Publication Date:
2018
Description:
〈p〉Publication date: 1 December 2018〈/p〉
〈p〉〈b〉Source:〈/b〉 Physica B: Condensed Matter, Volume 550〈/p〉
〈p〉Author(s): H. Sadou, T. Hacib, Y. Le Bihan, O. Meyer, H. Acikgoz〈/p〉
〈div xml:lang="en"〉
〈h5〉Abstract〈/h5〉
〈div〉〈p〉In this paper, the microwave characterization of dielectric materials using open-ended coaxial line probe is proposed. The measuring cell is a coaxial waveguide terminated by a dielectric sample. The study consists in extracting the real and imaginary part of the relative dielectric permittivity (ε = 〈em〉ε′〈/em〉-〈em〉jε''〈/em〉) of the material under test from the measurements of the probe admittance (〈em〉Y〈/em〉〈sub〉mes〈/sub〉(〈em〉f〈/em〉) = 〈em〉G〈/em〉〈sub〉me〈em〉s〈/em〉〈/sub〉(〈em〉f〈/em〉)+〈em〉jB〈/em〉〈sub〉mes〈/sub〉(〈em〉f〈/em〉)) on a broad band frequency (〈em〉f〈/em〉 between 1 MHz and 1.8 GHz), hence a direct and inverse problems have to be solved. In order to build a database, the direct problem is solved using Finite Elements Method (FEM) for the probe admittance (〈em〉Y〈/em〉(〈em〉f〈/em〉) = 〈em〉G〈/em〉(〈em〉f〈/em〉)+〈em〉jB〈/em〉(〈em〉f〈/em〉)). Concerning the inverse problem, Partial Least Square (PLS) Regression (PLSR) is investigated as a fast, simple and accurate inversion tool. It is a dimensionality reduction method which aims to model the relationship between the matrix of independent variables (predictors) 〈strong〉〈em〉X〈/em〉〈/strong〉 and the matrix of dependant variables (response) 〈strong〉〈em〉Y〈/em〉〈/strong〉. The purpose of PLS is to find the Latent Variables (LV) having the higher ability of prediction by projecting original predictors into a new space of reduced dimension. The original inverse model has only three predictors (〈em〉f〈/em〉, 〈em〉G〈/em〉 and 〈em〉B〈/em〉) but is nonlinear, so inspired by the extended 〈strong〉〈em〉X〈/em〉〈/strong〉 bloc method, more predictors have been created mathematically from the original ones (for example: 1〈em〉/f〈/em〉〈sup〉2〈/sup〉, 〈em〉B〈/em〉/〈em〉f〈/em〉〈sup〉2〈/sup〉, 〈em〉GB〈/em〉, 1/B, 〈em〉G〈/em〉/〈em〉f〈/em〉, 〈em〉f〈/em〉〈sup〉2〈/sup〉〈em〉G〈/em〉, 〈em〉fG〈/em〉〈sup〉2〈/sup〉〈em〉B〈/em〉, 〈em〉f〈/em〉〈sup〉2〈/sup〉〈em〉G〈/em〉〈sup〉2〈/sup〉〈em〉B〈/em〉〈sup〉2〈/sup〉, … etc) in order to take into account the nonlinearity, whence the appellation Predictors Generation Partial Least Square Regression (PG-PLSR). Inversion results of experimental measurements for liquid (ethanol, water) and solid (PEEK (Polyether-ether-ketone)) samples have proved the applicability and efficiency of PG-PLSR in microwave characterization. Moreover, the comparison study in the last section has proved the superiority of PG-PLSR on Multi-Layer Perceptron Neural Network (MLP-NN) in terms of rapidity, simplicity and accuracy.〈/p〉〈/div〉
〈/div〉
Print ISSN:
0921-4526
Electronic ISSN:
1873-2135
Topics:
Physics
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