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

Abstract

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 imaging system consisting of 24 transceivers installed in the bin. Key for practical applications, the neural networks are trained on synthetic data sets but generate the parametric information using experimental data as input, without the use of calibration objects or open-short-load measurements. To accomplish this, we formulate a data normalization scheme that enables the use of a loss function that directly compares measured S-parameters and simulation model fields. The normalization strategy and the ability to train on synthetic data means we do not need to collect experimental training data. We demonstrate the applicability of this synthetically trained neural network to experimental data from two different bin geometries, and discuss the ability of these neural networks to successfully infer parameters that can be used for grain inventory management. Our neural-network-based approach enables rapid inference, providing a more cost-effective long-term solution than existing optimization-based parametric inversion methods.

A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging

“…To offset the difference in magnitude of the parameter labels and to ensure that each of the four parameters is weighted equally in the loss function, the labels are normalized to zero mean, and unit variance prior to training. Full details on the bulk parameter inference network are available in our previous work [35].…”

“…We have recently had success extracting bulk imaging parameters from phaseless electromagnetic field data in the application which are stored grain monitoring. In grain bin imaging, the parameters consisting of grain height, angle of repose and bulk complexvalued permittivity, are obtained using either an iterative optimization technique [33] or machine learning using either single-frequency data [34] or multi-frequency data [35]. The machine learning approach provides a cost-effective, near real-time, long-term monitoring solution where the cost of a computationally expensive optimization for every measurement is instead transferred to one-time network training.…”

“…No experimental data is required for training the machine learning model presented in this work. An additional contribution includes the generalization of the machine-learning work-flow originally developed for grain-bin imaging to microwave breast imaging [33][34][35].…”

Machine-Learning-Enabled Recovery of Prior Information From Experimental Breast Microwave Imaging Data

“…In recent years, significant attention has been paid to applying machine learning for enabling, assisting, and improving electromagnetic imaging. Machine learning approaches include obtaining prior information from the data [15], improving conventional microwave imaging results with neural networks [16][17][18], and data-to-image reconstructions with assumed prior information [19]. In cases where the output of the machine learning model is an image, most experimental results are 2D, and benefit from initial early iterations of imaging algorithms [17,20].…”

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“…On the other hand, the ever-increasing available computing power is providing unvaluable tools for the practical implementation of the proposed solution techniques. In this rapidly-evolving situation, the recent introduction of novel methods based on artificial intelligence [21] and in particular deep learning approaches [22], [23] is opening new doors, overcoming several limitations of the traditional inversion techniques and lowering computational times [24]- [27].…”