This paper deals with the robust stabilization of infinite dimensional systems subjected to stochastic and deterministic perturbations. First, we give conditions providing the stability of the parameterized system. Then, we investigate the maximization of the stability radius by state feedback. We establish conditions for the existence of suboptimal controllers. Using these conditions we characterize the supreme achievable stability radius via an infinite dimensional Riccati equation.

Recently deep learning has shown significant achievement in the performance of many tasks, like natural language processing, image and speech recognition. Also, this improvement concerns multiple biometrics recognition systems. In this work, we focus on biometrics recognition, we present a stacked auto-encoder-based approach for various biometrics recognition, including Iris, Ear, palm-print, and face recognition. The proposed method allows training a neural network that includes two hidden layers for biometrics tasks. It runs in two steps, in the first one, each layer is trained individually in an unsupervised manner by auto-encoders, then the layers are stacked and trained in a supervised way. Experimental results on images, obtained from publicly available biometrics databases clearly demonstrate the benefit of using stacked auto-encoders as feature extraction and dimension reduction tools for biometrics recognition, as significant high accuracy rates are obtained over the four databases.

Recently deep learning has shown significant achievement in the performance of many tasks, like natural language processing, image and speech recognition. Also, this improvement concerns multiple biometrics recognition systems. In this work, we focus on biometrics recognition, we present a stacked auto-encoder-based approach for various biometrics recognition, including Iris, Ear, palm-print, and face recognition. The proposed method allows training a neural network that includes two hidden layers for biometrics tasks. It runs in two steps, in the first one, each layer is trained individually in an unsupervised manner by auto-encoders, then the layers are stacked and trained in a supervised way. Experimental results on images, obtained from publicly available biometrics databases clearly demonstrate the benefit of using stacked auto-encoders as feature extraction and dimension reduction tools for biometrics recognition, as significant high accuracy rates are obtained over the four databases.

In this work, we studied an L-shaped frequency selective surface (FSS) by a method called Wave Concept Iterative Procedure (WCIP), this method developed from the Modal Fast Transformation (FMT) is based on the cross- formulation. wave and the solution obtained by an iterative procedure does not use the matrix to ensure convergence and the procedure is stopped when it arrives at convergence, for this geometry the results of a single resonance obtained by the WCIP method have a resonant frequency of 5.35 GHz with a band bandwidth of 2.3 GHz, when the structure is excited in the X direction, a frequency at 10.35 GHz with a bandwidth of 0.44 GHz when the structure is excited in the Y direction. The simulation of the results obtained by the WCIP method is compared with the results of the software HFSS 13.0 (High Frequency Structure Simulator), we find a good agreement.

In this work, we studied an L-shaped frequency selective surface (FSS) by a method called Wave Concept Iterative Procedure (WCIP), this method developed from the Modal Fast Transformation (FMT) is based on the cross- formulation. wave and the solution obtained by an iterative procedure does not use the matrix to ensure convergence and the procedure is stopped when it arrives at convergence, for this geometry the results of a single resonance obtained by the WCIP method have a resonant frequency of 5.35 GHz with a band bandwidth of 2.3 GHz, when the structure is excited in the X direction, a frequency at 10.35 GHz with a bandwidth of 0.44 GHz when the structure is excited in the Y direction. The simulation of the results obtained by the WCIP method is compared with the results of the software HFSS 13.0 (High Frequency Structure Simulator), we find a good agreement.

Dans cette thèse, nous nous sommes intéressés à un modèle de gestion des connaissances des entreprises industrielles. Certaines tâches manufacturières impliquent un niveau élevé de connaissance tacite des opérateurs qualifiés. L'industrie a besoin des méthodes fiables pour la capture et l'analyse de ces connaissances tacites afin qu'elles puissent être partagées et sans aucune perte. Nous proposons, un modèle de gestion contenant deux processus de gestion, le premier processus est la capitalisation des connaissances basée sur une tâche industrielle. Nous avons utilisé une combinaison de deux méthodologies : une méthodologie d’ingénierie de connaissances CommonKADS et une méthodologie d’élicitation des connaissances MACTAK. Dans la phase de modélisation, nous avons utilisé deux différentes techniques de modélisation, une modélisation basée sur les connaissances d’expert et la deuxième une représentation ontologique. Ce modèle facilite la capture des connaissances d’experts et transforme les connaissances tacites en explicites avec une maximisation des règles de production. Le deuxième processus concerne le partage des connaissances à base d’une ontologie des Tâches Manufacturières MATO en identifiant un ensemble des concepts de fabrication et leurs relations, cette ontologie proposée facilite le partage des connaissances entre les tâches de fabrication et aide à partager et à réutiliser les connaissances durant l'exécution des tâches. Ensuite, une application proposée pour le diagnostic de système d’alarme dans une centrale thermique a été présentée pour démontrer l’importance et l’apport de l’ontologie.

