CRTI Library

Robust fuzzy c-means clustering algorithmusing non-parametric Bayesian estimation in wavelet transform domain for noisy MR brain image segmentation

The major drawback of the fuzzy c-means (FCM) algorithm is its sensitivity to noise. The authors propose a new extended FCM algorithm based a non-parametric Bayesian estimation in the wavelet transform domain for segmenting noisy MR brain images. They use the Bayesian estimator to process the noisy wavelet coefficients. Before segmentation based on FCM algorithm, they use an a priori statistical model adapted to the modelisation of the wavelet coefficients of a noisy image.The main objective of this wavelet-based Bayesian statistical estimation is to recover a good quality image, from a noisy imageof poor quality. Experimental results on simulated and real magnetic resonance imaging brain images show that their proposed method solves the problem of sensitivity to noise and offers a very good performance that out performs some FCM-based algorithms. Voir les détails

Mots clés : fuzzy C-means algorithm, Non-Parametric Bayesian Estimation, Wavelet transform, image segmentation, MR Brain Images

A Bayesian Mumford–Shah Model for Radiography ImageSegmentation

This paper investigates the segmentation of radiographic images using a level set method based on a BayesianMumford–Shahmodel. The objective is to separate regions in an image that have very close arithmetic means, where a model based on thestatistical mean is not effective. Experimental results show that the proposed model can successfully separate such regions,in both synthetic images and real radiography images. Voir les détails

Mots clés : Level set

Rolling Bearing Fault Diagnosis Based on an Improved Denoising Method Using the Complete Ensemble Empirical Mode Decomposition and the Optimized Thresholding Operation

Vibration signals are widely used in monitoring and diagnosing of rolling bearing faults. These signals are usually noisy and masked by other sources, which may therefore result in loss of information about the faults. This paper proposes an improved denoising method in order to enhance the sensitivity of kurtosis and the envelope spectrum for early detection of rolling bearing faults. The proposed method is based on a complete ensemble empirical mode decomposition with an adaptive noise (CEEMDAN) associated with an optimized thresholding operation. First, the CEEMDAN is applied to the vibration signals to obtain a series of functions called the intrinsic mode functions (IMFs). Second, an approach based on the energy content of each mode and the white noise characteristic is proposed to determine the trip point in order to select the relevant modes. By comparing the average energy of all the unselected IMFs with the energy of each selected IMFs, the singular IMFs are selected. Third, an optimized thresholding operation is applied to the singular IMFs. Finally, the kurtosis and the envelope spectrum are used to test the effectiveness of the proposed method. Different experimental data of the Case Western Reserve University Bearing Data Center are used to validate the effectiveness of the proposed method. The obtained experimental results illustrate well the merits of the proposed method for the diagnosis and detection of rolling bearing faults compared to those of the conventional method. Voir les détails

Mots clés : Vibration analysis, bearing Fault diagnosis, CEEMDAN, Denoising, thresholding operation, envelope, Kurtosis

Enhancement of rolling bearing fault diagnosis based on improvement of empirical mode decomposition denoising method

Signal processing is a widely used tool in the field of monitoring and diagnosis of rolling bearing faults. The vibration signals of rolling bearing contain important information which can be used for early detection and diagnosis of faults. These signals are usually noisy and masked by other sources and therefore the information about the fault can be lost. In this work, we propose an enhancement of rolling bearing fault diagnosis based on the improvement of empirical mode decomposition (EMD) denoising method. This method is made to extract the useful fault signal in order to use the detection indicators such as the kurtosis and the envelope spectrum. Firstly, EMD is applied to the vibration signals to obtain a series of functions called the intrinsic mode functions (IMFs). Secondly, we present an approach based on the energy content of each mode to determine the trip point which allows selecting the relevant modes. The singular selected IMFs are determined by comparing the average energy of all the unselected IMFs with the energy of each selected IMFs; then, an optimized thresholding operation is performed to denoise these IMFs. Finally, the kurtosis and spectral envelope analysis were investigated for early detection and localization of the fault position. Different experimental data are used to validate the effectiveness of the proposed method. The obtained results showed that the proposed method is more efficient and more sensitive to the early detection and diagnosis of rolling bearing faults than the conventional denoising method. Voir les détails

