Electronique

Nombre total de résultats :519
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ANALYSE, REGLAGE ET CONCEPTION DES CORRECTEURS PIλDμ D’ORDRE FRACTIONNAIRE POUR LA COMMANDE DES SYSTEMES

GHERFI Kaddour (2019)
Thèse de doctorat

Récemment, les opérateurs et les systèmes d’ordre fractionnaire ont investi tous les domaines de la commande classique par leur utilisation dans la conception de correcteurs et d’algorithmes pour la commande des systèmes dynamiques. Malgré l’introduction de beaucoup de correcteurs et d’algorithmes d’ordre fractionnaire, un travail de recherche continu et intensif pour le développement de nouvelles techniques de commande d’ordre fractionnaire est toujours en cours pour le rehaussement et l’amélioration de la qualité des performances et de la robustesse des systèmes de commande. Dans ce travail, des méthodes de conception de correcteurs fractionnaires en utilisant les opérateurs et les systèmes fractionnaires ont été développées pour des systèmes de commande à retour unitaire dont les processus sont représentés par un modèle du premier ordre avec retard afin d’améliorer la qualité de ses performances caractéristiques et sa robustesse. Des exemples illustratifs ont été présentés pour valider l’efficacité et la flexibilité des méthodes proposées dans la conception des correcteurs fractionnaires. Les résultats de simulation obtenus ont été comparés à ceux obtenus en utilisant des correcteurs classiques et d'ordre fractionnaires pour les mêmes systèmes pour montrer l'amélioration des performances caractéristiques et de la robustesse apportées par les correcteurs fractionnaires proposés. Voir les détails

Mots clés :

Asymmetric Generalized Gaussian Distribution Parameters Estimation based on Maximum Likelihood, Moments and Entropy

Nafaa Nacereddine, Aicha Baya Goumeidane  (2019)
Article de conférence

In this paper, we address the problem of estimating the parameters of Asymmetric Generalized Gaussian Distribution (AGGD) using three estimation methods, namely, Maximum Likelihood Estimation (MLE), Moment Matching Estimation (MME) and Entropy Matching Estimation (EME). For this purpose, these methods are applied on an unimodal histogram fitting of an image corrupted with AGGD noise. Experiments show that the effectiveness of each method comparatively to the other one depends on the variation range of the shape factor. Voir les détails

Mots clés : Asymmetric generalized Gaussian distribution, Parameter estimation, maximum likelihood, Moments, Entropy

Fast Adapting Mixture Parameters Schemes for Probability Density Difference-Based Deformable Model

Aicha Baya Goumeidane, Nafaa Nacereddine  (2019)
Publication

This paper presents a new region-driven active contour using the pdf difference to evolve. The pdf estimation is done via a new and fast Gaussian mixture model (GMM) parameters updating scheme. The experiments performed on synthetic and X-ray images have shown not only an accurate contour delineation but also outstanding performance in terms of execution speed compared to the GMM estimation based on EM algorithm and to non-parametric pdf estimations. Voir les détails

Mots clés : Active contour, Adaptive mixture, GMM parameters update

Unsupervised weld defect classification in radiographic images using multivariate generalized Gaussian mixture model with exact computation of mean and shape parameters

Nafaa Nacereddine, Aicha Baya Goumeidane, Djemel Ziou  (2019)
Publication

In industry, the welding inspection is considered as a mandatory stage in the process of quality assurance/quality control. This inspection should satisfy the requirements of the standards and codes governing the manufacturing process in order to prevent unfair harm to the industrial plant in construction. For this purpose, in this paper, a software specially conceived for computer-aided diagnosis in weld radiographic testing is presented, where a succession of operations of preprocessing, image segmentation, feature extraction and finally defects classification is carried out on radiographic images. The last operation which is the main contribution in this paper consists in an unsupervised classifier based on a finite mixture model using the multivariate generalized Gaussian distribution (MGGD). This classifier is newly applied on a dataset of weld defect radiographic images. The parameters of the nonzero-mean MGGD-based mixture model are estimated using the Expectation-Maximization algorithm where, exact computations of mean and shape parameters are originally provided. The weld defect database represent four weld defect types (crack, lack of penetration, porosity and solid inclusion) which are indexed by a shape geometric descriptor composed of geometric measures. An outstanding performance of the proposed mixture model, compared to the one using the multivariate Gaussian distribution, is shown, where the classification rate is improved by 3.2% for the whole database, to reach more than 96%. The efficiency of the proposed classifier is mainly due to the flexible fitting of the input data, thanks to the MGGD shape parameter. Voir les détails

Mots clés : Mixture model, Multivariate GGD, radiography, weld defect, classification

Real-Time Switches Fault Diagnosis for Voltage Source Inverter Driven Induction Motor Drive

H. MERABET, T. BAHI, K. Bedoud, D. DRICI  (2019)
Publication

Induction machine is the frequently used for electrical drive applications in almost many industrial processes due to its simple and robust construction. Speed control of induction machine is required depending on the type of application. Speed of the induction motor can be varied by varying frequency or by variation of the terminal voltage. Variable voltage can be fed to induction machine using the voltage source inverter which is found efficient technique of controlling induction motor drive. The potential faults that occur in inverter are the open and short circuit switch fault. The cost of this schedule can be high, and this justifies the development of fault diagnostic methods. In this paper we present a reliable strategy for diagnosis and detection of open and short circuit switch faults in plush width modulation of voltage source inverter (PWM-VSI) using the fuzzy logic approach. The principle of the proposed approach is based on the acquisition of stator currents, to calculate the average absolute values of currents (AAVC), which allows the real-time detection and localization of inverter IGBT open or short-circuit faults using just the motor phase currents. A model of the system is built using MATLAB/SIMULINK. Simulation results are presented showing the monitoring approach performance under distinct operating conditions. Voir les détails

