ETUDE DE L’EFFET DE LA FORME DU RADIER DE L’ALVEOLE AVAL SUR LA PERFORMANCE DU DEVERSOIR EN TOUCHES DE PIANO

Une nouvelle forme de déversoir non rectiligne baptisée Piano Key Weir (PK-Weir) a été développée enl’année2003 par Hydrocoop-France et le Laboratoire Aménagements Hydrauliques et Environnement de l’Université de Biskra (ALGERIE). Cette innovation, a été primée par le trophée de la COP 21, lors de la conférence de Paris sur le climat (2015). Elle représente une alternative fiable et économique pour contrer les risques néfaste des crues. Ce type de déversoir secaractérise par une géométrie particulière définit par des murs verticaux formant des éléments rectangulaires en plan, avec une partie en porte-à-faux ce qui favorise la conception d’un radier incliné. Le radier des alvéoles du PK-Weir peut prendre plusieurs configurations selon l’emplacement du PK-Weir et l’objectif prévu. Le présent travail s’intéresse à l’étude de la forme du radier des alvéoles aval du PK-Weir type A1 sous différentes configurations. Pour ce faire, plusieurs formes de radiers ont fait l’objet d’études expérimentales sur modèles physiques avec des conditions d’écoulement libre et sans contraction latérale. Selon les résultats obtenus, il a été conclu que le PK-Weir de type A1 représente une configuration géométrique qui permet de concevoir le radier des alvéoles aval sous plusieurs forme en fonction de l’objectif défini sans pour autant affecté la performance hydraulique

Mobilização de Águas Superficiais: Comentários Gerais sobre Barragens na Argélia no Passado, Presente e Futuro

Algeria is located in a region with an arid climate, which requires rational management of water resources. According to the current state of knowledge, the mobilisation of water through dams started with diversion works before the colonial period. During the colonial period (1830-1962), sixteen dams with a total capacity of one billion m3 were built. It was only after independence that large dams were erected to reach a capacity of about nine billion m3 in 2020. The preservation of these structures is one of the current and future priorities. The phenomenon of silting risks compromising development in terms of water mobilisation. Currently, the storage capacity lost through silting is 11 %. To remedy this phenomenon, studies have been carried out at the University of Biskra (Algeria) with Hydrocoop-France which have made it possible to define new techniques for increasing storage capacity.Argelia se encuentra en una región de clima árido, que exige una gestión racional de los recursos hídricos. Según los conocimientos actuales, la movilización del agua con presas comenzó con las obras de desviación antes del periodo colonial. Durante este periodo (1830-1962) se construyeron dieciséis presas con una capacidad total de mil millones de m3. Después de la independencia, se construyeron grandes presas para alcanzar una capacidad de unos nueve mil millones m3 en 2020. La conservación de estas estructuras es una de las prioridades actuales y futuras. El fenómeno del encenagamiento corre el riesgo de comprometer el desarrollo en términos de movilización del agua. Actualmente, la capacidad de almacenamiento que se pierde por sedimentación es del 11 %. Para remediarlo, se han realizado estudios en la Universidad de Biskra (Argelia) con Hydrocoop-Francia, que han permitido definir nuevas técnicas para aumentar la capacidad de almacenamientoL’Algérie est dans une région à climat aride, ceci nécessite une gestion rationnelle de la ressource en eau. Selon l’état des connaissances actuelles, la mobilisation des eaux par le biais des barrages a commencé par des ouvrages de dérivation avant l’époque coloniale. Pendant cette période (1830–1962), on compte l’édification de seize barrages d’une capacité totale d’un milliard de m3. Après l’indépendance, des grands barrages ont été érigés pour atteindre une capacité d’environ neuf milliards de m3 en 2020. La préservation de ces ouvrages est l’une des priorités actuelles et futur. Le phénomène d’envasement risque de compromettre le développement en matière de mobilisation des eaux. Actuellement, la capacité de stockage perdue par envasement est de 11 %. Pour y remédier, des études ont été réalisées à l’université de Biskra (Algérie) avec Hydrocoop-France, qui ont permis de définir de nouvelles techniques d’accroissement de la capacité de stockage.L'Algeria si trova in una regione dal clima arido, che richiede una gestione razionale delle risorse idriche. Secondo lo stato attuale delle conoscenze, la mobilitazione dell'acqua attraverso le dighe è iniziata con opere di deviazione prima del periodo coloniale. Durante il periodo coloniale (1830-1962), furono costruite sedici dighe con una capacità totale di un miliardo di m3. Solo dopo l'indipendenza sono state erette grandi dighe per raggiungere una capacità di circa nove miliardi di m3 nel 2020. La conservazione di queste strutture è una delle priorità attuali e future. Il fenomeno dell'insabbiamento rischia di compromettere lo sviluppo in termini di mobilitazione dell'acqua. Attualmente, la capacità di stoccaggio persa a causa dell'insabbiamento è dell'11 %. Per rimediare a questo fenomeno, sono stati condotti studi all'Università di Biskra (Algeria) con Hydrocoop-France che hanno permesso di definire nuove tecniche per aumentare la capacità di stoccaggio.A Argélia está situada numa região de clima árido, o que requer uma gestão racional dos recursos hídricos. De acordo com o estado actual dos conhecimentos, a mobilização da água através de barragens começou com obras de desvio antes do período colonial. Durante este tempo (1830-1962), foram construídas dezasseis barragens com uma capacidade total de um bilião de m3. Após a independência grandes barragens foram erguidas para atingir uma capacidade de cerca de nove mil milhões de m3 em 2020. A preservação destas estruturas é uma das prioridades actuais e futuras. O fenómeno do assoreamento corre o risco de comprometer o desenvolvimento em termos de mobilização de água. Actualmente, a capacidade de armazenamento perdida através do assoreamento é de 11 %. Para remediar, foram realizados estudos na Universidade de Biskra (Argélia) com Hydrocoop-France que permitiram definir novas técnicas para aumentar a capacidade de armazenament