In recent decades, vascular surgery has seen the arrival of endovascular techniques for the treatment of vascular diseases such as aortic diseases (aneurysms, dissections, and atherosclerosis). The 3D printing process by addition of material gives an effector of choice to the digital chain, opening the way to the manufacture of shapes and complex geometries, impossible to achieve before with conventional methods. This chapter focuses on the bio-design study of the thoracic aorta in adults. A bio-design protocol was established based on medical imaging, extraction of the shape, and finally, the 3D modeling of the aorta; secondly, a bio-printing method based on 3D printing that could serve as regenerative medicine has been proposed. A simulation of the bio-printing process was carried out under the software Simufact Additive whose purpose is to predict the distortion and residual stress of the printed model. The binder injection printing technique in a Powder Bed Printer (PBP) bed is used. The results obtained are very acceptable compared with the results of the error elements found.

In recent decades, vascular surgery has seen the arrival of endovascular techniques for the treatment of vascular diseases such as aortic diseases (aneurysms, dissections, and atherosclerosis). The 3D printing process by addition of material gives an effector of choice to the digital chain, opening the way to the manufacture of shapes and complex geometries, impossible to achieve before with conventional methods. This chapter focuses on the bio-design study of the thoracic aorta in adults. A bio-design protocol was established based on medical imaging, extraction of the shape, and finally, the 3D modeling of the aorta; secondly, a bio-printing method based on 3D printing that could serve as regenerative medicine has been proposed. A simulation of the bio-printing process was carried out under the software Simufact Additive whose purpose is to predict the distortion and residual stress of the printed model. The binder injection printing technique in a Powder Bed Printer (PBP) bed is used. The results obtained are very acceptable compared with the results of the error elements found.

Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking technique is developed in order to improve the photovoltaic system performances under the variations of the temperature and irradiation. Methods. This work is to calculate and follow the maximum power point for a photovoltaic system operating according to the artificial intelligence mechanism is and the latter is used an adaptive modified perturbation and observation maximum power point tracking algorithm based on function sign to generate an specify duty cycle applied to DC-DC converter, where we use the feed forward artificial neural network type trained by Levenberg-Marquardt backpropagation. Results. The photovoltaic system that we chose to simulate and apply this intelligent technique on it is a stand-alone photovoltaic system. According to the results obtained from simulation of the photovoltaic system using adaptive modified perturbation and observation artificial neural network the efficiency and the quality of the production of energy from photovoltaic is increased. Practical value. The proposed algorithm is validated by a dSPACE DS1104 for different operating conditions. All practice results confirm the effectiveness of our proposed algorithm.

Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking technique is developed in order to improve the photovoltaic system performances under the variations of the temperature and irradiation. Methods. This work is to calculate and follow the maximum power point for a photovoltaic system operating according to the artificial intelligence mechanism is and the latter is used an adaptive modified perturbation and observation maximum power point tracking algorithm based on function sign to generate an specify duty cycle applied to DC-DC converter, where we use the feed forward artificial neural network type trained by Levenberg-Marquardt backpropagation. Results. The photovoltaic system that we chose to simulate and apply this intelligent technique on it is a stand-alone photovoltaic system. According to the results obtained from simulation of the photovoltaic system using adaptive modified perturbation and observation artificial neural network the efficiency and the quality of the production of energy from photovoltaic is increased. Practical value. The proposed algorithm is validated by a dSPACE DS1104 for different operating conditions. All practice results confirm the effectiveness of our proposed algorithm.

Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking technique is developed in order to improve the photovoltaic system performances under the variations of the temperature and irradiation. Methods. This work is to calculate and follow the maximum power point for a photovoltaic system operating according to the artificial intelligence mechanism is and the latter is used an adaptive modified perturbation and observation maximum power point tracking algorithm based on function sign to generate an specify duty cycle applied to DC-DC converter, where we use the feed forward artificial neural network type trained by Levenberg-Marquardt backpropagation. Results. The photovoltaic system that we chose to simulate and apply this intelligent technique on it is a stand-alone photovoltaic system. According to the results obtained from simulation of the photovoltaic system using adaptive modified perturbation and observation artificial neural network the efficiency and the quality of the production of energy from photovoltaic is increased. Practical value. The proposed algorithm is validated by a dSPACE DS1104 for different operating conditions. All practice results confirm the effectiveness of our proposed algorithm.

