Matteo Cacciola

Microwave Devices and Antennas Modelling by Support Vector Regression Machines

Development of fast and accurate models of microwave devices and antennas is of paramount importance in computer-aided design and circuit analysis. At this purpose, artificial neural networks (ANNs) have been extensively exploited in technical literature. However, in the last years support vector machines (SVMs) developed by Vapnik are gaining popularity due to many attractive features capable to overcome the limitations connected to ANNs. In this work, support vector regression machines (SVRMs) modelling performances are investigated and compared with ANNs performances by means of several cases of study

Fuzzy Characterization of Flawed Metallic Plates with Eddy Current Tests

Eddy Current Techniques (ECT) for Non-Destructive Testing and Evaluation (NDT/NDE) of conducting materials is one of the most application-oriented field of research within electromagnetism. In this work, a novel approach is proposed in order to characterize defects on metallic plates in terms of their depth and shape, starting from a set of experimental measurements. The problem is solved by means of a hybrid classification system based on Computation with Words (CWs) and Fuzzy Entropy (FE). They extract information about the specimen under test from the measurements. Main advantages of proposed approach are the introduction of CWs as well as the usage of the FE based minimization module, in order to improve flaw characterization by a low computational complexity system.

SAR imagery classification using multi-class support vector machines

In this paper, we present the application to SAR imagery classification of a novel pattern recognition technique named Multi-class Support Vector Machines (M-SVMs). M-SVMs are a n-ary extension of Support Vector Machines (SVM), introduced by Vapnik within the framework of the Statistical Learning Theory. In this article we use the M-SVMs in order to classify an ERS-1 SAR multi-frequency survey of Torre de Hercules coast, Spain (December 13, 1992). The main objective of this work is to evaluate the classification performances of M-SVMs in comparison with the most frequently employed Neural Networks and Fuzzy classifiers. M-SVMs provided interesting results with respect to Neural Networks and Fuzzy classifiers, having a reliability factor around to 94%.

FEA Design and Misfit Minimization for In-Depth Flaw Characterization in Metallic Plates With Eddy Current Nondestructive Testing

Nondestructive testing techniques are more and more exploited in order to quickly and cheaply recognize flaws into the inspected materials. Within this framework, a concern of eddy current tests is the depth of penetration delta, above all in such applications as the control of steel beams. Thus, an optimal design of exciting coil is strictly required in order to reach as higher delta as possible. The aim of this paper is first to design, by finite-element analysis, and test by in-lab measurements, a suitable exciting coil. Subsequently, the inverse ill-posed problem for defect characterization, starting from experimental measurements, has been studied and regularized, in order to characterize the depth and the extension of defects.

Elman neural networks for characterizing voids in welded strips: a study

Within the framework of aging materials inspection, one of the most important aspects regarding defects detection in metal welded strips. In this context, it is important to plan a method able to distinguish the presence or absence of defects within welds as well as a robust procedure able to characterize the defect itself. In this paper, an innovative solution that exploits a rotating magnetic field is presented. This approach has been carried out by a finite element model. Within this framework, it is necessary to consider techniques able to offer advantages in terms of sensibility of analysis, strong reliability, speed of carrying out, low costs: its implementation can be a useful support for inspectors. To this aim, it is necessary to solve inverse problems which are mostly ill-posed; in this case, the main problems consist on both the accurate formulation of the direct problem and the correct regularization of the inverse electromagnetic problem. We propose a heuristic inversion, regularizing the problem by the use of an Elman network. Experimental results are obtained using a database created through numerical modeling, confirming the effectiveness of the proposed methodology.

Diffusion Tensor Imaging measurements for neuro-detection

The interest of scientific community on brain activities and issues are well-known, especially for neuro-detection of variety of impairments that affect cerebral areas. Various techniques and methods have been using to characterize and to try to understand brain activities for many purposes. Epilepsy, one of them, is a topic of great impact in brain research as well as in Alzheimer issues. Thanks to the development of new biomedical instrumentation it is possible to use appropriate techniques to diagnose the specific pathology. DTI (Diffusion Tensor Imaging) is one of the ultimate technique to have a comprehensive approach to brain activities. This interdisciplinary research highlights the use of DTI to determine preliminarily the ROI (Region Of Interest) for patients with suspected cases of epilepsy. A specific algorithm has been developed to trace out the ROI and the fibers.

