This talk reviews research work carried out in the Laboratoire de Génie Electrique de Paris durin... more This talk reviews research work carried out in the Laboratoire de Génie Electrique de Paris during the last decade about finite element modeling for microwave engineering applications. The first part is devoted to coupled electromagnetic-thermal problems. In particular the microwave heating of dielectric material located in a coaxial cell will be presented. The second part summarizes recent work dedicated to microwave characterization of dielectric material using a combination of finite element methods and neural networks. It is hoped that the material of this paper could help to evaluate the potential of the finite element technique for microwave sintering.
Purpose – The dielectric properties of materials (complex permittivity) can be deduced from the a... more Purpose – The dielectric properties of materials (complex permittivity) can be deduced from the admittance measured at the discontinuity plane of a coaxial open-ended probe. This implies the implementation of an inversion procedure. The purpose of this paper is to develop a new non-iterative inversion methodology in the field of microwave characterization allowing reducing the computation cost comparatively to iterative
This paper deals with the homogenization of biological tissues for electromagnetic dosimetry appl... more This paper deals with the homogenization of biological tissues for electromagnetic dosimetry applications. In order to reduce the computation time, we present alternative methods for the human head modeling. We show that using only the external interface between the air and the skin gives better results compared to other proposed homogenization techniques.
This paper presents the use of the least-square support vector machines (LS-SVM) technique, combi... more This paper presents the use of the least-square support vector machines (LS-SVM) technique, combined with the finite element method (FEM), to evaluate the microwave properties of dielectric materials. The LS-SVM is a statistical learning method that has good generalization capability and learning performance. The FEM is used to create the data set required to train the LS-SVM. The performance of LS-SVM model depends on a careful setting of its associated hyper-parameters. Different tuning techniques for optimizing the LS-SVM hyper-parameters are studied: cross validation (CV), genetic algorithms (GA), heuristic approach, and Bayesian regularization (BR).
This paper shows the efficiency of neural networks (NN), coupled with the finite element method (... more This paper shows the efficiency of neural networks (NN), coupled with the finite element method (FEM), to evaluate the broadband properties of dielectric materials. A characterization protocol is built to characterize dielectric materials and NN are used in order to provide the estimated permittivity. The FEM is used to create the data set required to train the NN. A method based on Bayesian regularization ensures a good generalization capability of the NN. It is shown that NN can determine the permittivity of materials with a high accuracy and that the Bayesian regularization greatly simplifies their implementation.
The aim of this study is to determine the complex permittivity of dielectric materials using a co... more The aim of this study is to determine the complex permittivity of dielectric materials using a coaxial discontinuity and the combination of neural networks (NN) with the finite element method. Two types of measurement cells are used. One is for solid samples and the other one for liquids. Data sets used to train neural networks are created using the finite
This talk reviews research work carried out in the Laboratoire de Génie Electrique de Paris durin... more This talk reviews research work carried out in the Laboratoire de Génie Electrique de Paris during the last decade about finite element modeling for microwave engineering applications. The first part is devoted to coupled electromagnetic-thermal problems. In particular the microwave heating of dielectric material located in a coaxial cell will be presented. The second part summarizes recent work dedicated to microwave characterization of dielectric material using a combination of finite element methods and neural networks. It is hoped that the material of this paper could help to evaluate the potential of the finite element technique for microwave sintering.
Purpose – The dielectric properties of materials (complex permittivity) can be deduced from the a... more Purpose – The dielectric properties of materials (complex permittivity) can be deduced from the admittance measured at the discontinuity plane of a coaxial open-ended probe. This implies the implementation of an inversion procedure. The purpose of this paper is to develop a new non-iterative inversion methodology in the field of microwave characterization allowing reducing the computation cost comparatively to iterative
This paper deals with the homogenization of biological tissues for electromagnetic dosimetry appl... more This paper deals with the homogenization of biological tissues for electromagnetic dosimetry applications. In order to reduce the computation time, we present alternative methods for the human head modeling. We show that using only the external interface between the air and the skin gives better results compared to other proposed homogenization techniques.
This paper presents the use of the least-square support vector machines (LS-SVM) technique, combi... more This paper presents the use of the least-square support vector machines (LS-SVM) technique, combined with the finite element method (FEM), to evaluate the microwave properties of dielectric materials. The LS-SVM is a statistical learning method that has good generalization capability and learning performance. The FEM is used to create the data set required to train the LS-SVM. The performance of LS-SVM model depends on a careful setting of its associated hyper-parameters. Different tuning techniques for optimizing the LS-SVM hyper-parameters are studied: cross validation (CV), genetic algorithms (GA), heuristic approach, and Bayesian regularization (BR).
This paper shows the efficiency of neural networks (NN), coupled with the finite element method (... more This paper shows the efficiency of neural networks (NN), coupled with the finite element method (FEM), to evaluate the broadband properties of dielectric materials. A characterization protocol is built to characterize dielectric materials and NN are used in order to provide the estimated permittivity. The FEM is used to create the data set required to train the NN. A method based on Bayesian regularization ensures a good generalization capability of the NN. It is shown that NN can determine the permittivity of materials with a high accuracy and that the Bayesian regularization greatly simplifies their implementation.
The aim of this study is to determine the complex permittivity of dielectric materials using a co... more The aim of this study is to determine the complex permittivity of dielectric materials using a coaxial discontinuity and the combination of neural networks (NN) with the finite element method. Two types of measurement cells are used. One is for solid samples and the other one for liquids. Data sets used to train neural networks are created using the finite
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