Add to ORKGIn this paper we discuss the use of the Probabilistic Neural Network (PNN) for the classification of the defects detected via the Remote Field Eddy Current (RFEC) inspection technique. The neural network is employed in order to associate each defect to one of the predefined classes. Each defect is represented by means of the phase response of the probe system. The reported results show that the proposed artificial neural network allows reliable classification results
In this paper, we present a fast method for classification of defects detected by eddy current testi...
In the framework of Eddy Current Testing (ECT), this work presents an automated non-destructive test...
In the framework of Eddy Current Testing (ECT), this work presents an automated non-destructive test...
In this paper we discuss the use of the Probabilistic Neural Network (PNN) for the classification of...
This work was partially supported by the Italian MURST. Abstract- The aim of this paper is to presen...
The aim of this paper is to propose the use of elliptical basis function probabilistic neural networ...
The aim of this work is to propose and discuss a technique which allows for classifying the defects ...
The inverse problem in nondestructiye evaluation involves the characterization of flaw parameters gi...
Accurate evaluation and characterization of defects in multilayered structures from eddy current non...
The aim of this work is to propose and discuss a technique which allows for classifying the defects ...
A new methodology has been proposed to carry on-line multifrequency eddy current testing. This uses ...
This paper presents an artificial neural network for quantitative eddy current testing of materials....
International audienceIn the aeronautics sector, aircraft parts are inspected during manufacture, as...
The probability of detection (POD) of hidden fatigue defects in riveted multilayer joints, e.g. airc...
In this paper, we present a fast method for classification of defects detected by eddy current testi...
In this paper, we present a fast method for classification of defects detected by eddy current testi...
In the framework of Eddy Current Testing (ECT), this work presents an automated non-destructive test...
In the framework of Eddy Current Testing (ECT), this work presents an automated non-destructive test...
In this paper we discuss the use of the Probabilistic Neural Network (PNN) for the classification of...
This work was partially supported by the Italian MURST. Abstract- The aim of this paper is to presen...
The aim of this paper is to propose the use of elliptical basis function probabilistic neural networ...
The aim of this work is to propose and discuss a technique which allows for classifying the defects ...
The inverse problem in nondestructiye evaluation involves the characterization of flaw parameters gi...
Accurate evaluation and characterization of defects in multilayered structures from eddy current non...
The aim of this work is to propose and discuss a technique which allows for classifying the defects ...
A new methodology has been proposed to carry on-line multifrequency eddy current testing. This uses ...
This paper presents an artificial neural network for quantitative eddy current testing of materials....
International audienceIn the aeronautics sector, aircraft parts are inspected during manufacture, as...
The probability of detection (POD) of hidden fatigue defects in riveted multilayer joints, e.g. airc...
In this paper, we present a fast method for classification of defects detected by eddy current testi...
In this paper, we present a fast method for classification of defects detected by eddy current testi...
In the framework of Eddy Current Testing (ECT), this work presents an automated non-destructive test...
In the framework of Eddy Current Testing (ECT), this work presents an automated non-destructive test...