Study on Droplet Composition Recognition Based on LSTM Network Combined with Convolutional Neural Networks

Abstract

The collision and spreading processes of droplets are not the same due to the physical and chemical characteristics of droplets of different composition. A droplet collision experiment system was set up to capture videos of the droplet collision and spreading processes which changed as the droplet composition changed. A cascaded network was applied to extract spatial-temporal features in the videos and realize droplet composition recognition. The proposed method achieved 80% classification accuracy, which was relatively better performance compared to single back propagation network.