Introduction. This article deals with the optimization of the energy conversion of a grid-connected photovoltaic system. The novelty is to develop an intelligent maximum power point tracking technique using artificial neural network algorithms. Purpose. Intelligent maximum power point tracking technique is developed in order to improve the photovoltaic system performances under the variations of the temperature and irradiation. Methods. This work is to calculate and follow the maximum power point for a photovoltaic system operating according to the artificial intelligence mechanism is and the latter is used an adaptive modified perturbation and observation maximum power point tracking algorithm based on function sign to generate an specify duty cycle applied to DC-DC converter, where we use the feed forward artificial neural network type trained by Levenberg-Marquardt backpropagation. Results. The photovoltaic system that we chose to simulate and apply this intelligent technique on it is a stand-alone photovoltaic system. According to the results obtained from simulation of the photovoltaic system using adaptive modified perturbation and observation artificial neural network the efficiency and the quality of the production of energy from photovoltaic is increased. Practical value. The proposed algorithm is validated by a dSPACE DS1104 for different operating conditions. All practice results confirm the effectiveness of our proposed algorithm.

Feed for word neural networks (FFNN) have attracted a great attention, in digital communication area. Especially they are investigated as nonlinear equalizers at the receiver, to mitigate channel distortions and additive noise. The major drawback of the FFNN is their extensive training. We present a new approach to enhance their training efficiency by adapting the activation function. Adapting procedure for activation function extensively increases the flexibility and the nonlinear approximation capability of FFNN. Consequently, the learning process presents better performances, offers more flexibility and enhances nonlinear capability of NN structure thus the final state kept away from undesired saturation regions. The effectiveness of the proposed method is demonstrated through different challenging channel models, it performs quite well for nonlinear channels which are severe and hard to equalize. The performance is measured throughout, convergence properties, minimum bit error achieved. The proposed algorithm was found to converge rapidly, and accomplish the minimum steady state value. All simulation shows that the proposed method improves significantly the training efficiency of FFNN based equalizer compared to the standard training one.

Feed for word neural networks (FFNN) have attracted a great attention, in digital communication area. Especially they are investigated as nonlinear equalizers at the receiver, to mitigate channel distortions and additive noise. The major drawback of the FFNN is their extensive training. We present a new approach to enhance their training efficiency by adapting the activation function. Adapting procedure for activation function extensively increases the flexibility and the nonlinear approximation capability of FFNN. Consequently, the learning process presents better performances, offers more flexibility and enhances nonlinear capability of NN structure thus the final state kept away from undesired saturation regions. The effectiveness of the proposed method is demonstrated through different challenging channel models, it performs quite well for nonlinear channels which are severe and hard to equalize. The performance is measured throughout, convergence properties, minimum bit error achieved. The proposed algorithm was found to converge rapidly, and accomplish the minimum steady state value. All simulation shows that the proposed method improves significantly the training efficiency of FFNN based equalizer compared to the standard training one.

Feed for word neural networks (FFNN) have attracted a great attention, in digital communication area. Especially they are investigated as nonlinear equalizers at the receiver, to mitigate channel distortions and additive noise. The major drawback of the FFNN is their extensive training. We present a new approach to enhance their training efficiency by adapting the activation function. Adapting procedure for activation function extensively increases the flexibility and the nonlinear approximation capability of FFNN. Consequently, the learning process presents better performances, offers more flexibility and enhances nonlinear capability of NN structure thus the final state kept away from undesired saturation regions. The effectiveness of the proposed method is demonstrated through different challenging channel models, it performs quite well for nonlinear channels which are severe and hard to equalize. The performance is measured throughout, convergence properties, minimum bit error achieved. The proposed algorithm was found to converge rapidly, and accomplish the minimum steady state value. All simulation shows that the proposed method improves significantly the training efficiency of FFNN based equalizer compared to the standard training one.

Feed for word neural networks (FFNN) have attracted a great attention, in digital communication area. Especially they are investigated as nonlinear equalizers at the receiver, to mitigate channel distortions and additive noise. The major drawback of the FFNN is their extensive training. We present a new approach to enhance their training efficiency by adapting the activation function. Adapting procedure for activation function extensively increases the flexibility and the nonlinear approximation capability of FFNN. Consequently, the learning process presents better performances, offers more flexibility and enhances nonlinear capability of NN structure thus the final state kept away from undesired saturation regions. The effectiveness of the proposed method is demonstrated through different challenging channel models, it performs quite well for nonlinear channels which are severe and hard to equalize. The performance is measured throughout, convergence properties, minimum bit error achieved. The proposed algorithm was found to converge rapidly, and accomplish the minimum steady state value. All simulation shows that the proposed method improves significantly the training efficiency of FFNN based equalizer compared to the standard training one.

