Hulusi Acikgoz

Microwave Characterization Using Least-Square Support Vector Machines

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). Results show that BR provides a good compromise between accuracy and computational cost.

MICROWAVE CHARACTERIZATION OF DIELECTRIC MATERIALS USING BAYESIAN NEURAL NETWORKS

This paper shows the efficiency of neural networks (NN), coupled with the finite element method (FEM), to evaluate the broad- band 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.

Support vector machines for measuring dielectric properties of materials

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 procedures.Design/methodology/approach – The inversion methodology combines the support vector machine (SVM) technique and the finite element method (FEM). The SVM are used as inverse models. They show good approximation and generalization capabilities. FEM allows the generation of the data sets required by the SVM parameter adjustment. A data set is constituted of input (complex admittance and frequency) and output (complex permittivity) pairs.Findings – The results show the applicability of SVM to solve microwave inverse problems instead of using traditional iterative inversion methods w...

An approach based on ANFIS and input selection procedure for microwave characterization of dielectric materials

The principle of microwave characterization of dielectric materials using open-ended coaxial line probe is to link the dielectric properties of the sample under test to the measurements of the probe admittance (Y(f) = G(f)+ jB(f )). The purpose of this paper is to develop an alternative inversion tool able to predict the evolution of the complex permittivity (e = e′ – je″) on a broad band frequency (f from 1 MHz to 1.8 GHz).,The inverse problem is solved using adaptive network based fuzzy inference system (ANFIS) which needs the creation of a database for its learning. Unfortunately, train ANFIS using f, G and B as inputs has given unsatisfying results. Therefore, an inputs selection procedure is used to select the three optimal inputs from new inputs, created mathematically from original ones, using the Jang method.,Inversion results of measurements give, after training, in real time the complex permittivity of solid and liquid samples with a very good accuracy which prove the applicability of ANFIS to solve inverse problems in microwave characterization.,The originality of this paper consists on the use of ANFIS with input selection procedure based on the Jang method to solve the inverse problem where the three optimal inputs are selected from 26 new inputs created mathematically from original ones (f, G and B).

Electromagnetic Acoustic Transducer for cracks detection in conductive material

This paper is concerned with the characterization methodologies of defects in conducting materials by an Electromagnetic Acoustic Transducer (EMAT) testing system. It has been developed to create a virtual environment for Non-Destructive Testing (NDT) before implementing it in real, to study the change effect on the defect geometry at the signal received. EMAT is a new technology, which provides a noncontact process of testing materials compared to ultrasonic testing technique. This work is based on the simulation of two-dimensional numerical model, using Finite Element Method, (FEM) like a simulator model forward analysis, which includes the calculation of induced eddy current, the Lorentz force, and mechanical displacement inside conducting material. Results obtained shows that the model is capable of detecting the depth, the width and the location of the surface defect in an Aluminum material, using the mechanical displacement amplitude.

Microwave characterization using partial least square regression

Inverse problems for determination of dielectric materials properties (complex permittivity) are usually solved by iterative methods using numerically based forward model. These methods are computationally expensive. In this paper, we propose a fast inversion model based on partial least square regression (PLSR). The idea is to build a model able to predict in real time the properties of the sample under test using measurements of admittance or reflexion coefficient of the propagating electromagnetic micro wave along the coaxial line. Numerical solution of the direct problem is made using Finite Element Method (FEM).