Mots clés : open circuit fault, short circuit fault, Fuzzy logic, modeling, simulation

Propriétés Optique de TiO2 et Application de la Méthode de Swanepoel pour la Détermination de l’Épaisseur Optique et de l’Indice de Réfraction

K. Bedoud, H. MERABET  (2019)
Publication

Dans ce travail, des nano-films de dioxyde de titane « TiO2 » ont été déposés par pulvérisation cathodique en utilisant une cible en céramique de Ti pur de 3" de diamètre et 0,250" d'épaisseur avec une pureté de 99,99% sur des substrats en verre à des épaisseurs (e) différents. Nous visons par ce travail d’étudier l’effet de l’épaisseur sur les propriétés optiques de TiO2 nano films. Pour cela, nous avons utilisé la spectroscopie de transmittance optique UV-Visible pour la caractérisation optique. La variation du gap optique des films est inversement proportionnelle à la variation de l’épaisseur de 3,6 eV à 3,8 eV, respectivement. Pour la détermination de l'indice de réfraction et l'épaisseur du film nous avons utilisé la méthode proposée par Swanepoel, qui s’articule sur l’utilisation des franges d’interférence. On observe que, l'indice de réfraction n augmente avec l’augmentation de l'épaisseur de la couche déposée. Voir les détails

Mots clés : couches minces, pulvérisation, semi-conducteur, TiO2, nano-films, UV-Vis, épaisseur, indice de réfraction, Swanepoel

Study of Optical and Morphological TiO2 Nano-Films Properties Deposited by MagnetronSputtering on Glass Substrate

K. Bedoud, H. MERABET, L. Alimi  (2019)
Publication

In this paper, TiO2 nano-films were deposited by RF magnetron sputtering using a TiO2ceramic target of pureTi of 3" diameter and 0.250" thickness with a purity of 99.99%, onto heated glass substrates in a temperature range of200 to 450°C. This study determines the temperature effect on the structural, optical and morphological properties ofTiO2 nano-films. For this, we used X-ray diffraction for structural characterization and optical transmission spectroscopyUV-Visible for optical characterization and atomic force microscopy (AFM) for morphological characterization of thefilms produced. The (101), (400), (112), (200), (105), (211), (213), (204) peaks of the anatase structure and the (210),(102), (-112) (710) peaks of the monoclinic structure are observed. In addition, the peaks are sharp and intense whichimplies a good crystalline structure. Otherwise, the films optical gap variation is proportional to the temperature variationfrom 3,9eV to 3,92 eV for T=200°C and T=450°C, respectively. The surface roughness of TiO2 nano-films range from1,031nm to 4,665nm. Voir les détails

Mots clés : Thin films, sputtering, semiconductor, TiO2 nano-films, gas sensors, nano-films, RF magnetron sputtering, DRX, UV-Vis, AFM.

Tomographic Image Reconstruction in the Case of Limited Number of X-Ray Projections Using Sinogram Inpainting

a.allag, a.benammar, R.Drai, T.BOUTKEDJIRT  (2019)
Publication

In many medicine and industry applications, a precise X-ray tomography reconstruction of the internal objects structure is of great importance for reliable interpretation data. The tomography allows obtaining a spatial distribution of the internal materials structure. In certain experiments conditions, the projection data acquisition is guided by angle limitations or a restricted angle, this requires a subsampling of the projections number or a partial data absence. Accordingly, the reconstructed images may suffer from severe artefacts especially with the presence of noise. In this context, the purpose of this paper is to propose a tomographic image reconstruction method based on FBP associated to sinogram inpainting. The studied inpainting technique is based on first order variational methods such as the Chambolle-Pock algorithm. This method allows the quality improvement of the reconstruction images tomographic with reduced number of projection. The PSNR is improved by 7 to 10 dB in the reconstructed image compared to the classical FBP reconstruction. Voir les détails

Mots clés : x-ray tomographic, image reconstruction

Segmentation of Weld Defects Using Multiphase Level Set by the Piecewise-Smooth Mumford-Shah Model

N.RAMOU  (2019)
Publication

This paper deals with the problem of the X-ray image segmentation used to detect weld defects for a non-destructive testing task. In this work we have implemented a multiphase method using the Mumford and Shah piecewise smooth model to extract the size and the texture information of defect and its region. Using this model which is more robust to the noise and less sensitive to the position of the initial curve, we have obtained a maximum of information for several regions in the X-ray image (the geometry of weld beads and defects). Our results show that the model developed can do at the same time the image segmentation and restoration. Voir les détails

Mots clés : Level set

A novel correlation filter based on variational calculus

Djemel Ziou, Dayron Rizo Rodriguez, Nafaa Nacereddine, Salvatore Tabbone  (2019)
Publication

Correlation filters have been a popular technique for tackling image classification problems. The traditionalcriteria used to design correlation filters overlook some properties that can improve their discriminative power.Therefore, new criteria are proposed to design a novel correlation filter. Such criteria take advantage ofnegative samples, spatial information and the smoothness of the correlation output space. A closed formis derived from the criteria proposed using variational calculus. Moreover, it is shown that the resultingcorrelation filter is a bandpass filter. Experiments are conducted for face identification under illuminationvariation for a single training image per subject and head pose classification. The correlation filter proposeddelivers favorable scores when compared to other correlation filters and state-of-the-art approaches Voir les détails

Mots clés : Correlation filter, Variational calculus, Face identification, Illumination variation, Single training image, Pose classification