文献类型：期刊文献

英文题名：Sensor Signal Decoupling Modeling Based on Hierarchical Cascade Residual Network

作者：Bi, Jinglong[1]; Liang, Kaihao[1]; Hu, Yue[1]; Gao, Yang[1]

机构：[1] School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, China

年份：2024

起止页码：1584

外文期刊名：2024 10th Asia Conference on Mechanical Engineering and Aerospace Engineering, MEAE 2024

收录：EI(收录号：20253118907759)

语种：英文

外文关键词：Forecasting - Learning systems - Neural networks - Optical resonators - Remote sensing - Signal processing

摘要：In-situ monitoring of equipment in harsh environments is crucial in various engineering fields. Traditional wired measurement methods are limited due to the highly enclosed and explosive nature of these environments, leading to the use of wireless passive sensors for monitoring. The coupling relationship between multi-parameter sensor signals makes data decoupling challenging. To improve the prediction accuracy of each variable's data, a temperature and humidity prediction method based on a hierarchical cascade residual neural network (HCRN) is proposed. This method addresses the introduction of prior information through hierarchical training and uses a residual structure in the humidity prediction model to enhance model performance. By collecting the resonant frequency under certain temperature and humidity conditions using a Complementary Split Ring Resonator (CSRR) sensor, this method models the data and verifies the model, achieving optimal multi-target prediction accuracy. Compared to extreme learning machines, support vector regression, and fully connected neural networks, this model performs the best. ? 2024 IEEE.