Portail HAL UHA (Université de Haute-Alsace)
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What are the impacts of combustion parameters on magnesia aerosol produced in a swirl-stabilized magnesium flame?
No full textInternational audienceMagnesium is an alternative to fossil fuels for the production of zero-carbon energy through combustion under air. Metal oxide MgO which is produced appears as white powder that can be trapped and recycled using clean primary energies. A trapping of MgO particles in the combustion system should be as complete as possible for an efficiently closed cycle. In the present study, MgO particles produced by a Mg/air swirled flame in a pilot burner were characterized through different and complementary techniques. More than 89 wt% of MgO particles were trapped in the combustion system. The diameter and the number of MgO particles were measured by means of an Electrical Low Pressure Impactor (ELPI) placed downstream of a cyclone subsystem. An in-depth study of the characteristics of MgO particles collected in the combustion chamber, the cyclone subsystem and on the ELPI plates was carried out. Number size distribution showed that MgO particles collected after the cyclone subsystem were mainly PM1, with a maximum of particles observed in the range 320–760 nm. Evolution of the MgO structure was analyzed by Scanning Electron Microscope (SEM) and Transmission Electron Microscope (TEM). MgO particles collected in the combustion chamber were cubic single crystals which formed first aggregates and micrometric sized particles. A global mechanism for the formation of MgO in the combustion system is finally proposed
Generalisations of multiple zeta values to rooted forests
No full textInternational audienceWe show that any convergent (shuffle) arborified zeta value admits a series representation. This justifies the introduction of a new generalisation to rooted forests of multiple zeta values, and we study its algebraic properties. As a consequence of the series representation, we derive elementary proofs of some results of Bradley and Zhou for Mordell-Tornheim zeta values and give explicit formulas. The series representation for shuffle arborified zeta values also implies that they are conical zeta values. We characterise which conical zeta values are arborified zeta values and evaluate them as sums of multiple zeta values with rational coefficients
Formability of through-the-thickness tufted reinforcements
No full textInternational audienceTufting technology is an advanced textile method to improve the interlaminarperformance of the composites. The slidable tufts make it possible to form throughthicknessreinforcements into double-curved shapes. However, it is still important tocontrol the tufting process to avoid or minimize the forming defects, which wouldreduce the mechanical properties of the composite part. The objective of the presentpaper is to give an overview of the literature dedicated to the formability of throughthicknesstufted reinforcements. Therefore, tufting technology, main formingparameters, and experimental characterization of forming behaviors are reviewed.Furthermore, some advanced numerical methods used for characterizing the wrinkledefects are summarized and the guidance for tufting process is given.La technologie de tufting (piquage) est un procédé textile avancé qui permet d'améliorer les performances inter laminaires des composites. Les boucles insérées permettent de renforcer dans l’épaisseur des renforts à formes à double courbure. Cependant, il est toujours important de contrôler le processus de tufting afin d'éviter ou de minimiser les défauts d’emboutissage, qui réduiraient les propriétés mécaniques de la pièce composite. L'objectif du présent article est de donner un aperçu de la littérature consacrée à la formabilité des renforts tuftés dans l'épaisseur. Il passe donc en revue la technologie du tufting, les principaux paramètres d’emboutissage et la caractérisation expérimentale des comportements des structures tuftées lors de l’emboutissage.En outre, certaines méthodes numériques avancées utilisées pour caractériser les défauts de plissage (rides) sont résumées et des conseils pour le processus de tufting sont donnés
Vers un protocole de consensus robuste et léger pour les blockchains autorisées
No full textThe PBFT consensus is widely used in permissioned blockchains due to its Byzantine fault tolerance and low energy consumption, although it has limited scalability because of quadratic communication.This thesis proposes a robust and lightweight consensus protocol that is Byzantine fault-tolerant, aiming to reduce bandwidth usage and latency while improving scalability. A scoring mechanism evaluates nodes, refined through reinforcement learning and optimized using a multi-task classification model to eliminate malicious nodes. Sharding enables simultaneous request processing, while a fair rotation system ensures balanced shard allocation. The integration of IPFS addresses storage challenges, and a hybrid incentive mechanism encourages active participation.Le consensus PBFT est largement utilisé dans les blockchains autorisées grâce à sa tolérance aux fautes byzantines et sa faible consommation énergétique, bien que peu évolutif en raison d'une communication quadratique.Cette thèse propose un protocole de consensus robuste et léger, tolérant aux fautes byzantines, visant à réduire la bande passante et la latence, tout en améliorant la scalabilité. Un mécanisme de notation évalue les nœuds, affiné par apprentissage par renforcement, puis optimisé via un modèle de classification multi-tâches, pour éliminer les nœuds malveillants. Le sharding permet le traitement simultané des requêtes, tandis qu’un système de rotation équitable assure une répartition équilibrée. L’intégration d’IPFS répond aux défis de stockage, et un mécanisme d’incitation hybride stimule la participation active
Génération de trajectoires locales temps réel pour véhicule autonome dans un environnement dynamique et coopératif
