XXXVIII Simposio Nacional de la Unión Científica Internacional de Radio - URSI 2023, Caceres (Spain). 13-15 September 2023
Summary:
This paper presents the application of Support Vector Regressor models trained with glycerin-water mixture signals from a Dielectric Resonator sensor. Each signal is labeled with a concentration considered. The performance of these models indicates which mixing rule fits the most with experimental permittivity values. Some modifications of these formulas are validated to acquire better estimations.
Published in: URSI 2023: Libro de actas del XXXVIII Simposio Nacional de la Unión Científica de Radio, Cáceres, 13 a 15 de septiembre de 2023, ISBN: 978-84-09-53230-8
Publication date: 31-Dec-2023.
Citation:
M. Monteagudo Honrubia, F.J. Herraiz-Martínez, J. Matanza, "A Machine Learning approach for the validation and optimization of permittivity mixing rules for binary liquids", presented at XXXVIII Simposio Nacional de la Unión Científica Internacional de Radio - URSI 2023, Caceres, Spain, 13-15 September 2023. In: URSI 2023: Libro de actas del XXXVIII Simposio Nacional de la Unión Científica de Radio, Cáceres, 13 a 15 de septiembre de 2023
IIT-23-142C
