Portail HAL Ensta
Not a member yet
11080 research outputs found
Sort by
Deuteron relativistic charge distributions
No full textInternational audienceWe study the relativistic 2D charge distributions in the case of a spin-1 target. These charge distributions are based on a phase-space approach allowing one to study their frame dependence, and hence to relate the familiar rest-frame picture with the light-front picture developed in the last two decades. Like in the spin-1/2 case, we show that relativistic kinematical effects associated with spin are responsible for the distortions of the charge distributions seen in a moving frame. Applying our results to the deuteron, we observe a mild frame dependence compared to the nucleon case
Optimal planetary landing with pointing and glide-slope constraints
No full textInternational audienceThis paper studies a vertical powered descent problem in the context of planetary landing, considering glide-slope and thrust pointing constraints and minimizing any final cost. After stating the Max-Min-Max or Max-Singular-Max form of the optimal control deduced from the Pontryagin Maximum Principle, it theoretically analyzes the optimal trajectory for a more specific problem formulation to show that there can be at most one contact or boundary interval with the state constraint on each Max or Min arc
Survey of spatio-temporal couplings throughout high-power ultrashort lasers
No full textInternational audienceThe investigation of spatio-temporal couplings (STCs) of broadband light beams is becoming a key topic for the optimization as well as applications of ultrashort laser systems. This calls for accurate measurements of STCs. Yet, it is only recently that such complete spatio-temporal or spatio-spectral characterization has become possible, and it has so far mostly been implemented at the output of the laser systems, where experiments take place. In this survey, we present for the first time STC measurements at different stages of a collection of high-power ultrashort laser systems, all based on the chirped-pulse amplification (CPA) technique, but with very different output characteristics. This measurement campaign reveals spatio-temporal effects with various sources, and motivates the expanded use of STC characterization throughout CPA laser chains, as well as in a wider range of types of ultrafast laser systems. In this way knowledge will be gained not only about potential defects, but also about the fundamental dynamics and operating regimes of advanced ultrashort laser systems
Optimisation du dimensionnement d'une flotte de véhicules électriques et de leurs bornes de recharge par des méthodes de décomposition
No full textInternational audienceOptimisation du dimensionnement d'une flotte de véhicules électriques et de leurs bornes de recharge par des méthodes de décompositio
Utilisation d'indices contextuels et d'interactions pour la prédiction de la trajectoire des usagers de la route
No full textAutonomous vehicle navigation in urban areas involves interactions with the different road-users or traffic-agents like cars, bicycles, and pedestrians, sharing the same road network. The ability of autonomous vehicle to observe, understand and predict the behaviour of these traffic-agents is very important to gain a good situation understanding prior to deciding what manoeuvre to follow. While this is achieved to various degrees of success using model-based or data-driven methods, human drivers remain much more efficient at this task, instinctively inferring different agent motions even in previously unseen and challenging situations. Moreover, context plays a very important role that enables us humans to understand what is being perceived and make finer predictions. The need to increase situational awareness of autonomous vehicles, as well as for safety related driving assistance functions, stimulates our goal to exploit contextual information to predict the future trajectories of the observed traffic-agents in different conditions.Over the past years, machine learning has proven to be efficient at solving a wide variety of problems, particularly those associated to machine perception. This thesis therefore focuses on developing machine learning models to exploit contextual information in order to observe and learn the trajectories of different interacting traffic-agents as perceived from an autonomous vehicle. While most models proposed in the past rely on a single sensor and model-based techniques, the current approaches often rely on the use of multiple sensors and process their outputs using different machine learning methods. The approach proposed in this thesis follows these trends by combining information from different sensors to predict the trajectories of the observed traffic-agents using machine learning, as well as integrating contextual information and interactions into the prediction process.The thesis gradually builds a machine learning architecture based on a theoretical formulation and experimentation. Our approach is based on an LSTM encoder-decoder model that accepts data from different inputs. Trajectory observations from 3D LiDAR point-cloud data and semantic information from map-masks are used. Map masks represent areas where the traffic-agents can operate or not, in a binary manner. The information on pedestrian attention to oncoming