<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karim Ferroudji</style></author><author><style face="normal" font="default" size="100%">Outtas, Touffik</style></author><author><style face="normal" font="default" size="100%">Monkova, K</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modal Analysis of a Two Axis Photovoltaic Solar Tracker</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Artificial Intelligence in Renewable Energetic Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2021</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-92038-8_23</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">230-236</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	As compared to a fixed Photovoltaic (PV) system, a two axis solar tracker system can increase electrical energy production from 35% to 45% in a year. Vibration characteristic is an essential factor in evaluating the reliability and stability of solar tracker structure during operating course. In this paper, the free vibration behaviour (modal analysis) of 12&amp;nbsp;kW two axis PV solar tracker structure is investigated numerically. The modal analysis by using commercial finite element package (SOLIDWORKS SIMULATION) to identify the modal parameters of the tracker structure (natural frequencies and corresponding modal shapes). The simulation results obtained for tracker structure at maximum elevation angle (60deg) indicate that no resonant problem (according to ASHRAE Standard) during solar tracker operation under wind load (from 0 to 36&amp;nbsp;m/s).
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</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abdelghani Tafsast</style></author><author><style face="normal" font="default" size="100%">Karim Ferroudji</style></author><author><style face="normal" font="default" size="100%">Mohamed Laid Hadjili</style></author><author><style face="normal" font="default" size="100%">Ayache Bouakaz</style></author><author><style face="normal" font="default" size="100%">Nabil Benoudjit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic microemboli characterization using convolutional neural networks and radio frequency signals</style></title><secondary-title><style face="normal" font="default" size="100%">2018 International Conference on Communications and Electrical Engineering (ICCEE)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">1-4</style></pages><isbn><style face="normal" font="default" size="100%">1-72810-112-3</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Abdelghani Tafsast</style></author><author><style face="normal" font="default" size="100%">Karim Ferroudji</style></author><author><style face="normal" font="default" size="100%">Hadjijli, Mohamed Laid</style></author><author><style face="normal" font="default" size="100%">Ayache Bouakaz</style></author><author><style face="normal" font="default" size="100%">Nabil Benoudjit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic microemboli classification using convolutional neural networks and RF signals</style></title><secondary-title><style face="normal" font="default" size="100%"> International Conference on Communications and Electrical Engineering (ICCEE)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2018</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/abstract/document/8634521</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">El Oued, Algérie</style></pub-location><pages><style face="normal" font="default" size="100%">1-4</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;text-align: justify;&quot;&gt;
	Characterization of microembolic behavior, as solid or gaseous, guides to an efficient treatment protocol. In this study a new methodology to classify microembolic signals by Deep Convolutional Neural Networks (CNN) is implemented. The experimental system is made up of a flow phantom (ATSLaB) with a cylinder of 6 mm in width. Contrast agents composed of bubbles are employed in this investigational study to imitate the ultrasonic characteristics of gaseous emboli. A Doppler liquid which contains particles, have scatter proprieties analogous to red blood cells, is exploited to mimic the ultrasonic characteristics of the solid emboli. In order to optimize the CNN topology in the training phase, an adaptive learning Root Mean Square (RMSProp) algorithm is used. A classification rate of 99.9% is achieved in this experimental study. These results demonstrate that the CNN optimized model can be adequately exploited for microemboli classification using radio frequency (RF) signals compared to artificial neural networks (ANN) models.
