Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System
Abstract
:1. Introduction
2. Problem Formulation
2.1. Forward Model
2.2. Parametric Model for Moisture Distribution
Algorithm 1 Pseudocode for generating the moisture distribution. Note that a small diagonal component is added in matrix C to ensure the positive definiteness. 

3. Inverse Problem: Convolutional Neural Network
3.1. Training, Validation, and Test Datasets
3.2. Reconstruction Results
3.2.1. Sample with Low, and Moderate Moisture Content
3.2.2. Sample with High Moisture Distribution
3.2.3. Error Statistics
4. Experimental Setup and Result
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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${\overline{\mathit{a}}}_{\mathit{\theta}}$  ${\mathit{\delta}}_{{\mathit{a}}_{\mathit{\theta}}}$  ${\overline{\mathit{b}}}_{\mathit{\theta}}$  ${\mathit{\delta}}_{{\mathit{b}}_{\mathit{\theta}}}$  

${\u03f5}_{r}^{\prime}$  1.085  0.01591  0.01256  0.00062 
${\u03f5}_{r}^{\u2033}$  0.03021  0.0025  0.02249  0.0009 
Low Moisture  Moderate Moisture  

$\kappa $  0.9558  0.9361 
High Variation  Homogeneous  

$\kappa $  0.923  0.883 
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Yadav, R.; Omrani, A.; Link, G.; Vauhkonen, M.; Lähivaara, T. Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System. Sensors 2021, 21, 6919. https://doi.org/10.3390/s21206919
Yadav R, Omrani A, Link G, Vauhkonen M, Lähivaara T. Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System. Sensors. 2021; 21(20):6919. https://doi.org/10.3390/s21206919
Chicago/Turabian StyleYadav, Rahul, Adel Omrani, Guido Link, Marko Vauhkonen, and Timo Lähivaara. 2021. "Microwave Tomography Using Neural Networks for Its Application in an Industrial Microwave Drying System" Sensors 21, no. 20: 6919. https://doi.org/10.3390/s21206919