XXXVIII Simposio Nacional de la Unión Científica Internacional de Radio - URSI 2023, Cáceres (España). 13-15 septiembre 2023
Resumen:
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.
Publicado en URSI 2023, ISBN: 978-84-09-53230-8
Fecha de publicación: 2023-12-31.
Cita:
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, XXXVIII Simposio Nacional de la Unión Científica Internacional de Radio - URSI 2023, Cáceres (España). 13-15 septiembre 2023. En: 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
