q Zhang, S Kang, C Yin, Z Li, Y Shi Sensors and Actuators A: Physical 346, 113824, 2022 | 34 | 2022 |
| PSCFormer: A lightweight hybrid network for gas identification in electronic nose system Z Li, S Kang, N Feng, C Yin, Y Shi Pattern Recognition 145, 109912, 2024 | 15 | 2024 |
| Performance improvement: A lightweight gas information classification method combined with an electronic nose system Y Shi, B Wang, C Yin, Z Li, Y Yu Sensors and Actuators B: Chemical 396, 134551, 2023 | 10 | 2023 |
| A residual dense comprehensively regulated convolutional neural network to identify spectral information for egg quality traceability H Lin, X He, H Chen, Z Li, C Yin, Y Shi Analytical Methods 14 (38), 3780-3789, 2022 | 10 | 2022 |
| Determination of Tea Quality: A Separate Domain Graph Convolution Network Combined With An Electronic Nose N Fen, S Kang, C Yin, Z Li, Y Shi IEEE Sensors Journal, 2024 | | 2024 |
Yan LuThe Chinese University of Hong Kong; Shanghai AI laboratory <<< University of Sydney在 pjlab.org.cn 的电子邮件经过验证
Yan LuThe Chinese University of Hong Kong; Shanghai AI laboratory <<< University of Sydney在 pjlab.org.cn 的电子邮件经过验证
Wanli Ouyang (欧阳万里)Shanghai AI Lab & CUHK在 sydney.edu.au 的电子邮件经过验证