In this digital age embracing robotics across various areas of life, especially intellectual ones, have reaped great benefits owing to this modern technology. Therefore, the learning field has not remained unchanged given current evolutions as the schooling conditions have been improved through these smart devices. However, teachers still face some difficulties when choosing pedagogical methods and means for effective language learning for children. Thus, this paper aims to measure the effectiveness of social robots in facilitating children’s learning of a second language (L2). For this purpose, the term L2 learning and its subordinate concepts have been distinguished, and then the different methods of language learning were discussed. The latest research regarding social robots in the educational context was also discussed when reviewing the literature. An experimental study conducted on a sample of children illustrated that the use of the social robot significantly helped them in the L2 learning regarding the assimilation fast, retention, and correct pronunciation of its vocabulary. Finally, this study concludes that the social robot would be a good solution and recommends their widespread use in education given its role in improving the schooling conditions of children.

In this digital age embracing robotics across various areas of life, especially intellectual ones, have reaped great benefits owing to this modern technology. Therefore, the learning field has not remained unchanged given current evolutions as the schooling conditions have been improved through these smart devices. However, teachers still face some difficulties when choosing pedagogical methods and means for effective language learning for children. Thus, this paper aims to measure the effectiveness of social robots in facilitating children’s learning of a second language (L2). For this purpose, the term L2 learning and its subordinate concepts have been distinguished, and then the different methods of language learning were discussed. The latest research regarding social robots in the educational context was also discussed when reviewing the literature. An experimental study conducted on a sample of children illustrated that the use of the social robot significantly helped them in the L2 learning regarding the assimilation fast, retention, and correct pronunciation of its vocabulary. Finally, this study concludes that the social robot would be a good solution and recommends their widespread use in education given its role in improving the schooling conditions of children.

In this digital age embracing robotics across various areas of life, especially intellectual ones, have reaped great benefits owing to this modern technology. Therefore, the learning field has not remained unchanged given current evolutions as the schooling conditions have been improved through these smart devices. However, teachers still face some difficulties when choosing pedagogical methods and means for effective language learning for children. Thus, this paper aims to measure the effectiveness of social robots in facilitating children’s learning of a second language (L2). For this purpose, the term L2 learning and its subordinate concepts have been distinguished, and then the different methods of language learning were discussed. The latest research regarding social robots in the educational context was also discussed when reviewing the literature. An experimental study conducted on a sample of children illustrated that the use of the social robot significantly helped them in the L2 learning regarding the assimilation fast, retention, and correct pronunciation of its vocabulary. Finally, this study concludes that the social robot would be a good solution and recommends their widespread use in education given its role in improving the schooling conditions of children.

The aim of the present study was to evaluate age related changes in testicular histomorphometry and spermatogenic activity of bulls during their sexual development. A total of 36 bulls were selected and divided into four groups (n=9 in each) according to their age. Bulls included in Groups I, II, III and IV were 10, 12, 14 and 16 months old respectively. Left testes of bulls were subjected to histomorphometry after slaughter. Statistical analysis revealed that the secondary spermatocytes, round and elongated spermatids increased significantly (P˂0.05) with the age of bulls. Likewise, both sertoli and leydig cell numbers increased significantly (P˂0.05) with the age of bulls. However, the number of spermatogonia and primary spermatocytes did not change (P>0.05) due to age. The mean tubular diameter increased from 200.70±5.45 μm (10 months of age) to 227.30±9.16 μm (16 months of age) and the total volume of seminiferous tubule per testis from 69.63±1.50 % (10 months of age) to 84.64±2.53 % (16 months of age). A positive linear relationship (P<0.05) was found between meiotic index (Y) and the age (X, in month), which was characterized by the equation 0.048X+3.135 and a coefficient of correlation (R) of 0.396. The correlation between age and sertoli cell efficiency was 0.482 with a regression equation Y= 0.141X+7.696. It is concluded that histomorphometric parameters of the bulls’ testes and spermatogenic activity are correlated with the age, so these parameters provide a reliable tool for the assessment of the reproductive state and sperm production capacity of a bull in a breeding program.