@inproceedings{5f9d8c387b8949188dbaf190b7f193f6,
title = "SE-ResNet-based noise reduction for steady-state micro-thrust measurement",
abstract = "Micro-Newton thrusters are widely utilized in the field of astronautics. Typically, the precision of micro-newton thrust measurement is fundamentally hinged upon the level of background noise. In this research, we introduce Residual Neural Network (ResNet) to identify the effective signals merged in the background noise. Experimental studies are carried out to investigate the effect of noise reduction of ResNet. Squeeze-and-Excitation (SE) block and the SE-ResNet are then adopted to optimize the net. It is shown that steady-state signal with 0.1μN as the minimum change unit can be recovered from the noises with amplitude of 0.8μN, and the accuracy reaches 70.70\% with ResNet. Besides, SE-ResNet shows better performance with accuracy of 73.41\% than the conventional ResNet. The proposed method has great potential for noise reduction of steady-state sensor signals.",
keywords = "ResNet, noise reduction, thrust measurement",
author = "Zhikang Liu and Chengxin Zhang and Xingyu Chen and Jiawen Xu and Liye Zhao and Ruqiang Yan",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 3rd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022 ; Conference date: 22-12-2022 Through 24-12-2022",
year = "2022",
doi = "10.1109/ICSMD57530.2022.10058468",
language = "英语",
series = "2022 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022 - Proceedings",
}