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Robust Adaptive Control for Fully Actuated Hexa-Rotors Without Precise Knowledge of Rotor Poses
No full textInternational audienceThe control of fully actuated hexa-rotors is highly dependent on their physical parameters, especially those describing the rotors’positions and orientations. Uncertainties in these parameters significantly affect the performance of classical control approaches,particularly in small drones, where manufacturing tolerances amplify these uncertainties. To address this challenge, we proposea novel adaptive and robust control strategy that compensates for parameter uncertainties and external disturbances withoutrequiring precise prior knowledge of the rotor poses. Unlike existing methods, our approach explicitly incorporates motor dynam-ics into the control design, resulting in a more realistic and implementable framework. Using Lyapunov-based stability analysis,we demonstrate the global asymptotic stability of the proposed control system under parameter uncertainties and disturbances.Extensive simulations validate the efficacy of our method, showcasing superior tracking performance and robustness comparedto conventional controllers. This work represents a significant step toward enabling fully actuated multi-rotor UAVs to perform inreal-world scenarios with uncertain and dynamic environments
Jumeaux numériques dans la gestion de la chaîne logistique : portée et problèmes méthodologiques
No full textInternational audienceThis paper investigates the implementation of Digital Twins (DTs) in Supply Chain Management (SCM), highlighting the gap between their conceptual promise and practical applications. DTs are recognised for their potential to correct real-time deviations and anticipate and prevent disruptions as they emerge; however, operational deployments in SCM remain rare. Numerous studies mislabel simulation models or Digital Shadows (DSs) as DTs, blurring essential distinctions. To address this issue, this paper adopts a praxeological approach that aims to situate observed implementations within their decision-making context. From this perspective, we propose a novel methodological framework that integrates the historical evolution of supply chain information and decision systems with a multidimensional analysis grid, outlining technological progress from simulators to DSs and DTs. This grid evaluates core DT functionalities (simulation, detection, anticipation and correction) across 51 empirical case studies, providing granular insights into maturity levels and AI enhanced patterns. The results show that most models support monitoring and decision-making, but only 16% achieve closed-loop capabilities typical of fully functional DTs, mainly in closed systems. In contrast, open systems still depend on human intervention, although AI can increasingly support such contexts. This praxeological approach provides critical and evidence-based snapshots of actual implementations. It offers researchers a clarified conceptual lens and practitioners empirically grounded guidance, outlining avenues for future inquiry and a reflective framework to guide the development and governance of DTs in supply chains.Cet article examine la mise en œuvre des jumeaux numériques (DT) dans la gestion de la chaîne logistique (SCM), et souligne l’écart entre leurs fondements conceptuels et leurs applications opérationnelles. Les DT sont reconnus pour leur potentiel à corriger les écarts en temps réel et à anticiper et prévenir les perturbations dès leur apparition ; cependant, leur déploiement opérationnel dans la SCM reste rare. De nombreuses études qualifient à tort les modèles de simulation ou les Digital Shadows (DS) de DT, brouillant ainsi les frontières. Pour remédier à ce problème, cet article adopte une approche praxéologique visant à situer les mises en œuvre observées dans leur contexte décisionnel. Dans cette perspective, nous proposons un cadre méthodologique fondé sur l'évolution historique des systèmes d'information et de décision de la chaîne logistique, présenté dans une grille d'analyse multidimensionnelle, s'appuyant sur les progrès technologiques allant des simulateurs jusqu'aux DS et aux DT. Cette grille évalue comment sont traitées les fonctionnalités essentielles des DT (simulation, détection, anticipation et correction) à partir de 51 études de cas empiriques, fournissant des informations détaillées sur les niveaux de maturité et les les améliorations potentielles apportées par l'IA. Les résultats montrent que la plupart des modèles prennent en charge la surveillance et la prise de décision, mais que seuls 16 % d'entre eux atteignent les capacités de rétroaction typiques des DT pleinement fonctionnels, principalement dans des systèmes fermés. En revanche, les systèmes ouverts dépendent toujours, au final, de l'intervention humaine, bien que l'IA puisse de plus en plus prendre en charge ces contextes. Cette approche praxéologique fournit des analyses critiques fondées sur des cas de mise en œuvre réels. Elle offre aux chercheurs une perspective conceptuelle clarifiée et aux praticiens des conseils fondés sur l'expérience, décrivant des pistes de recherche futures ainsi qu'un cadre de réflexion pour guider le développement et la gouvernance des DT au sein des chaînes logistiques
Vascular Geometry Drives Stroke Risk in Sickle Cell Disease10.1002/ajh.70184
