The aim of this study is to develop a modeling procedure based on using artificial neural networks (ANNs) for predicting the frequency-dependent behavior of a microwave split-ring resonator (SRR) used for the dielectric characterization of liquid samples. The SRR device was designed and fabricated using the inkjet printing technology and, then, calibrated by means of water/ethanol mixtures with varying concentrations. By observing the variations in the forward transmission coefficient (i.e., S21) of the studied microwave device, a frequency shift of the resonant frequency and variations in the magnitude of S21 were recorded, which were related to the ethanol volume fraction. Using this calibration data, an ANN-based model is developed, which takes the ethanol volume fraction as input feature and, then, predicts the SRR sensor resonant parameters. The accuracy of the ANN-based model is reported and discussed.

Artificial Neural Network Modeling of Microwave Sensors for Dielectric Liquids Characterization

Gugliandolo, Giovanni
Primo
;
De Marchis, Cristiano;Battaglia, Filippo;Latino, Mariangela;Campobello, Giuseppe;Crupi, Giovanni
Penultimo
;
Donato, Nicola
Ultimo
2023-01-01

Abstract

The aim of this study is to develop a modeling procedure based on using artificial neural networks (ANNs) for predicting the frequency-dependent behavior of a microwave split-ring resonator (SRR) used for the dielectric characterization of liquid samples. The SRR device was designed and fabricated using the inkjet printing technology and, then, calibrated by means of water/ethanol mixtures with varying concentrations. By observing the variations in the forward transmission coefficient (i.e., S21) of the studied microwave device, a frequency shift of the resonant frequency and variations in the magnitude of S21 were recorded, which were related to the ethanol volume fraction. Using this calibration data, an ANN-based model is developed, which takes the ethanol volume fraction as input feature and, then, predicts the SRR sensor resonant parameters. The accuracy of the ANN-based model is reported and discussed.
2023
979-8-3503-0080-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3286288
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