Liste des communications
Optimization of AZO/ZnO/Cu2O Thin Film Heterojunction Solar Cell with Gaussian Defect
In the present article, we report on the simulation study of defected n+-n-p heterojunction metal oxide (MO) thin film solar cell. In the structure, the natural p-type cuprous oxide (p-Cu2O) thin film as an absorber layer is conducted with the natural n-type zinc oxide (n-ZnO) thin film as a buffer layer and a transparent conducting aluminum-doped zinc oxide (n+-AZO) thin film at the front of the n-ZnO buffer layer to verify the function of the window layer. The update xwAMPS version of AMPS one-dimensional simulator has been used to optimize the feasibility of n+-AZO/n-ZnO/p-Cu2O solar cell under air mass AM1.5 illuminations and 300K of temperature. The impact of the Cu2O absorber layer thickness in the n+-n-p heterojunction MO solar cell is investigated and hence, the performance of the n+-AZO/n-ZnO/p-Cu2O structure with gaussian defect is optimized. Voir les détails
Mots clés : Cu2O, ZnO, AZO, Gaussian defect, heterojunction, J-V data, wxAMPS
A survey on deep learning-based object detectionalgorithms for drones
Unmanned Aerial Vehicles (UAVs) are being used in a very large number of applications. Building an intelligent UAVis a very exciting and challenging topic. Recently, deep learning and computer vision are highly used for the purpose of realizing a fully-autonomous drone that does not need human intervention. Computer vision is a field focused on enabling drones to interpret and understand the content of an image or a video using Convolutional Neural Networks (CNNs). This paper focuses on reviewing recent deep learning-based object detection algorithms used for UAVs. We will discuss the most important research papers and techniques which helped improve the object detection state-of-the-art for drones. Finally, we will conclude this reviewing with a description of the main challenges for the application of deep learning for drone-based solutions. Voir les détails
Mots clés : Computer vision, UAV, Deep Learning, Object Detection, Convolutional Neural Network
Lightweight CNNs-Based Object Detection forEmbedded Systems implementation
Deep Learning algorithms, based on the implementation of Convolutional Neural Networks (CNN), are more and more used in Artificial Intelligence (AI) applications, especially in the image recognition field, like image classification, object detection, segmentation. These algorithms learn from training data a set of parameters to create a model, which is capable of performing a classification task with high accuracy. The most recent models consist of millions of parameters, which make it computationally very exhausting, especially in the field of embedded systems where resources are very limited. Recently, deep learning and computer vision are highly used to realize a fully-autonomous drone and self-driving cars, which does not need human intervention. Computer vision is a field focused on enabling drones to interpret and understand the content of an image or a video using CNNs. This paper focuses on reviewing recent lightweight CNNs architectures used that can be implemented on embedded targets. Voir les détails
Mots clés : Computer vision, Deep Learning, Object Detection, Convolutional Neural Network, lightweight CNN
A DC/DC Buck Converter Voltage Regulation UsingAn Adaptive Fuzzy Fast Terminal Synergetic Control
In this paper, an adaptive fuzzy fast terminalsynergetic voltage regulation for DC/DC buck converter isdesigned based on recently developed synergetic theory and aterminal attractor method. The advantages of presentedsynergetic control include the characteristics of finite timeconvergence, insensitive to parameters variation and chatteringfree phenomena. Rendering the design more robust, fuzzy logicsystems are used to approximate the unknown parameters in theproposed controller without calling upon usual modellinearization and simplifications. Taking the DC/DC buckconverter in continuous conduction mode as an example, thealgorithm of proposed synergetic control is analyzed in detail. Allthe simulation results demonstrate the effectiveness and the highdynamic capability of the proposed AF-FTSC control techniqueover the FTSC strategy Voir les détails
Mots clés : synergetic control, fuzzy logic system, terminal technique, finite time convergence, DC/DC buck converter
Real Time Implementation of Grid Connected Wind Energy Systems: Predictive Current Controller
This work, suggests a new control strategy usingFinite-Control-Set Model-Predictive-Control (FCS-MPC) for thecontrol of a wind turbine system (WTS) based on PermanentMagnet Synchronous Generator (PMSG). The consideredcontroller is separated on two parts: FCS-MPC-based on thecurrent control loop for the single switch mode rectifier tooptimally release the maximum wind power, and FCS-MPCbased on the voltage control loop for the voltage source inverterto enhance the THD of grid currents. A wind energy systemprototyping platform was developed and accomplished in thelaboratory, and the experimental results are provided to verifythe performances of the considered FCS-MPC strategies. Voir les détails
Mots clés : Finite-Control-Set Model-Predictive-Control (FCSMPC), Permanent Magnet Synchronous Generator (PMSG), Wind Turbine System (WTS), Maximum Power Point Tracking (MPPT), Grid Connected, Experimental Results