Mots clés : Vibration analysis, bearing Fault diagnosis, EMD, threshold Denoising, energy, Relevant mode selection, envelope, Kurtosis

Rolling bearing fault diagnosis based on improved complete ensemble empirical mode of decomposition with adaptive noise combined with minimum entropy-deconvolution

The vibration signals provide useful information about the state of rolling bearing and the diagnosis of the faults requires an accurate analysis of these signals. Several methods have been developed for diagnosing rolling bearing faults by vibration signal analysis. In this paper, we present an improvement of the technique Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), this technique is combined with the Minimum Entropy Deconvolution (MED) and the correlation coefficient to diagnose defects. First, the vibration signal was decomposed by the improved CEEMDAN decomposition into several oscillatory modes called Intrinsic Mode Function (IMF). After calculation of the correlation coefficients between the original signal and their IMFs, the modes with higher coefficients are selected as the relevant modes. Secondly, the MED technique is applied to the selected modes in order to improve the sensitivity of the scalar and frequency indicators of faults detection. Finally, kurtosis and envelope analysis are used to detect and locate the defect position. The simulation is carried out using the Case Western University data base and the results obtained show that the proposed method provides very good results for the early detection and diagnosis of defects and can efficiently extract the defective characteristics of the rolling bearing. Voir les détails

Mots clés : vibration signal, rolling bearing fault, complementary ensemble empirical mode decomposition, coefficient correlation, minimum entropy deconvolution, Kurtosis, Envelope analysis

Modeling and Simulation of Biaxial Strained P-MOSFETs: Application to a Single and Dual Channel Heterostructure

The objectives of this work are focused on the application of strained silicon on MOSFET transistor. To do this, the impact and benefits obtained with the use of strained silicon technology on p-channel MOSFETs are presented. This research attempt to create conventional and two-strained silicon MOSFETsfabricated from the use of TCAD, which is a simulation tool from Silvaco. In our research, two-dimensional simulation of conventional MOSFET, biaxial strained PMOSFET, and dual-channel strained P-MOSFET has been achieved to extract their characteristics. ATHENA and ATLAS have been used to simulate the process and validate the electronic characteristics. Our results allow showing improvements obtained by comparing the three structures and their characteristics. The maximum of carrier mobility improvement is achieved with the percentage of 35.29 % and 70.59 % respectively, by result an improvement in drive current with the percentage of 36.54 % and 236.71 %, and reduction of leakage current with the percentage of 59.45 % and 82.75 %, the threshold voltage is also enhanced with the percentage of 60 % and 61.4%. Our simulation results highlight the importance of incorporating strain technology MOSFET transistors. Voir les détails

Mots clés : Biaxial strain, CMOS technology, SILVACO-TCAD, Strained silicon layers

Particle characterization by ultrasound using artificial intelligence methods

This thesis presents a study on how microemboli problems can be detected and characterized. It investigates a novel approach to the detection and classification of microemboli using a combination of data mining techniques, signal processing methods, and Radio Frequency information extracted from gaseous and solid emboli instead of the traditionally used Doppler signals processing. Embolic phenomena, whether air or particulate emboli which are particles larger than blood cells, could occlude blood vessels and consequently prevent the normal blood flow to vital organs and surrounding tissue. As a result, it can induce immediate damages like heart attack or ischemic stroke. It is believed that detecting the emboli in early stage could prevent or reduce the associated risks of embolism. Embolus composition (gaseous or particulate matter) is vital in predicting clinically significant complications. Unfortunately, embolus detection using Doppler methods have shown their limits to differentiate solid and gaseous embolus. Radio Frequency (RF) ultrasound signals backscattered by the emboli contain additional information on the embolus in comparison to the traditionally used Doppler signals. Gaseous bubbles show a nonlinear behavior under specific conditions of the ultrasound excitation wave, this nonlinear behavior is exploited to differentiate solid from gaseous microemboli. In order to verify the usefulness of RF ultrasound signal processing in the detection and classification of microemboli, an in vitro set-up is developed at the University of François Rabelais Tours, France in the INSERM U930 laboratory under the direction of Professor A. Bouakaz. Sonovue micro bubbles are exploited to mimic the acoustic behavior of gaseous emboli. They are injected in a nonrecirculating flow phantom containing a tube of 0.8 mm in diameter. The tissue mimicking material surrounding the tube is chosen to imitate the acoustic behavior of solid emboli. Both gaseous and solid emboli are imaged using an Anthares ultrasound scanner with a probe emitting at a transmit frequency of 1.82 MHz and at two mechanical indices (MI) 0.2 and 0.6. Therefore, we acquire four datasets, each dataset consists of 102 samples (51 gaseous emboli and 51 solid emboli). This dataset is exploited to create a number of discriminative features used for the detection and classification of circulating microemboli.First, we employ Fast Fourier Transform approach based on neural network analysis using fundamental and second harmonic components information contained in the RF signal backscattered by an embolus. The proposed approach allows the classification of microemboli with a discrimination rate of 92.85%.Second, we exploit a discrete wavelet transform approach using three dimensionality reduction algorithms; Differential Evolution technique, Fisher Score method, and Principal Component Analysis based on Support Vector Machines in the analysis and the characterization of the backscattered RF ultrasound signals from the emboli. Furthermore, we propose a strategy to select the suitable wavelet filter among 59 mother wavelet functions. The experimental results, based on the selected wavelet function and differential evolution algorithm, show clearly that discrete wavelet transform method achieves better average classification rates (96.42%) compared to the results obtained in the previous method using FFT based approach. The obtained results demonstrated that Radio Frequency ultrasound signals bring real opportunities for microemboli detection and characterization. Voir les détails