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</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karim Ferroudji</style></author><author><style face="normal" font="default" size="100%">Nabil Benoudjit</style></author><author><style face="normal" font="default" size="100%">Ayache Bouakaz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Automated Microemboli Detection and Classification System using Backscatter RF Signals and Differential Evolution</style></title><secondary-title><style face="normal" font="default" size="100%">Australasian Physical &amp; Engineering Sciences in MedicineAustralasian Physical &amp; Engineering Sciences in Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">40</style></volume><pages><style face="normal" font="default" size="100%">85-99</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karim Ferroudji</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Particle characterization by ultrasound using artificial intelligence methods</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2017</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Doctorat</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fouzi Douak</style></author><author><style face="normal" font="default" size="100%">Abdelghani Tafsast</style></author><author><style face="normal" font="default" size="100%">Damien Fouan</style></author><author><style face="normal" font="default" size="100%">Karim Ferroudji</style></author><author><style face="normal" font="default" size="100%">Ayache Bouakaz</style></author><author><style face="normal" font="default" size="100%">Nabil Benoudjit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A wavelet optimization approach for microemboli classification using RF signals</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">1-4</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Wavelets are known particularly to be an effective tool for extracting discriminative features in the scattered RF signals of both solid and gaseous emboli. However, the selection of an appropriate mother wavelet for the signal being analyzed is an important criterion. This offers the possibility to perform an optimization procedure to obtain the best wavelet. The purpose of the study is to propose a new approach to classify microembolic echoes using a discrete wavelet transform (DWT) based on genetic algorithm optimization and support vector machine (SVM) classifier. The experimental setup consists of a flow phantom (ATSLaB) containing a tube of 6 mm in diameter. In order to mimic the ultrasonic behavior of gaseous emboli, contrast agents consisting of microbubbles are used in our experimental setup. However, to mimic the behavior of the solid emboli we have used the Doppler fluid which contains particles with scatter characteristics comparable to red blood cells. The acquisitions are carried out at 2 MHz and 3.5 MHz transmit frequency. Ultrasound waves are transmitted at different intensities corresponding to mechanical indices (MI) of 0.21 and 0.42 for the transmit frequency of 2 MHz, and 0.31 and 0.62 for the transmit frequency of 3.5 MHz. Two concentrations of the contrast agent (100 μl and 200 μl) are diluted into a 100 ml volume of water. The polyphase representation of the discrete wavelet transform (DWT) is exploited in this study. Such representation allows generating a wavelet filter bank from a set of angular parameters, in order to minimize the fitness function based on genetic algorithm optimization and the SVM classifier. The best accuracy classifications of microemboli obtained in this study are equal to 99.90% for 2MHz and to 99.60% for 3.5MHz. These results illustrate that wavelet optimization approach works well for microemboli classification using RF signals.</style></abstract><custom1><style face="normal" font="default" size="100%">Tours, France</style></custom1><custom3><style face="normal" font="default" size="100%">IEEE International Ultrasonics Symposium (IUS). September 18-21</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karim Ferroudji</style></author><author><style face="normal" font="default" size="100%">Nabil Benoudjit</style></author><author><style face="normal" font="default" size="100%">Medjili, Fayçal</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MODELLING OF SMART THIN FILM THERMAL-CONDUCTIVITY HUMIDITY SENSOR USING ANN</style></title><secondary-title><style face="normal" font="default" size="100%">1er Congrès National de Physique et Chimie Quantique CPCQ 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2015</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karim Ferroudji</style></author><author><style face="normal" font="default" size="100%">Nabil Benoudjit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Détection des particules par ultrason via l'intelligence artificielle: Application à la caractérisation des emboles</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Éditions universitaires européennes</style></publisher><isbn><style face="normal" font="default" size="100%">3-8417-3012-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karim Ferroudji</style></author><author><style face="normal" font="default" size="100%">Nabil Benoudjit</style></author><author><style face="normal" font="default" size="100%">Ayache Bouakaz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Empirical mode decomposition based support vector machines for microemboli classification</style></title><secondary-title><style face="normal" font="default" size="100%">2013 8th International Workshop on Systems, Signal Processing and their Applications (WoSSPA)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2013</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">84-88</style></pages><isbn><style face="normal" font="default" size="100%">1-4673-5540-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nabil Benoudjit</style></author><author><style face="normal" font="default" size="100%">Karim Ferroudji</style></author><author><style face="normal" font="default" size="100%">Bahaz, Mohamed</style></author><author><style face="normal" font="default" size="100%">Ayache Bouakaz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">In vitro microemboli classification using neural network models and RF signals</style></title><secondary-title><style face="normal" font="default" size="100%">UltrasonicsUltrasonics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">51</style></volume><pages><style face="normal" font="default" size="100%">247-252</style></pages><isbn><style face="normal" font="default" size="100%">0041-624X</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>