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Linear Wavelet-Based Estimators of Partial Derivatives of Multivariate Density Function for Stationary and Ergodic Continuous Time Processes
No full textInternational audienceIn this work, we propose a wavelet-based framework for estimating the derivatives of a density function in the setting of continuous, stationary, and ergodic processes. Our primary focus is the derivation of the integrated mean square error (IMSE) over compact subsets of Rd, which provides a quantitative measure of the estimation accuracy. In addition, a uniform convergence rate and normality are established. To establish the asymptotic behavior of the proposed estimators, we adopt a martingale approach that accommodates the ergodic nature of the underlying processes. Importantly, beyond ergodicity, our analysis does not require additional assumptions regarding the data. By demonstrating that the wavelet methodology remains valid under these weaker dependence conditions, we extend earlier results originally developed in the context of independent observations
Enhancing adhesive properties with unmodified and phosphate-modified chitin in particleboard production
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Integrating Constraints via Probabilistic Circuits
No full textInternational audienceOne of the recent advances in the domain of Probabilistic Circuits (PCs) is the introduction of methodologies for incorporating constraints into the represented distributions, thereby enabling the integration of external sources of information. In this paper, we investigate the extension of such paradigms to other classes of probabilistic models. In particular, we consider four representative models: continuous mixtures of tractable probabilistic models, Bayesian networks, Chow-Liu trees, and decision trees. We show that principled extensions of the techniques developed for PCs can be effectively applied to these models, thereby facilitating constrained optimization within a broader class of probabilistic frameworks
Machine-learning enhanced predictors for accelerated convergence of partitioned fluid-structure interaction simulations
No full textInternational audienceStable partitioned techniques for simulating unsteady fluid-structure interaction (FSI) are known to be computationally expensive when high added-mass is involved. Multiple coupling strategies have been developed to accelerate these simulations, but often use predictors in the form of simple finite-difference extrapolations. In this work, we propose a non-intrusive data-driven predictor that couples reduced-order models of both the solid and fluid subproblems, providing an initial guess for the nonlinear problem of the next time step calculation. Each reduced order model is composed of a nonlinear encoder-regressor-decoder architecture and is equipped with an adaptive update strategy that adds robustness for extrapolation. In doing so, the proposed methodology leverages physics-based insights from high-fidelity solvers, thus establishing a physics-aware machine learning predictor. Using three strongly coupled FSI examples, this study demonstrates the improved convergence obtained with the new predictor and the overall computational speedup realized compared to classical approaches
La ville numérique sans ses algorithmes : fabrique urbaine ordinaire, acteurs, institutions
No full textInternational audienceAs research on cities and digital technology gains prominence in urban studies, this position critically reassesses the common emphasis on the extraordinary nature of futuristic projects often associated with digital technology in urban imaginaries. Rather than viewing the city merely as a construct governed by algorithms and reduced to technical objects – such as sensors, mobile applications, or platforms its innovations, and its emblematic projects which are often short-lived or unfinished, this article encourages an exploration of how digital technology is negotiated, interpreted, and integrated into everyday life. This includes not only professional and institutional practices but also the daily urban practices of citizens. By focusing on the ordinary urban fabric, we can examine how digital technologies are transforming relationships among institutions, businesses, and residents in city production, as well as how these changes are inscribed within broader urban and territorial dynamics. This perspective also invites attention to the agency of the diverse actors engaged in urban production, acknowledging that their relationship with technology is never overdetermined.À l’heure où les travaux sur la ville et le numérique occupent une place de plus en plus importante dans la recherche urbaine, cet article de positionnement propose de faire un pas de côté par rapport aux contributions portant sur l’exceptionnalité de projets futuristes souvent associés au numérique dans les imaginaires urbains. Plutôt qu’une ville gouvernée par les algorithmes, résumée à ses objets techniques (capteurs, applications mobiles ou encore plateformes), à ses innovations et projets emblématiques, souvent éphémères ou inaboutis, nous invitons à observer la manière dont le numérique se négocie, s’interprète et s’incorpore au quotidien, dans des pratiques professionnelles, institutionnelles et dans des pratiques habitantes qui concourent à fabriquer la ville. Appréhender la fabrique urbaine ordinaire permet ainsi de comprendre comment les technologies numériques redéfinissent les rapports entre institutions, entreprises et habitants dans la construction de la ville et comment ces transformations s’inscrivent dans des dynamiques urbaines et territoriales. Ce parti pris est aussi une invitation à se concentrer sur l’agentivité des acteurs de la fabrique urbaine dans leur diversité, dont le rapport à la technologie n’est jamais surdéterminé
Full state quaternion-based observer control for multirotor aerial grasping
No full textInternational audienceThis paper presents an enhanced observer control strategy for multirotor aerial grasping. Unlike previous approaches, which focused solely on translational dynamics, this method incorporates dual observers-one for the translational subsystem and another for the rotational dynamics. By leveraging quaternions, the proposed control framework provides a singularity-free representation of orientation while naturally decoupling rotational and translational dynamics. This allows the system to be treated as fully actuated in both position and orientation, improving disturbance rejection and compensating for torques induced by off-center or asymmetrically shaped objects during grasping. A passive, non-actuated gripper further enhances the drone's ability to interact with objects in realworld scenarios. Experimental validations confirm the robustness and adaptability of the proposed approach, demonstrating its effectiveness in handling dynamic variations in mass and torque while maintaining stable flight.</div
OBIWAN project: from organic waste to chemical components via biogas – an integrated (bio)chemical carbon cycle with CO2 recovery