vehicles obtained from camera images is also exploited to enrich the sequence prediction system. The goal is to feed the model with context-cues and semantic information to enhance the prediction of the traffic-agent trajectories, by knowing whether or not the agents are aware of the presence of the subject vehicle and including knowledge on areas where they are likely to navigate. Moreover, interactions of the autonomous vehicle with traffic-agents often govern its behaviour as the vehicle navigates. A mechanism to incorporate this information to the machine learning model is also developed as an interaction-aware trajectory prediction system enhanced by context-cues.Machine learning architectures are built using datasets acquired from the perception sensors of a vehicle navigating in the expected workspace. As datasets play an important role in solving machine learning problems, available annotated datasets for autonomous navigation were reviewed according to their availability of sensor data and contextual information. Experiments were performed for our models to learn, and gradually build the resulting architecture. Their performance are demonstrated using the well-known NuScenes dataset acquired in urban settings. The performance of the proposed approach were compared with model and data-driven approaches, demonstrating that the incorporation of multiple contextual information and agent interactions provides a substantial performance increase.La navigation autonome des véhicules dans les zones urbaines implique des interactions avec les différents usagers de la route ou agents de la circulation partageant le même réseau routier comme les voitures, les vélos et les piétons. La capacité du véhicule autonome à observer, comprendre et prédire le comportement de ces agents est très importante pour acquérir une bonne compréhension de la situation avant de décider de la manœuvre à suivre. Bien que cela soit réalisé à divers degrés de succès en utilisant des méthodes basées sur des modèles ou des données, les conducteurs humains restent beaucoup plus efficaces dans cette tâche, déduisant instinctivement différents mouvements d'agent même dans des situations inédites et difficiles. De plus, le contexte joue un rôle très important qui permet à nous les humains de comprendre ce qui est perçu et de faire des prédictions plus fines. La nécessité d'accroître la connaissance de la situation des véhicules autonomes, ainsi que des fonctions d'aide à la conduite liées à la sécurité, stimule notre objectif d'exploiter ces informations contextuelles pour prédire les trajectoires futures des agents observés dans différentes conditions.Au cours des dernières années, l'apprentissage automatique s'est avéré efficace pour résoudre une grande variété de problèmes, en particulier ceux associés à la perception. Cette thèse se concentre donc sur le développement de modèles d'apprentissage automatique pour exploiter des informations contextuelles afin d'observer et d'apprendre les trajectoires de différents agents en interaction. Alors que la plupart des modèles proposés dans le passé reposent sur un seul capteur et des techniques basées sur un modèle, les approches actuelles reposent souvent sur l'utilisation de plusieurs capteurs et traitent leurs sorties à l'aide de différentes méthodes d'apprentissage automatique. L'approche proposée dans cette thèse suit ces tendances en combinant les informations de différents capteurs pour prédire les trajectoires des agents observés à l'aide de l'apprentissage automatique, ainsi qu'en intégrant des informations contextuelles et des interactions dans le processus de prédiction.La thèse construit progressivement une architecture d'apprentissage automatique basée sur une formulation théorique et des expérimentations. Notre approche est basée sur un modèle d'encodeur-décodeur LSTM qui accepte les données de différentes entrées. Des observations de trajectoire à partir de données de nuages de points LiDAR 3D et d'informations sémantiques à partir de masques de carte sont utilisées. Les masques de cartes représentent des zones où les agents peuvent opérer ou non, de manière binaire. Les informations sur l'attention des piétons aux véhicules venant en sens inverse obtenues à partir des images des caméras sont également exploitées pour enrichir le système de prédiction de séquence. L'objectif est d'alimenter le modèle avec des indices contextuels et des informations sémantiques.Les architectures d'apprentissage sont construites à partir de jeux de données acquis à partir des capteurs de perception d'un véhicule. Étant donné qu'ils jouent un rôle important dans la résolution des problèmes d'apprentissage, les jeux de données annotés disponibles pour la navigation autonome ont été examinés en fonction de la disponibilité des données des capteurs et des informations contextuelles. Sur cette base, nos expériences ont permis de valider nos modèles et de construire progressivement leur architecture. Leurs performances sont démontrées à l'aide du célèbre jeu de données NuScenes acquis en milieu urbain. Les performances de l'approche proposée comparées aux approches basées sur des modèles et des données démontrent que l'ajout de multiples informations contextuelles et des interactions d'agents permet une augmentation substantielle des performances