No full textInternational audienceSickle cell disease (SCD) is the leading cause of stroke in children and young adults, primarily due to cerebral vasculopathy (CV) occurring within the first decade of life. The main risk factor for CV is elevated blood velocity in intracranial arteries, contributing to stenosis formation in very young children. This study addresses three key questions: (i) the relationship between hemoglobin levels and intracranial blood velocities in SCD patients, (ii) additional factors contributing to elevated velocity beyond anemia, and (iii) the presence of flow anomalies. To investigate these aspects, biological and transcranial Doppler data from pediatric and adult SCD patients were analyzed. An image-based in silico modeling approach was also developed to simulate blood flow in the internal carotid, anterior cerebral, and middle cerebral arteries of SCD patients, of different age classes, and prior to any possible stenosis. Analysis revealed that while anemia is a recognized CV risk factor, it does not fully explain elevated velocities, as no significant correlation was found in children under five. In in silico simulations, young patients reached pathological arterial intracranial velocities at physiological flow rates, whereas adults remained below risk thresholds even at high flow rates. Pathological velocities were primarily observed in distal internal carotid arteries, where stenoses often develop. High flow rates, small arterial diameters, and pronounced curvatures led to extreme velocities and complex flow, likely causing endothelial damage and promoting CV progression. These findings enhance understanding of hemodynamic mechanisms underlying SCD-related stroke risk, paving the way for improved predictive models and early interventions
Additive Manufacturing of real-scale carotid artery models: The content–container interaction in sickle cell disease-related cerebral vasculopathy
No full textAbstract Sickle cell disease–related cerebral vasculopathy depends on patient-specific vascular geometry and hemodynamics that are not captured by conventional experimental systems or animal models. To address this limitation, we developed an additively manufactured artery scaffold representing the container and integrated it into a controlled fluidic circuit reproducing physiological flow profiles, pressures, and viscosities, the content. Validation with clinical imaging confirmed the anatomical accuracy and flow fidelity of the additively manufactured models. Mechanosensitive cells, including endothelial cells and platelets, were incorporated into the model for biological analysis. This study serves as a proof of concept demonstrating how the Container–Content model can enhance the mechanistic understanding of vascular biology under physiologically relevant conditions. The ethically responsible platform bridges computational simulations and in vitro experimentation, offering a versatile foundation for investigating cerebrovascular complications in sickle cell disease and advancing the field of personalized medicine
The M-Tensor Format: Optimality in High Dimensional Regression for Nonlinear Models with Scarce Data
No full textWe present a nonlinear regression framework based on tensor algebra tailored to high dimensional contexts where data is scarce. We exploit algebraic properties of a partial tensor product, namely the m-tensor product, to leverage structured equations with separated variables. The proposed method combines kernel properties along with tensor algebra to prevent the curse of dimensionality and tackle approximations up to hundreds of parameters while avoiding the fixed point strategy. This formalism allows us to provide different regularization techniques fit for low amount of data with a high number of parameters while preserving well-known matrix-based properties. We demonstrate complexity scaling on a general benchmark and dynamical systems to show robustness for engineering problems and ease of implementation
Mental practice improves pass accuracy in elite rugby players
No full textMental practice has been shown to improve motor performance; however, its effects in elite sports, particularly under constraints, remains underexplored. In this study, we examine the impact of mental practice, preceded by imagined or virtual game scenarios, on pass accuracy in elite rugby players. Seventy-five players from national and regional talent development teams participated in this study. They were divided into three groups: control (CTRL), motor imagery (MI), and virtual reality and motor imagery (VRMI) groups. All players completed pre- and post-tests assessing pass accuracy at three distances (10, 15 and 20 meters) under two conditions (with and without time constraints). Between the tests, the MI and VRMI groups performed three mental practice sessions over three different days. Each session consisted of two blocks of imagined (MI) or virtually observed (VRMI) stressful game scenarios (yellow card or try conceded) followed by imagined passes at each distance. The CTRL group did not engage in any practice during this period. Our results showed that mental practice improved pass accuracy under no constraints, with both MI and VRMI greater than CTRL (p<0.03 in all). Vividness of motor imagery improved over training sessions for MI and VRMI (p=0.01), but VRMI did not further enhance imagery vividness compared to MI alone. Under time constraints, pass accuracy at 20m declined during the pre-test for all groups (p=0.003). However, both mental practice groups failed to counteract this decrease at post-test. In conclusion, three mental practice sessions effectively enhanced pass accuracy under no constraints but did not mitigate the negative effects of time constraints on accuracy. Coaches and practitioners might consider implementing mental practice to further improve motor accuracy and reduce physical workload
Thermodynamic modeling and experimental study of protective barriers against carbon diffusion during spark plasma sintering process
