Liste des documents
Tribological investigation of carbon fiber-epoxy composite reinforced by metallic filler layer
This work aims to develop a carbon fiber/epoxy composite reinforced by metal fillers (Copper/Stainless steel) in order to improve the tribological properties. For this, a tribological study has been conducted using a ball-on-disc configuration. The surface of the material has been modified by deposing a layer of metal powder during manufacturing. For a better understanding of the wear mechanisms, the worn surface characteristics have been examined using a scanning electron microscope (SEM). The coefficient of friction and the wear rate under different normal loads have been determined for the filled and unfilled composite. The results obtained revealed an increase of the wear rate with the increase of the applied load. Metallic filled carbon–epoxy showed better wear resistance and friction behaviour under different loads. In fact, microhardness measurement showed that the surface hardness has been greatly influenced by the metal filler. The overall results illustrate the impact of metal powders in the modification of polymer matrix composites surfaces. This method is promising to improve the tribological properties. Voir les détails
Mots clés : Polymer matrix composites, Metallic fillers, wear, Coefficient of friction
A Survey on Lightweight CNN-Based Object Detection Algorithms for Platforms with Limited Computational Resources
Autonomous drones must be able to identify the existence of one or more objects of interest in a complex environment with high accuracy and speed to fly around safely. Most existing object detection techniques, based on traditional machine learning algorithms, can't offer acceptable performance in complicated environments. Deep Convolutional Neural Networks (CNNs) provide us such ability with high performance. Today, deep CNN-based object detection algorithms are more and more used in Artificial Intelligence (AI) applications. However, it still very difficult to deploy large CNNs architectures on small devices with limited hardware resources, because they consist of millions of parameters, which make them computationally very exhausting. Lightweight CNN architectures are proposed as a solution to make the deployment of deep neural networks on small devices feasible. This paper focuses on reviewing recent used lightweight CNN architectures that can be implemented on embedded targets to improve the object detection performance for small devices-based systems, like drones. We need to select fast and lightweight CNN models to use them on drone platforms. The purpose of this reviewing is to choose the most accurate and fastest algorithm to implement it on our drones. Voir les détails
Mots clés : Computer vision, Deep Learning, Object Detection, Convolutional Neural Network, lightweight CNN
IDENTIFICATION OF THERMAL AND MICROSTRUCTURAL PROPERTIES OF HOT ROLLING SCALE
A significant amount of scale is produced during casting of ingots and processing of hot-rolled products. In manufacturing steel, during the various rolling operations, the amount of scale produced is approximately 0.1% of the annual production of the rolling mills. The quality of the thin sheet during rolling is affected by the behavior of the iron oxide layers formed on their surfaces. For this reason, acids and oils are used for the descaling of slabs and billets by means of pressurized water. The calamine, contaminated by these various acids and used oils, is rejected and stored involuntarily on important areas and pollutes soil and groundwater. Micrographic observations as well as X-ray diffraction analysis have shown that calamine consists mainly of iron oxides. Hematite and magnetite become the main components for oxidation times greater than 1 hour. Characterization tests have shown that calamine is dense (ρ = 4.8 g/cm3), its particle size is variable depending on the degree of oxidation (from 0.5 to 10 mm). Simultaneous thermal analysis showed that an increase in mass of the calamine sample with a release of heat. Studies are underway for the physico-chemical characterization of the soils of the storage areas. Voir les détails
Mots clés : Scale, Risks, soil, thermal analysis, granulometry, X-ray diffraction
Manufacturing Nanostructured and Microstructured Chitosan Prepared By Milling Shrimp Shell
Nano-products are of great interest in the field of pharmaceutical, corrosion, medecine and engineering. dis research aimed to produce nano-chitosan. Nanocrystallite. Shrimp shells has been prepared by using a high-energy planetary ball with an optimal rotational speed.The raw material was subjected to standard chemical processing for chitin extraction, followed by deacetylation to obtain nanocrystallite chitosan, which is characterized by Scanning Electron Microscope SEM and Fourier Transform Infrared Spectrometry FT-IR. Voir les détails
Mots clés : Shrimp shell, Planetary milling, chitosan, SEM, FT-IR.