No full textInternational audienceOBIWAN, project supported by Interreg France-Wallonie-Vlaaderen Program and funded by European Regional DevelopmentFund (ERDF), aims to convert organic waste streams into advancedchemicals and sustainable aviation fuels. After an initial anaerobicdigestion process for biogas production, which yields a mixture of CH4and CO2, a subsequent chemical conversion will transform the CO2 intohigher-value products. The excess carbon will then be valorized assolid carbon for applications such as gas purification or as a filler fortires. In this way, OBIWAN aims to develop a technology that mitigatesclimate change, not only by preventing greenhouse gas emissions butalso by transforming these gases into valuable products. The keyinnovations the project will focus on include ensuring a consistentproduction and stable composition of biogas, as well as demonstratinghydrogen production at an industrially relevant scale
Exploring the synergy between chemistry and non-conventional sintering processes: a first step towards the eco-design of functional ceramics and devices
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Complexité et IA
No full textInternational audienceArtificial intelligence already has a long history, but it is also very topical. Generative artificial intelligence, by introducing a new way of mobilising well-known scientific and technical principles, has introduced innovations in tools and a breakthrough in usage. From now on, the machine will speak to us, write to us and respond to us. So much so that, like the various technical developments of the past, we are beginning to extrapolate not only the future performance of these techniques but also their ability to imitate and surpass that of humans. The aim of this thesis is to study this technical history from the perspective of complexity theories. AI was in fact born out of cybernetics, which consisted of an initial approach to complexity by unifying the natural and the artificial, the mechanical and the biological, the cognitive and the non-knowing via theories combining circular causality, negative feedback, teleology and new formal models such as formal neural networks. The history of AI does not coincide with that of complexity theories, but the links have always been present; for example, while chaos theory and dynamical systems theory have not had a direct impact, they have nevertheless strongly influenced certain computational paradigms. Physics often provides principles of effectivity which, when related to their algorithmic side, give rise to computational paradigms (genetic algorithms are a case in point). The aim of this thesis is to identify how AI fits into the history of complexity and helps to provide both principles for approaching it and illustrations of it. The perspective that will be studied in particular is to understand how AI is not constituted as a paragon or competitor of human knowledge, but rather its technical mediation. The challenge is then to grasp the very nature of the complexity constituted by the human-IA complex, the human in its relationship to and use of intellectual techniques when they become computational and automated.L'intellligence artificielle a déjà une longue histoire mais également une actualité intense. L'intelligence artificielle générative, en introduisant une nouvelle manière de mobiliser des principes scientifiques et techniques bien connus, a introduit des innovations dans les outils et une rupture dans les usages. Désormais, la machine nous parle, écrit, et nous répond. A tel point que, à l'instar des différentes évolutions techniques du passé, on se prend à extrapoler non seulement les performances futures de ces techniques mais aussi leur capacité à imiter et dépasser celles des humaines. L'objectif de cette thèse est d'étudier cette histoire technique sous l'angle des théories de la complexité. L'IA est en effet née de la cybernétique qui a consisté en une première approche de la complexité en unifiant le naturel et l'artificiel, le mécanique et le biologique, le cognitif et le non connaissant via des théories alliant la causalité circulaire, le rétroaction négative, la téléologie ainsi que des modèles formels nouveaux pour comme les réseaux de neurones formels. L'histoire de l'IA ne coïncide pas avec celle des théories de la complexité mais les liens ont toujours été présents ; ainsi, si la théorie du chaos et des systèmes dynamiques n'a pas eu d'impact direct mais elle a cependant fortement influencé certains paradigmes calculatoires. Il est fréquent que la physique fournit des principes d'effectivité qui, rapportés à leur versant algorithmique, donnent lieu a des paradigmes calculatoires (un exemple est celui des algorithmes génétiques). L'objectif de la thèse est d'une part de cerner comment l'IA s'inscrit dans l'histoire de la complexité et contribue à fournir tant des principes pour l'aborder que des illustrations de cette dernière. La perspective qui sera étudiée en particulier consistera à comprendre comment non pas l'IA se constitue comme parangon ou concurrent de la connaissance humaine mais plutôt sa médiation technique. L'enjeu est alors de saisir la nature propre de la complexité constituée par le complexe humain-IA, l'humain dans son rapport et son usage des techniques intellectuelles quand elles deviennent calculatoires et automatisées
Planning urban EV charging stations with GIS based multi-criteria decision making
No full textInternational audienceWith the accelerating adoption of electric vehicles (EV), strategic planning of charging infrastructure has become a critical component of sustainable urban mobility. The current EV charging points can be inadequately equipped to meet the rapidly increasing battery charging demand of EVs. Therefore, in this paper, a Geographic Information System (GIS)-based Multiple-Criteria Decision Analysis (MCDA) approach is adopted to help providing a solution of charging points location problem. Furthermore, the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used to select the optimal charging station site. A five-step approach is developed: determination of 14 criteria across four perspectives, spatial analysis using QGIS, criteria prioritization with AHP, site ranking in use of TOPSIS, and site capacity estimation. By integrating these methodologies, optimal charging point locations can be identified while ensuring effective deployment and utilization of EV charging infrastructure. Through a complete and considerate analysis, the study identifies the three most appropriate alternative locations for EVs with/without including photovoltaic (PV) system. The results derived from the proposed methodology were cross-referenced with the spatial distribution of existing charging stations. Although most of the current installations coincided with the priority zones identified by the model, the analysis also revealed additional high-potential locations that are not currently equipped. A distinctive advantage of the methodology lies in its integrative framework, which not only enables optimal site selection but also incorporates an evaluation of feasible energy sources, with a particular focus on the potential for solar energy utilization