Feature Extraction and Attribute Recognition of Aerosol Particles from In Situ Light-Scattering Measurements Based on EMD-ICA Combined LSTM Model

Abstract

Accurate identification and monitoring of aerosol properties is crucial for understanding their sources and impacts on human health and the environment. Therefore, we propose a feature extraction and attribute recognition method from in situ light-scattering measurements based on Bayesian Optimization, wavelet scattering transform, and long short-term memory neural network (BO-WST-LSTM), with empirical mode decomposition (EMD) and independent component analysis (ICA) algorithm for signal preprocessing. In this study, an experimental platform was utilized to gather light-scattering signals from particles with varying characteristics. The signals are then processed using the EMD-ICA noise reduction technique. Then, the wavelet scattering network is used to realize the adaptive extraction of the characteristics of the particle light-scattering signal, and the Bayesian Optimization model is used to optimize the hyperparameters of the LSTM neural network. The extracted scattering coefficient matrix is input into the LSTM for iterative training. Finally, the SoftMax layer’s probability classification method is applied to the identification of particle attributes. The results show that the multi-angle particle light-scattering signal detection system designed and built in this study performs well and is capable of effectively collecting particle light-scattering signals. At the same time, the proposed new method for particle property recognition demonstrates good classification performance for six different types of particles with a particle size of 2 μm, achieving a classification accuracy of 98.83%. This proves its effectiveness in recognizing particle properties and provides a solid foundation for particle identification.