Analytical Modeling of the Behavior of Composite Structure Subjected to Combined Tensile and Moment Loading
The present work is a contribution in assessment of the security factor of the structures made from composites materials in order to work within safe conditions. In the first step, a Matlab code is developed in order to develop a mechanical model able to predict the mechanical behavior of the high performance composite material without necessity to the expensive experimental test. In the second step, an analytical modeling is performed for the composite structure subjected to a tensile load in the longitudinal direction and a moment in the transversal direction in order to assess the strain and the stress fields in all layers of the composite material and evaluate the security factor in all plies. Meanwhile, the new mechanical model can be used as a tool decision in design and maintenance in order to check the integrity of the composite structure. Voir les détails
Mots clés : composite, laminate, integrity, security factor, moment loading
Effect of the c-axis tilting angle in piezoelectric ZnO crystal on the performances of electroacoustic SAW sensors
This paper aims to study the effect of c-axis tilting angle of piezoelectric ZnO/Si on the performances of electroacoustic SAW sensors, the dispersion curves of phase velocity, the electromechanical coupling factor K² and sensitivity to mass loading of Rayleigh and Sezawa modes are studied for different hZnO/λ and for different c-tilting angles (0, θ, 90°). The effect of the tilting angle θ on the performances of electroacoustic devices, is studied by finite element analysis. Based on the obtained results, SAW device is fabricated onto a ZnO/SiO2/Si multilayered structure. The obtained results show best performances and high sensitivity to gas and will contribute in enhancing the sensitivity and performances of SAW electroacoustic devices. Voir les détails
Mots clés : surface acoustic waves, electroacoustic devices, Finite Element Analysis, Piezoelectric materials, c-tilted ZnO
The Importance of Applying Artificial Intelligence on Unmanned Aerial Vehicle
Unmanned Aerial Vehicles (UAVs) are used in several applications and they are growing in popularity. Recent progress in unmanned aerial vehicles and artificial intelligence constitutes a new chance for an autonomous operation and flight. Nowadays, artificial intelligence and deep learning are driving the evolution of UAVs and fueling their autonomous future. Computer vision achieved very important progress in image classification and segmentation, and object detection, which make it very attractive research field when it is applied on unmanned aerial vehicle. Artificial intelligence is not only important and benefic, but can be rather, dangerous and serious matter since the UAVs learns through algorithms, and use that for future decision making. This work is a survey, where we present works, challenges and dangerous part of using artificial intelligence on UAVs. Voir les détails
Mots clés : UAV, machine learning, Artificial intelligence, System, Drone
Comparative study of the corrosion behavior of the high strength steel after thermal and thermomechanical treatments
The corrosion behaviour of X80 steel samples rolled and other quench-tempered in 3.5% NaCl have been studied. Optical microscopy, scanning electron microscopy, potentiodynamic polarization tests and electrochemical impedance measurements are the techniques used to characterize the samples. The results show that the tempered steel has a low corrosion current density compared to the rolled steel. The impedance measurements show the presence of a single capacitive loop attributed to the load transfer phenomenon. Voir les détails
Mots clés : HSLA grade steel X80, corrosion, structure, thermomechanical treatment.
Electronic Properties of Graphene
Winding a graphene sheet around itself creates periodic boundary conditions, perpendicular to the nanotube axis. Therefore a limited number of wave vectors are allowed in this direction. It depends on the diameter and the winding of the graphene sheet on itself [1]. If the edge conditions include the corners of the Brillouin zone, the behavior of the nanotube is metallic. This is the case for all “chair” type nanotubes and a third of “chiral” [2] and “zig-zag” nanotubes. In other cases, the band structure has a band gap, as a first approximation, inversely proportional. at the radius of the nanotube. These properties have been confirmed experimentally by measuring the tunnel current between the tip of an STM (Tunnel Effect Microscope) and a nanotube, which provides a direct estimate of electron density. In addition, STM makes it possible to image the atomic structure of nanotubes and therefore to determine their chirality and their diameter. The transport properties can thus be correlated with the structure of the nanotube. Metal nanotubes have only two one-dimensional conduction bands that cross the Fermi level: all current flows through these two bands and the theory predicts the conductance G0 = 2e2 / h, equal to twice the unit of fundamental conductance. Voir les détails
Mots clés : Graphene, STM, Nanotube