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 = ε′-jε’’) of the material under test from the measurements of the probe admittance (Ymes(f) = Gmes(f)+jBmes(f)) on a broad band frequency (f 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 (Y(f) = G(f)+jB(f)). 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) X and the matrix of dependant variables (response) Y. 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 (f, G and B) but is nonlinear, so inspired by the extended X bloc method, more predictors have been created mathematically from the original ones (for example: 1/f2, B/f2, GB, 1/B, G/f, f2G, fG2B, f2G2B2, … 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. © 2018 Elsevier B.V.
Eser Adı (dc.title) | Predictors Generation by Partial Least Square Regression for microwave characterization of dielectric materials |
Yayın Türü (dc.type) | Makale |
Yazar/lar (dc.contributor.author) | SADOU, Hakim |
Yazar/lar (dc.contributor.author) | HACIB, Tarik |
Yazar/lar (dc.contributor.author) | LE BIHAN, Yan |
Yazar/lar (dc.contributor.author) | MEYER, Olivier |
Yazar/lar (dc.contributor.author) | AÇIKGÖZ, Hulusi |
DOI Numarası (dc.identifier.doi) | 10.1016/j.physb.2018.08.037 |
Atıf Dizini (dc.source.database) | Scopus |
Yayıncı (dc.publisher) | Elsevier B.V. |
Yayın Tarihi (dc.date.issued) | 2018 |
Kayıt Giriş Tarihi (dc.date.accessioned) | 2020-08-07T12:51:52Z |
Açık Erişim tarihi (dc.date.available) | 2020-08-07T12:51:52Z |
Kaynak (dc.source) | Physica B: Condensed Matter |
ISSN (dc.identifier.issn) | 09214526 (ISSN) |
Özet (dc.description.abstract) | 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 = ε′-jε’’) of the material under test from the measurements of the probe admittance (Ymes(f) = Gmes(f)+jBmes(f)) on a broad band frequency (f 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 (Y(f) = G(f)+jB(f)). 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) X and the matrix of dependant variables (response) Y. 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 (f, G and B) but is nonlinear, so inspired by the extended X bloc method, more predictors have been created mathematically from the original ones (for example: 1/f2, B/f2, GB, 1/B, G/f, f2G, fG2B, f2G2B2, … 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. © 2018 Elsevier B.V. |
Yayın Dili (dc.language.iso) | en |
Tek Biçim Adres (dc.identifier.uri) | http://hdl.handle.net/20.500.12498/2834 |