No full textInternational audienceDuring spark plasma sintering (SPS), the contact between the graphite tooling and the powder results in carbon diffusion. Graphite foils coated with a physical vapor deposition (PVD) film represent a promising solution to overcome this issue. In the present work, simulation and experiments were combined to understand the barrier effectiveness against carbon diffusion of a titanium PVD film deposited on graphite foils used during SPS of an iron powder. In non-equilibrium processes, simulation alone is insufficient to describe the multiple diffusion scenarios. On the other hand, experimental measurements are not always relevant. The approach adopted in this work enabled the prediction of the potential phases that form as a function of diffusion depth. Several scenarios were proposed, helping to explain the influence of the film thickness. This methodology, applied to the C-Ti-Fe system, can be extended to other film-substrate couples, reducing the number of tests and the associated costs
Mental Workload Measurement in Helicopter Maintenance: Comparing Virtual Reality Simulation and Real‐World Conditions
No full textInternational audienceDesigning tomorrow's maintenance systems with a human-centered approach is crucial to ensure optimal safety and performance. A key prerequisite for achieving this goal is to anticipate operators' cognitive behavior early in the design cycle. This study aims to determine whether the combination of mental workload measurement tools, including subjective, behavioral, and physiological measures, can detect comparable levels of cognitive effort during helicopter maintenance tasks in both real-world and virtual reality conditions. We analyzed data from 10 participants who performed four maintenance tasks of varying complexity on a helicopter, including component removal and installation. These efforts were measured using subjective scales (NASA-TLX), performance indicators (completion time), and cardiovascular data (heart rate, heart rate variability). Our observations revealed similar completion times and higher NASA-TLX scores for complex tasks, regardless of the real and virtual environment. Regarding cardiovascular data, the time-domain heart rate variability indicators showed consistent trends across both real and virtual environments. in both real and virtual settings. This research marks a significant step forward in the multidimensional, anticipatory measurement of mental workload in maintenance within a realistic industrial context.</div
A statistical decomposition of geometric imperfections applied to robust topology optimisation
No full textInternational audienceThe present paper deals with the integration of geometric imperfections due to the additive manufacturing process in the framework of a density-based topology optimisation method. Specifically, the model presented in this work splits the geometric imperfections into modes using a strategy based on a statistical decomposition of local geometric patterns. The aleatory uncertainty related to the lack of repeatability of the manufacturing process is propagated through the Monte Carlo method and a robust topology optimisation problem formulation is proposed. The method is applied to the classic problem of the minimisation of a cost function formulated as a weighted sum of the mean and the standard deviation of the structural compliance subject to a constraint on the volume. The effectiveness of the approach is tested on 2D benchmark problems taken from the literature and complemented by a sensitivity analysis on the convergence and accuracy of Monte Carlo estimates. Moreover, a modification of cost function weights allows to support decision-making depending on uncertainty fearfulness. The proposed method has proven effective in mitigating uncertainties arising from geometric imperfections during the design activity.</div
Enhancing User Experience in Virtual Reality Through Optical Flow Simplification with the Help of Physiological Measurements: Pilot Study
No full textInternational audienceThe use of virtual reality (VR) has made significant advancements, and now it is widely used across a range of applications. However, consumers’ capacity to fully enjoy VR experiences continues to be limited by a chronic problem known as cybersickness (CS). This study explores the feasibility of mitigating CS through geometric scene simplification combined with electroencephalography (EEG)-based monitoring. According to the sensory conflict theory, this issue is caused by the discrepancy between the visually induced self-motion (VIMS) through immersive displays and the real motion the vestibular system detects. While prior mitigation strategies have largely relied on hardware modifications or visual field restrictions, this paper introduces a novel framework that integrates geometric scene simplification with EEG-based neurophysiological activity to reduce VIMS during VR immersion. The proposed framework combines EEG neurophysiology, allowing us to monitor users’ brainwave activity and cognitive states during virtual immersion experience. The empirical evidence from our investigation shows a correlation between CS manifestation and neural activation in the parietal and temporal lobes. As an experiment with 15 subjects, statistical differences were significantly different with P= 0.001 and large effect size η2=0.28, while preliminary trends suggest lower neural activation during simplified scenes. Notably, a decrease in neural activation corresponding to reduced optic flow (OF) suggests that VR environment simplification may help attenuate CS symptoms, providing preliminary support for the proposed strategy