Mots clés : Microemboli, classification, Radio Frequency Ultrasound Signals

RESOLUTION DES EQUATIONS D’ETAT LINEAIRES D’ORDRE FRACTIONNAIRE

Dans ce travail, la résolution de l’équation d’état fractionnaire dmx(t)/dtm= Ax(t)+Be(t) , 0 < m< 1, représentant les systèmes linéaires fractionnaires d’ordre commensurable, pour tous les cas de figure des valeurs propres de la matrice d’état A et l’ordre de différentiation m a été proposé. Les expressions explicites des solutions homogènes et non homogènes de cette équation d’état fractionnaire ont été développées. Pour différentes valeurs propres de la matrice d’état A et l’ordre m, les solutions obtenues sont des combinaisons linéaires de fonctions fondamentales fractionnaires appropriées dont les transformées de Laplace sont des fonctions irrationnelles. Les approximations de ces fonctions irrationnelles par des fonctions rationnelles ont été obtenues pour que les solutions de l’équation d’état fractionnaire soient des combinaisons linéaires de fonctions exponentielles, cosinus, sinus, cosinus amorti et sinus amorti classiques. Des exemples illustratifs pour tous les cas de figure des valeurs propres de la matrice d’état A et l’ordre m ont été présentés et les résultats obtenus ont été très satisfaisants. Voir les détails

Mots clés : Décomposition modale

Génération d’un réseau sur puce au format VHDL RTL à partir d’unemodélisation de haut niveau UML par raffinement

Dans le passé, les systèmes embarqués et numériques ont été confinés surtout aux systèmesinformatiques. Aujourd'hui, ces systèmes sont appliqués dans un grand nombre de domaines etd’appareils tels que les télévisions numériques, les systèmes de communication, les radars, lessystèmes militaires et les instrumentations médicales. L’un des plus grands challenges au niveaude la conception de ces systèmes est l’interconnexion entre ses différents modules. Les réseauxsur puce (NoC) constituent un nouveau paradigme d’interconnexion pour les systèmes sur puce(SoC). Ils ont été proposés comme une solution prometteuse pour résoudre les problèmesrencontrés au niveau des interconnexions classiques.L’augmentation de la taille du réseau provoque plusieurs inconvénients, comme laréduction au niveau de la bande passante et la fréquence de fonctionnement et une augmentationau niveau de la latence et la consommation de l’énergie. Dans le présent document, nousprésenterons une nouvelle approche appliquée pour les réseaux sur puce (topologie Mesh 2D)afin de résoudre les problèmes rencontrés dans les architectures classiques. Cette approche estbasée sur une combinaison entre une stratégie de placement des modules, un routage XY à deuxniveaux et une technique de clustering basée sur la charge de communication entre les modules.Afin d’accélérer la conception de cette structure, nous avons utilisé une approche despécification orientées modèles à base de l’Ingénierie Dirigée par les Modèles (IDM). Nousavons utilisé le paquetage RSM pour modéliser la topologie Mesh 2D à base de cluster et lepaquetage à machine d’état ou encore le paquetage d’activité pour la modélisation de l'algorithmede routage XY à deux niveau (intra-cluster et inter-cluster). Voir les détails