Wavelet Coherence and Fuzzy Subtractive Clustering for Defect Classification in Aeronautic CFRP

Despite their high specific stiffness and strength, carbon fiber reinforced polymers, stacked at different fiber orientations, are susceptible to interlaminar damages. They may occur in the form of micro-cracks and voids, and leads to a loss of performance. Within this framework, ultrasonic tests can be exploited in order to detect and classify the kind of defect. The main object of this work is to develop the evolution of a previous heuristic approach, based on the use of Support Vector Machines, proposed in order to recognize and classify the defect starting from the measured ultrasonic echoes. In this context, a real-time approach could be exploited to solve real industrial problems with enough accuracy and realistic computational efforts. Particularly, we discuss the cross wavelet transform and wavelet coherence for examining relationships in time-frequency domains between. For our aim, a software package has been developed, allowing users to perform the cross wavelet transform, the wavelet coherence and the Fuzzy Inference System. Since the ill-posedness of the inverse problem, Fuzzy Inference has been used to regularize the system, implementing a data-independent classifier. Obtained results assure good performances of the implemented classifier, with very interesting applications.

Rotating Electromagnetic Field for Crack Detection in Railway Tracks

The main problem about a railway analysis is detection of cracks in the structure. If these deflciencies are not controlled at early stages they might cause huge economical problems afiecting the rail network (unexpected requisition of spare parts, handling of incident and/or accidents). Within this framework, the early and continuous use of Non Destructive Tests can be useful. In this context, Eddy Current Testing is increasing in importance and popularity. Particularly, in this paper we exploit the measure of normal component, with respect to the scanned surface, of magnetic fleld. Whilst the scientiflc literature proposes a lot of solutions for detecting sub-superflcial defects, an open problem is related to the geometrical complexity of the structure and the relevant di-culty of crack detection. In this paper, we propose a Finite Element Method based approach for modelling a fast and accurate evaluation of the defect in railways tracks. The modelled system is strongly versatile and the choice of electrical parameters afiect the design of new probes for this kind of inspection. In particular, we propose a solution exploiting a rotating electromagnetic fleld with very encouraging results: The proposed model is able to recognize deep and surface cracks even if their orientations is vertical to the longitudinal direction of the sensor.

Advances in Signal Processing to Reduce Lift-off Noise in Eddy Current Tests

Nowadays, Non Destructive Testing and Evaluation (NDT/NDE) are frequently used to evaluate integrity of manufactured articles in civil or industrial applications. In this framework, Eddy Current Test (ECT) technique has a primary role for inspection of conducting materials. Solution of many related inverse problems requires an accurate comprehension of mea- sured data: it is one of the most challenging problem in this context. Thats why a suitable signal pre-processing is necessary. In this paper, advanced signal processing techniques are evaluated in order to reduce the impact of lift-ofi efiect on the eddy current data. Some hybrid approaches are also depicted, with encouraging results. DOI: 10.2529/PIERS061007215011

The GPR Technology on the Seisimic Damageability Assessment of Reinforced Concrete Building

Use of Georadar methodologies and techniques has a big relevance in scientific environment, due to its potential in various fields of application. The aim of this paper is to show operational methodologies as obtained result of developing an application of radar techniques for: − Civil Engineering applications, with special regard to determining the internal morphology, the lack of homogeneity, defectiveness, and the location of the steel reinforcements within concrete; − Preventive characterization of soils and contextual mapping of subsoil, with a particular reference to structural safety. The test area is in southern part of Reggio Calabria, Italy. The system (RIS/S system) is composed of an acquisition apparatus and an computation system , seems to give good planimetric and 3D results in terms of objetct localization and positioning.