Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System

Sensors

Marko Vauhkonen¹  Rahul Yadav¹  Timo Lähivaara¹  Guido Link²  Adel Omrani² 

[1] Department of Applied Physics, University of Eastern Finland, FI-70210 Kuopio, Finland;Institute for Pulsed Power and Microwave Technology, Karlsruhe Institute of Technology (KIT), 76133 Karlsruhe, Germany;

关键词: microwave drying;    moisture content distribution;    microwave tomography;    inverse problems;    neural networks;   

DOI  :  10.3390/s21206919

来源: DOAJ

【 摘 要 】

The article presents an application of microwave tomography (MWT) in an industrial drying system to develop tomographic-based process control. The imaging modality is applied to estimate moisture distribution in a polymer foam undergoing drying process. Our Leading challenges are fast data acquisition from the MWT sensors and real-time image reconstruction of the process. Thus, a limited number of sensors are chosen for the MWT and are placed only on top of the polymer foam to enable fast data acquisition. For real-time estimation, we present a neural network-based reconstruction scheme to estimate moisture distribution in a polymer foam. Training data for the neural network is generated using a physics-based electromagnetic scattering model and a parametric model for moisture sample generation. Numerical data for different moisture scenarios are considered to validate and test the performance of the network. Further, the trained network performance is evaluated with data from our developed prototype of the MWT sensor array. The experimental results show that the network has good accuracy and generalization capabilities.

【 授权许可】

Unknown