ETUDE DU DEVERSOIR EN TOUCHES DE PIANO (TYPE A) FONCTIONNANT DANS DES CONDITIONS D’ECOULEMENTS LIBRE ET NOYE

Le déversoir en touches de piano (PK-Weir) est une solution qui permet d’améliorer l'efficacité et la sécurité des ouvrages hydrauliques tels que les barrages réservoirs et les seuils en rivières. De nombreuses études théoriques et expérimentales ont été menées par les différents chercheurs du domaine des ouvrages hydrauliques pour tenter de mieux comprendre son fonctionnement et de définir sa configuration géométrique optimale pour des conditions d’écoulement libre. Par contre, il n’y a que peu de travaux menés sur PK-Weir avec des conditions d'écoulement submergé.  Le présent travail consiste à étudier par voie expérimentale les deux types d’écoulement, à savoir, l’écoulement libre et noyésur deux géométries de PK-Weir (type A et A1m).L’analyse des résultats a fait remarquer que dans les conditions d’écoulement libre, le modèle de PK-Weir de type A1m a été défini comme étant plus performant que le type A. Cependant, dans les conditions d’écoulement noyé, il a été constaté que la variation du niveau aval pouvait influencer l’écoulement en amont du PK-Weir, l'importance de cet effet varie en fonction de la géométrie du modèle

ETUDE DU DEVERSOIR EN TOUCHES DE PIANO (TYPE A) FONCTIONNANT DANS DES CONDITIONS D’ECOULEMENTS LIBRE ET NOYE

Le déversoir en touches de piano (PK-Weir) est une solution qui permet d’améliorer l'efficacité et la sécurité des ouvrages hydrauliques tels que les barrages réservoirs et les seuils en rivières. De nombreuses études théoriques et expérimentales ont été menées par les différents chercheurs du domaine des ouvrages hydrauliques pour tenter de mieux comprendre son fonctionnement et de définir sa configuration géométrique optimale pour des conditions d’écoulement libre. Par contre, il n’y a que peu de travaux menés sur PK-Weir avec des conditions d'écoulement submergé.  Le présent travail consiste à étudier par voie expérimentale les deux types d’écoulement, à savoir, l’écoulement libre et noyésur deux géométries de PK-Weir (type A et A1m).L’analyse des résultats a fait remarquer que dans les conditions d’écoulement libre, le modèle de PK-Weir de type A1m a été défini comme étant plus performant que le type A. Cependant, dans les conditions d’écoulement noyé, il a été constaté que la variation du niveau aval pouvait influencer l’écoulement en amont du PK-Weir, l'importance de cet effet varie en fonction de la géométrie du modèle

Feature fusion via deep random forest for facial age estimation

International audienceIn the last few years, human age estimation from face images attracted the attention of many researchers in computer vision and machine learning fields. This is due to its numerous applications. In this paper, we propose a new architecture for age estimation based on facial images. It is mainly based on a cascade of classification trees ensembles, which are known recently as a Deep Random Forest. Our architecture is composed of two types of DRF. The first type extends and enhances the feature representation of a given facial descriptor. The second type operates on the fused form of all enhanced representations in order to provide a prediction for the age while taking into account the fuzziness property of the human age. While the proposed methodology is able to work with all kinds of image features, the face descriptors adopted in this work used off-the-shelf deep features allowing to retain both the rich deep features and the powerful enhancement and decision provided by the proposed architecture. Experiments conducted on six public databases prove the superiority of the proposed architecture over other state-of-the-art methods. (C) 2020 Elsevier Ltd. All rights reserved

