HAL Portal UTC Université de Technologie de Compiègne
Not a member yet
11652 research outputs found
Sort by
Feature Selection Effect on Context‐Aware Teacher‐Support Systems
No full textInternational audienceIn multi‐criteria decision‐making (MCDM), structuring the problem by defining relevant alternatives and criteria is a critical prerequisite for effective analysis. In this paper, this foundational phase is addressed within the multifaceted context influencing teacher performance, which represents a crucial task for effective decision‐making in education. However, traditional approaches often struggle to capture the complex interaction between the different features distributed over the teacher's living environment, work setting and emotional state, which represent a large and complex set of potential decision criteria. These features, represented as criteria, are essential for a comprehensive understanding of a teacher's context, represented as alternatives. The proposed approach introduces a formal, ontology‐driven approach to this problem structuring task. We investigate the impact of feature selection on representing the multidimensional context of teachers (the alternatives), both individually and collectively. We propose a novel, unsupervised feature selection approach based on feature variance, which leverages a teacher context ontology to identify the most salient criteria (features) for subsequent analysis. By employing an importance‐based threshold, the approach efficiently eliminates features with minimal explanatory power, leading to a more parsimonious and interpretable representation. Additionally, the proposed approach demonstrates superior performance according to the selected context in several key areas, providing a consistent, reliable set of representing features across different variations of data. Moreover, the proposed approach generates interpretable structures, such as lattices, to facilitate informed decision‐making
Probabilistic Time Slot Leasing in TDMA-Based IoT Networks for Enhanced Channel Utilization
No full textInternational audienc
Local Linear Regression for Functional Ergodic Data with Missing at Random Responses
No full textInternational audienceIn this article, we develop a novel kernel-based estimation framework for functional regression models in the presence of missing responses, with particular emphasis on the Missing At Random (MAR) mechanism. The analysis is carried out in the setting of stationary and ergodic functional data, where we introduce apparently for the first time a local linear estimator of the regression operator. The principal theoretical contributions of the paper may be summarized as follows. First, we establish almost sure uniform rates of convergence for the proposed estimator, thereby quantifying its asymptotic accuracy in a strong sense. Second, we prove its asymptotic normality, which provides the foundation for distributional approximations and subsequent inference. Third, we derive explicit closed-form expressions for the associated asymptotic variance, yielding a precise characterization of the limiting law. These results are obtained under standard structural assumptions on the relevant functional classes and under mild regularity conditions on the underlying model, ensuring broad applicability of the theory. On the methodological side, the asymptotic analysis is exploited to construct pointwise confidence regions for the regression operator, thereby enabling valid statistical inference. Furthermore, a comprehensive set of simulation experiments is conducted, demonstrating that the proposed estimator exhibits superior finite-sample predictive performance when compared to existing procedures, while simultaneously retaining robustness in the presence of missingness governed by MAR mechanisms
Preparation of chitosan/lignin nanoparticles-based nanocomposite films with high-performance and improved physicochemical properties for food packaging applications
No full textInternational audienceChitosan (CH)-based composite films have attracted increasing attention as promising green food packaging materials due to their biodegradability and ease of fabrication. Additionally, lignin (LN) has been widely used as additive for chitosan-based films to improve their physicochemical properties. In this study, a series of composite films made of chitosan nanoparticles (NCH) as a matrix and alkali lignin nanoparticles (LNPs) as functional filler were prepared. The NCH-LNPs composite films exhibited a more uniform appearance and enhanced crystallinity compared to NCH-LN films. The maximum pyrolysis temperature of NCH-LNPs films, determined by TG, reached 309 °C. Moreover, the antioxidant capacity of NCH-LNPs film was 1.5 and 3.4 times higher than those of NCH-LN and NCH films, respectively. The tensile modulus of NCH-LNPs films increased by 8.9 % and 36.5 %, while the tensile strain decreased by 16.0 % and 52.8 % compared to NCH and NCH-LN films, respectively. Finally, the suitability of prepared films for food preservation was studied on grape and cheese samples. The ability of NCH-LNPs films to inhibit lipid peroxidation in cheese was 2 times higher than that of NCH-LN films. These results showed that the improvement of physicochemical properties of NCH-based films by LNPs was significantly higher than that observed with LN
Optimal Multi-Objective Adaptive Coordinated Control via Hierarchical MPC Framework for Autonomous Vehicles