Investigation of drying shrinkage of cement-based materials assisted by digital image correlation
No full textInternational audienceThis paper presents a validation of a new protocol for the characterization of drying shrinkage of cement paste. The method consists of using environmental scanning electronic microscope (ESEM) or climatic chamber (CC) assisted by digital image correlation (DIC) to study the drying shrinkage of cement-based materials. The final motivation is to study the influence of the drying rate on cement-based materials’ delayed strain at the microscopic scale. Hence, the impact of specimen shape, size, and the imposed relative humidity history on the drying shrinkage is studied. Results show that for a given material, the amplitude of drying shrinkage is independent of the rate of drying and the specimen shape. Advantage was taken of the two techniques proposed in this paper to perform drying length change measurement both in ESEM and CC
Quadratic reformulations for the optimization of pseudo-boolean functions
No full textInternational audienceWe investigate various solution approaches for the uncon-strained minimization of a pseudo-boolean function. Moreprecisely, we assume that the original function is expressedas a real-valued polynomial in 0-1 variables, of degree threeor more, and we consider a generic family of two-step ap-proaches for its minimization. First, a quadratic reformu-lation step aims at transforming the minimization probleminto an equivalent constrained or unconstrained quadratic0-1 minimization problem (where “equivalent” means herethat a minimizer of the original function can be easily de-duced from a minimizer of the reformulation). Second, anoptimization step handles the obtained equivalent quadraticproblem.We provide a unified presentation of several quadratic re-formulation schemes proposed in the literature, e.g., (An-thony et al. 2017; Buchheim and Rinaldi 2007; Rodr ́ıguez-Heck 2018; Rosenberg 1975), and we review several meth-ods that can be applied in the optimization step, includinga standard linearization procedure (Fortet 1959) and moreelaborate convex quadratic reformulations, as in (Billionnetand Elloumi 2007; Billionnet, Elloumi, and Lambert 2012,2016; Elloumi, Lambert, and Lazare 2021). We discuss theimpact of the reformulation scheme on the efficiency of theoptimization step and we illustrate our discussion with somecomputational results on different classes of instances
Enhancement of mode I fracture toughness of adhesively bonded secondary joints using layup patterning of CFRP
No full textInternational audienceThis work aims to analyse the influence of the CFRP layup patterning on the crack path of composite bonded joints and evaluate its effect on the mode I fracture toughness. An experimental program has been performed using Double Cantilever Beam tests with three different CFRP layup patterning and two adhesives. In addition, a finite element analysis was also implemented to further identify different damage mechanisms during the tests. The outcome shows that different substrate CFRP layup patterning results in distinct crack onsets and propagation paths during the tests, also influenced by the type of adhesive used. Furthermore, an enhancement of around 25% in the joint's onset fracture toughness was observed with the layup patterning compared to a reference joint (with unidirectional layup). Thus, the substrate's patterning morphology seems to be a promising method to increase the mode I fracture toughness of the studied secondary joints
Bond line durability evaluation in a building integrated solar thermal system
No full textInternational audienc
Holistic Approach of Integrated Navigation Equipment for Cybersecurity at Sea
No full textInternational audienceRecent studies have demonstrated the interest of analyzing GNSS (Global Navigation Satellite System) and AIS (Automatic Identification System) data to improve the safety of naval infrastructures for a wide spectrum of maritime applications. However, in-depth analyzes also underline the sensitivity of these systems to attacks such as jamming and spoofing. In this context, it is essential that researchers, specialized organisations and companies rely on realistic data to improve these types of systems to better detect and cope with potential threats. However, because of the lack of open data sets, or due to financial, technical or operational reasons, the use of simulated data is preferred in most cases over real life data, which can lead to biases. To cope with this challenge, we have developed a prototype called "HAPPINESS" for "Holistic APProach of Integrated Navigation Equipment for Cybersecurity at Sea". The main objective of this dedicated and autonomous embedded system is to collect navigation data in real time without using proprietary or restrictive protocols. The generated open data, then continuously feeds a cyber naval platform able to reproduce the functional and operational systems of a ship. This prototype allows to reproduce the kinematics of a ship in various contexts (like specific maneuvers, long tracks, docking...) in NMEA format in order to design highly realistic scenarios based on real life data and allowing to obtain more complete and richer data (than those freely accessible online) in terms of information, giving additional means to detect anomalies on navigation systems