Knowledge-based tensor subspace analysis system for kinship verification

International audienceMost existing automatic kinship verification methods focus on learning the optimal distance metrics between family members. However, learning facial features and kinship features simultaneously may cause the proposed models to be too weak. In this work, we explore the possibility of bridging this gap by developing knowledge-based tensor models based on pre-trained multi-view models. We propose an effective knowledge-based tensor similarity extraction framework for automatic facial kinship verification using four pre-trained networks (i.e., VGG-Face, VGG-F, VGG-M, and VGG-S). Therefore, knowledge-based deep face and general features (such as identity, age, gender, ethnicity, expression, lighting, pose, contour, edges, corners, shape, etc.) were successfully fused by our tensor design to understand the kinship cue. Multiple effective representations are learned for kinship verification statements (children and parents) using a margin maximization learning scheme based on Tensor Cross-view Quadratic Exponential Discriminant Analysis. Through the exponential learning process, the large gap between distributions of the same family can be reduced to the maximum, while the small gap between distributions of different families is simultaneously increased. The WCCN metric successfully reduces the intra-class variability problem caused by deep features. The explanation of black-box models and the problems of ubiquitous face recognition are considered in our system. The extensive experiments on four challenging datasets show that our system performs very well compared to state-of-the-art approaches

Robust multimodal 2Dand 3D face authentication using local feature fusion

IF=1.43International audienceIn this work, we present a robust face authentication approach merging multiple descriptors and exploiting both 3D and 2D information. First, we correct the heads rotation in 3D by iterative closest point algorithm, followed by an efficient preprocessing phase. Then, we extract different features namely: multi-scale local binary patterns (MSLBP), novel statistical local features (SLF), Gabor wavelets, and scale invariant feature transform (SIFT). The principal component analysis followed by enhanced fisher linear discriminant model is used for dimensionality reduction and classification. Finally, fusion at the score level is carried out using two-class support vector machines. Extensive experiments are conducted on the CASIA 3D faces database. The evaluation of individual descriptors clearly showed the superiority of the proposed SLF features. In addition, applying the (3D+2D) multimodal score level fusion, the best result is obtained by combining the SLF with the MSLBP+SIFT descriptor yielding in an equal error rate of 0.98 % and a recognition rate of RR=97.22%

Learning multi-view deep and shallow features through new discriminative subspace for bi-subject and tri-subject kinship verification

International audienceThis paper presents the combination of deep and shallow features (multi-view features) using the proposed metric learning (SILD+WCCN/LR) approach for kinship verification. Our approach based on an automatic and more efficient two-step learning into deep/shallow information. First, five layers for deep features and five shallow features (i.e. texture and shape), representing more precisely facial features involved in kinship relations (Father-Son, Father-Daughter, Mother-Son, and Mother-Daughter) are used to train the proposed Side-Information based Linear Discriminant Analysis integrating Within Class Covariance Normalization (SILD+WCCN) method. Then, each of the features projected through the discriminative subspace of the proposed SILD+WCCN metric learning method. Finally, a Logistic Regression (LR) method is used to fuse the six scores of the projected features. To show the effectiveness of our SILD+WCNN method, we do some experiments on LFW database. In term of evaluation, the proposed automatic Facial Kinship Verification (FKV) is compared with existing ones to show its effectiveness, using two challenging kinship databases. The experimental results showed the superiority of our FKV against existing ones and reached verification rates of 86.20% and 88.59% for bi-subject matching on the KinFaceW-II and TSKinFace databases, respectively. Verification rates for tri-subject matching of 90.94% and 91.23% on the available TSKinFace database for Father-Mother-Son and Father-Mother-Daughter, respectively

Kinship verification from face images in discriminative subspaces of color components

International audienceAutomatic facial kinship verification is a challenging topic in computer vision due to its complexity and its important role in many applications such as finding missing children and forensics. This paper presents a Facial Kinship Verification (FKV) approach based on an automatic and more efficient two-step learning into color/texture information. Most of the proposed methods in automatic kinship verification from face images consider the luminance information only (i.e. gray-scale) and exclude the chrominance information (i.e. color) that can be helpful, as an additional cue, for predicting relationships. We explore the joint use of color-texture information from the chrominance and the luminance channels by extracting complementary low-level features from different color spaces. More specifically, the features are extracted from each color channel of the face image and fused to achieve better discrimination. We investigate different descriptors on the existing face kinship databases, illustrating the usefulness of color information, compared with the gray-scale counterparts, in seven various color spaces. Especially, we generate from each color space three subspaces projection matrices and then score fusion methodology to fuse three distances belonging to each test pair face images. Experiments on three benchmark databases, namely the Cornell KinFace, the KinFaceW (I & II) and the TSKinFace database, show superior results compared to the state of the art