No full textInternational audienceThis paper introduces a novel minimum-order, multi-objective centralized MPC control architecture for autonomous vehicles. The contributions are fourfold. First, we propose a hierarchical architecture that integrates path-tracking, speed control, and stability control based on a minimum-order predictive model. Second, stability control is dynamically activated and relaxed using an adaptive weight, based on a stability index. Third, speed control employs an adaptive weight to improve robustness, reduce overshoot and oscillations, and enhance energy efficiency. Fourth, path-tracking control is enhanced with an adaptive weighting scheme that considers lateral error, road adherence, and curvature to ensure smooth convergence and prevent oscillations, while also improving accuracy under uncertain and low-adherence conditions. The architecture is validated in a joint simulation between Simulink/Matlab and SCANeR Studio vehicle dynamics simulator. Our findings demonstrate the effectiveness of the architecture in enhancing stability and comfort at low runtime, while maintaining path-tracking precision and speed control robustness, at high speeds, high curvature, and low adhesion
Recognition of Group Actions from Individual Actions in Brainstorming Sessions
No full textInternational audienceIn this paper we show how an artificial intelligence system is able to recognize actions of a group of people during remote meetings. It is based on the combination of several basic detections. We explain why it is a very important task in order to help a coach manage teams. In particular, we explain how the recognition of discussions allows to measure the collaboration between participants or possibly their isolation. We detail the action of Eye Contact where two people look at each other during a discussion. We present the algorithm of the module of our system in charge of the recognition of an Eye Contact action and the difficulties raised by this module. We conclude with the heuristics rules that would allow to detect higher level of group actions from individual actions in brainstorming sessions.</div
Robust Control Architecture for Fleet Formation Based on LMI and Artificial Potential Fields
No full textInternational audienceFormation control plays a crucial role in many areas where multiple mobile robots need to be coordinated. In this paper, a formation's control architecture for a homogeneous fleet of three unicycles is proposed. The control architecture is robust by design when using the Linear Matrix Inequality (LMI) based approach. To include collision avoidance into the system, artificial potential fields are inserted in the control algorithm. The closed-loop system performance is evaluated in numerical simulations with different scenarios in motion or still. Simulation results demonstrate that with the proposed controller the agents reach the formation despite the disturbances and, according to the hierarchy defined between them, the unicycles deviate their trajectories to avoid collisions.</div
Tension-based reconfigurable multi-agent formation for aerial load transportation
No full textInternational audienceThis paper proposes a trajectory tracking controller for a cable-suspended load transported by n unmanned aerial vehicles based on Euler angle states. This is accomplished using the resultant tension on the load as a virtual input and then distributing it to each vehicle, for which low-level controllers generate the correct thrust and torque values. Furthermore, a leader-follower-based control is proposed to securely change the number of agents carrying the load. Numerical simulations are presented to validate the controllers. Finally, experiments show that the proposed method effectively makes the load perform as expected and allows one to change the formation configuration without severely impacting the load navigation
Identification of the differential and synergic lipotoxic patterns of oleic acid, palmitic acid, and their mixture in 3D HepG2/C3A tissue using liver‐on‐chip technology
No full textInternational audienceThe metabolic dysfunction‐associated steatotic liver disease (MASLD, previously formerly known as non‐alcoholic fatty liver disease, NAFLD) is rapidly expanding worldwide in parrallel with the obesity pandemic. Dietary fatty acids including oleic (OA) and palmitic acids (PA) contribute to the hepatic intracellular triglyceride accumulation, and are therefore thought to play key roles in disease development and progression. Taking advantage of the cutting‐edge organ‐on‐chip technology that mimics the 3D organ dynamic environment, we aimed at investigating the role of OA, PA and a 2:1 OA/PA mixture on the growth and function of the HepG2/C3A, a liver cell line model, over 2 and 7 days. OA supported sustained cell growth, leading to dense 3D tissues, whereas PA and OA exposure did not affect cell proliferation. PA treatment downregulated the GLUT2, INSRA, SREBP1, FASN, mRNA levels indicating a lipid metabolism perturbation in our model. The cell dysfunction caused by OA, PA, and OA/PA was associated with an increase in reactive oxygen species (ROS) production over time. Intracellular lipid monitored by oil red O was higher in cells exposed to OA than in the control ones and cells cultured with PA. Our data confirm the role of fatty acids on the growth and dysfunction of HepG2/C3A cells, and highlight distinct mechanisms through which OA and PA exert their effects