No full textResearch on Connected and Automated Vehicles (CAV) has primarily focused on highway and urban environments, neglecting the significance and dangers of two-lane two-way rural roads. However, CAV driving strategies have the potential to improve significantly this network safety, particularly in critical maneuvers such as overtaking. This PhD thesis proposes an overall safety autonomous driving architecture particularly adapted to overtaking maneuver in a two-lane two-way rural road context. The proposed architecture considers vehicle connectivity to share their ego speeds and positions, enabling a rule-based decision-making process coupled with a Fuzzy Inference Systems to manage the maneuver’s tasks and to ensure the feasibility of the maneuver. A safety-oriented abort task facilitates a return to the starting lane in case of potential collisions improving maneuver reactivity. Additionally, an original driving personalization is proposed through one driving style parameter modifying the trajectory shape and the maneuver initiation. Two low level controllers handle the vehicle control signals for braking, throttle, and steering wheel angle completing the architecture and allowing full autonomous driving. The algorithm is evaluated using a high fidelity simulation environment in different driving situations. The obtained results demonstrate its reliability and consistency in producing safe overtaking maneuvers regardless the generated situation. A Monte Carlo test highlights the correlation between driving style and comfort in most cases.La recherche sur les véhicules connectés et automatisés s'est principalement concentrée sur les conduites sur autoroutes et les environnements urbains, négligeant l'importance et les dangers des routes du réseau secondaire. Cependant, les stratégies de conduite automatisée ont le potentiel d'améliorer de manière significative la sécurité sur ce réseau, en particulier dans les manœuvres critiques telles que le dépassement. Cette thèse de doctorat propose une architecture de conduite autonome particulièrement adaptée aux manœuvres de dépassement sur route rurale à deux voies à double sens. L'architecture proposée utilise la connectivité des véhicules pour partager leurs vitesses et positions, permettant à un processus de prise de décision basé sur un système d'inférence floue de gérer les tâches de la manœuvre et assurer sa faisabilité. En particulier, une tâche d'abandon axée sur la sécurité facilite le retour sur la voie initiale en cas de collision potentielle, améliorant ainsi la réactivité de la manœuvre. De plus, une personnalisation originale de la conduite est proposée grâce à un paramètre de style de conduite qui modifie la forme de la trajectoire et le déclenchement de la manœuvre. Deux contrôleurs de bas niveau gèrent les signaux de commande du véhicule pour le freinage, l'accélérateur et l'angle du volant, complétant ainsi l'architecture et permettant une conduite entièrement autonome. La méthode est évaluée à l'aide d'un environnement de simulation haute-fidélité dans différentes situations de conduite. Les résultats obtenus démontrent sa fiabilité et sa cohérence dans la production de manœuvres de dépassement sûres, quelle que soit la situation générée
Référence implicite: une occasion d'échanges (explicites) de données entre chercheurs/euses et professionnels/les des bibliothèques
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Multi-surrogate assisted differential evolution for edge-based facility location problem
No full textInternational audienceThis paper addresses the computationally challenging edge-based facility location problem with the objective of minimizing total travel time while accommodating uniformly distributed demand on network edges. To enhance computational efficiency, the proposed method integrates differential evolution (DE) with three distinct surrogate models: random forest, extreme learning machines, and extreme gradient boosting. While the concept of distributed demand on network edges presents a more realistic depiction of location problems, the necessity of decomposing edges and assigning them to their nearest facilities increases the complexity of the problem at hand. Therefore, the development of an effective and efficient solution method is crucial, particularly in time-sensitive contexts where rapid decisions are essential. Empirical evaluations demonstrate the efficacy and efficiency of the proposed multi-surrogate approach when compared to traditional DE and a leading surrogatebased algorithm. The results illustrate superior computational performance while preserving solution quality across various benchmark functions
Influence of the reflective silver coating layer on the long-term durability of Ag nanoparticles/polymer assembly
No full textInternational audienceIn this paper, we have sought to determine if a reflective silver coating layer could prevent the degradation of a silver/acrylate polymer assembly and the loss of its optical property in an environment simulating sunlight. For this purpose, a multiscale approach from micro(nano) scale to macroscopic scale was applied. The overcomes of this investigation revealed that the metallic coating did not act as a barrier against photooxidation of the polymer matrix and silver nanoparticles, on contrary, catalysed the degradation process. The multiscale approach showed that not only the matrix underwent structural and architectural changes with the formation of oxidative products and volatile compounds due to chain scission reaction, but also the alteration of the metallic layer with the formation of defects at its surface, evidenced by optical and atomic force microscopy.An original approach using photo-DSC allowed to monitor the thermo-optical properties of the material. It pointed out the loss of the optical properties of the silver/polymer assembly during irradiation that was linked to the degradation of the acrylate polyme
COCALITE: A Hybrid Model COmbining CAtch22 and LITE for Time Series Classification
No full textInternational audienceTime series classification has achieved significant advancements through deep learning models; however, these models often suffer from high complexity and computational costs. To address these challenges while maintaining effectiveness, we introduce COCALITE, an innovative hybrid model that combines the efficient LITE model with an augmented version incorporating Catch22 features during training. COCALITE operates with only 4.7% of the parameters of the state-of-the-art Inception model, significantly reducing computational overhead. By integrating these complementary approaches, COCALITE leverages both effective feature engineering and deep learning techniques to enhance classification accuracy. Our extensive evaluation across 128 datasets from the UCR archive demonstrates that COCALITE achieves competitive performance, offering a compelling solution for resource-constrained environments