Mots clés : Réseaux sur puce, Algorithme de routage dynamique, Clustering, Topologie Mesh, Systèmes sur puce

Eccentricity Fault Diagnosis based on Wavelet Transform and Neuro-Fuzzy Inference System in Doubly-fed Induction Generator

he development of wind turbine system is becoming very influential, in conditions of power qualityand very interesting for ecological protection. However, their potential is considerable in the world, thewind energy sources have drawn more and more attention all over the world recent years to improve theserious environment problems and deal with the shortage of fossil fuels in recent years [1]. The doubly-fedinduction generator (DFIG) is one of essential part of wind turbine system and has dominated in the field ofelectromechanical energy conversion system because of robustness and low cost [2]. So, for a substantialprofit, the diagnosis should be properly developed to ensure a production system more make safe.Production systems must be provided with reliable protection systems as any failure can lead to inevitabledamage [3].The occurrence of different faults can be completely in damage this machine type and inevitably cause theprocess to stop, resulting in loss of production consequently [4]. Therefore, it is necessary to develop amodel machine allow to detect the presence of the faults.Wind turbine is prone many failures and because of their size and localization, it is very costly to repair oremplace their component. In generally, mechanical faults are the most encountered in wind turbinessystems at the gearbox. These faults can occur at the level of ball, inner and outer race bearings, andflanges of the machine shaft. In scientific research tasks shows that rotor faults are more frequentbreakdowns, [5, 6]. In this paper we are interested to study the rotor eccentricity faults types [7].The DFIG in this type of faults can be subjected to counteract between the center of rotation of the shaftand the center of the rotor resulting the oscillations in the electromagnetic torque, uneven distribution of thecurrents in the rotor and the unbalance of stator current. This phenomenon is called static or dynamiceccentricity, and both at the same time creates the fault mixed eccentricity, whose origin may be related toincorrect positioning of the bearings during assembly or bearing failure [8, 9].Eccentricity Fault Diagnosis based on WaveletTransform and Neuro-Fuzzy Inference System inDoubly-fed Induction GeneratorMerabet. HichemResearch Center in Industrial Technologies (CRTI) P.O.Box 64, Cheraga, Algeria.h.merabet@csc.dzBahi. TaharElectrical Department, University of Annaba, Algeriatbahi@hotmail.comBedoud. khouloudResearch Center in Industrial Technologies (CRTI) P.O.Box 64, Cheraga, Algeria.k.bedoud@csc.dzDrici. DjalelResearch Center in Industrial Technologies (CRTI)P.O. Box 64, Cheraga, Algeria.d.drissi@csc.dzTSeveral methods of diagnosis are based on spectral analysis of the electromagnetic quantities, using themagnetic flux, stator current and the neutral voltage vibration signal analysis and especially the statorcurrent which requires only a current sensor [10, 11]. Therefore most of the recent research has bennedirected towards non-invasive techniques such as stator current and vibration signal analysis, motorsignature analysis with wavelet transform, courant envelope, Artificial Intelligence such as NeuralNetwork, Fuzzy Logic and Fuzzy Neural Network. The analysis of the stator currents in the waveletdomain remains the most commonly used because the spectrum results contains a source of information onthe majority of electrical and/or mechanical faults and magnetic properties can appear in the machine [12,13].The artificial intelligences based on fuzzy logic system inference, artificial neural network (ANN) orcombined structure techniques of artificial neural fuzzy interference system (ANFIS) are widely used in thenew monitoring[14, 15].Therefore, in order to increase the efficiency and the reliability of the monitoring in the field of the(DFIG) supervision, the proposed technique is based on wavelet transform and Neuro-Fuzzy inferencesystem (ANFIS).In this paper, the investment interest in wind turbine conversion system based on DFIG is presented.Then, we focus on the study of their designs and the development of a global model for doubly-fedgenerator in case of rotor eccentricity faults.Finally, in order to validate the considered method, the proposed model has been simulated and validatedby numerical simulations using MATLAB/Simulink. Voir les détails

Mots clés : eccentricity, fault, diagnosis, wavelet, ANFIS, DFIG