Stored Grain Inventory Management Using Neural-Network-Based Parametric Electromagnetic Inversion

Stored Grain Inventory Management Using Neural-Network-Based Parametric Electromagnetic Inversion

We present a neural network architecture to determine the volume and complex permittivity of grain stored in metal bins. The neural networks output the grain height, cone angle and complex permittivity of the grain, using the input of experimental field data (S-parameters) from an electromagnetic im...

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Bibliographic Details
Main Authors: Keeley Edwards, Nicholas Geddert, Kennedy Krakalovich, Ryan Kruk, Mohammad Asefi, Joe Lovetri, Colin Gilmore, Ian Jeffrey
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Electromagnetic inverse scattering
machine learning
calibration
parametric inversion
phaseless imaging
grain storage monitoring
Online Access:https://ieeexplore.ieee.org/document/9260139/

Internet

https://ieeexplore.ieee.org/document/9260139/

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