Manufacturing Nanostructured and Microstructured Chitosan Prepared By Milling Shrimp Shelll
Nano-products are of great interest in the field of pharmaceutical,corrosion, medecine and engineering.This research aimed to produce nano-chitosan. Nanocrystallite.Shrimp shells have been prepared by using a high-energy planetaryball with an optimal rotational speed.The raw material was subjected to standard chemical processing forchitin extraction, followed by deacetylation to obtain nanocrystallitechitosan, which is characterized by Scanning Electron MicroscopeSEM Fourier, Transform Infrared Spectrometry FT-IRand X-raydiffraction (XRD) Voir les détails
Mots clés : Shrimp shell, Planetary milling, chitosan, SEM, FT-IR, XRD
Magnetic and structural Behavior of Fe-CoO NanocompositesMechanically Milled
The Fe60(CoO)40 nanostructured alloys have been prepared from pure iron and cobaltoxide powders by mechanical alloying technique within a high energy planetary ball-mill.Morphology, microstructural and magnetic properties of this powder were investigated by a Scanning Electron Microscope (SEM), X-ray diffraction (XRD) and Vibrating sample magnetometer (VSM). The effect of time of milling on magnetic behaviour of Fe(CoO) nanostructured composite has been investigated. Apparition of new phase polycrystallinesample having a size in the range of 12 and 26 nm, it is confirmed by X-ray diffraction testing.The enhanced magnetic properties and structural behaviour of the nanoparticle are due by the diminution of size of crystallite. After 40 hours of milling, the appearance of spinel structureof CoFe2O4. The reduction in particle size leads to a significant increase in magnetic hardening, the coercive field at room temperature increases from 6 Oe to 208 Oe Voir les détails
Mots clés : FeCoO nanostructured, Magnetic Properties
Investigation of some physical properties of pure and Co-doped MoO3 synthesized on glass substrates by the spray pyrolysis method
Pristine and Cobalt (Co)-doped MoO3 nanofilms were synthesized on glass substrates using the spray pyrolysis method. The nanometric pristine MoO3 films were prepared from the 10−2 M.L-1 solution of ammonium molybdate tetrahydrate [(NH4)6Mo7O24,4H2O] in distilled water. Co-doping at 0.5, 0.75 and 1% was achieved by adding cobalt (II) chloride hexahydrate (Cl2CoH12O6) in the pristine solution. The structure and the morphology of the films were investigated by means of X-ray diffraction and atomic force microscopy: two pronounced (020) and (040) peaks corresponding to the orthorhombic structure phase of α-MoO3 were detected. The AFM observations revealed the formation of micro-plates, parallel to the surface plane, with a roughness ranging from 33?nm to 54?nm. Optical properties were investigated through reflectance, transmittance and photoluminescence measurements. The optical band gap, the Urbach energy and the refractive index were deduced from these measurements. The presence of oxygen vacancies was revealed from the interband transitions in the blue and green domains. Co-doped MoO3 nanofilms showed ferromagnetic behavior. The photocatalytic degradation of an aqueous solution of methylene blue (MB) under UV irradiation, in the presence of Co-MoO3 nanomfilms, has been carried out using UV–vis spectrometery: the intensity of the absorption peak recorded at 660?nm decreased with the increase of the UV-illumination time while the color of the initial MB solution was drastically waned. Voir les détails
Mots clés : Spray pyrolysis method, MoO3 nanofilms, optical properties, Magnetic Properties
A novel correlation filter based on variational calculus
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
Video Processing and Analysisfor Endoscopy-Based InternalPipeline Inspection
Because of the increasing requirements in regards to the pipeline transport regulations, the operators take care to the rigorous application of checking routines that ensure nonoccurrence of leaks and failures. In situ pipe inspection systems such as endoscopy, remains a reliable mean to diagnose possible abnormalities in the interior of a pipe such as corrosion. Through digital video processing, the acquired videos and images are analyzed and interpreted to detect the damaged and the risky pipeline areas. Thus, the objective of this work is to bring a powerful analysis tool for a rigorous pipeline inspection through the implementation of specific algorithms dedicated to this application for a precise delimitation of the defective zones and a reliable interpretation of the defect implicated, in spite of the drastic conditions inherent to the evolution of the endoscope inside the pipeline and the quality of the acquired images and videos. Voir les détails
Mots clés : video processing, endoscopy, Pipeline inspection
Unsupervised weld defect classification in radiographic images usingmultivariate generalized Gaussian mixture model with exactcomputation of mean and shape parameters
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 themanufacturing process in order to prevent unfair harm to the industrial plant in construction. For thispurpose, in this paper, a software specially conceived for computer-aided diagnosis in weld radiographictesting is presented, where a succession of operations of preprocessing, image segmentation, featureextraction andfinally defects classification is carried out on radiographic images. The last operationwhich is the main contribution in this paper consists in an unsupervised classifier based on afinitemixture model using the multivariate generalized Gaussian distribution (MGGD). This classifier is newlyapplied on a dataset of weld defect radiographic images. The parameters of the nonzero-mean MGGDbasedmixture model are estimated using the Expectation-Maximization algorithm where, exactcomputations of mean and shape parameters are originally provided. The weld defect database representfour weld defect types (crack, lack of penetration, porosity and solid inclusion) which are indexed by ashape geometric descriptor composed of geometric measures. An outstanding performance of theproposed 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%. Theefficiency of the proposed classifier is mainly due to theflexiblefitting of the input data, thanks to theMGGD shape parameter. Voir les détails
Mots clés : Mixture model, Multivariate GGD, radiography, weld defect, classification