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Multi-scale friction coefficient: From roughness to system computation using deep learning
No full textThe calculations were carried out on the openstack cloud in the mesocenter of the University of Lille.International audienceThe presence of surface defects (roughness, surface imperfections, profiles, etc.) in a contact inevitably leads to the modification of its local properties, such as the coefficient of friction. In railway wheelsets, this surface condition is crucial as it dictates appropriate fatigue design for the final use. However, these local phenomena are not well understood and require a real step back. Therefore, the aim of this paper is to propose a multiscale numerical strategy to better understand these phenomena. The multiscale strategy is divided into two steps. Initially, an analysis by the Discrete Element Method (DEM) modelling the interaction of generated rough surfaces is carried out to determine the coefficient of friction. In a second step, the results of DEM are introduced into a structural calculation where the enrichment of the coefficient of friction is done on each finite element contact. Given the wide variety of potential surface defects (size, distribution, height, etc.), a large number of DEM simulations is performed. A specially developed deep learning program is then used to account for these dispersions. The application targeted in this paper is the fitting of a wheel on a railway axle
Degenerate/singular beam-type equations: a controllability result
No full textThe paper deals with the controllability of a degenerate/singular beam-type equation in divergence or in non-divergence form. In particular, we assume that the degeneracy and the singularity are at the same boundary point and we consider a suitable control localized on the non degenerate boundary point. As a first step, we prove the existence of a solution for the homogeneous problems, then we prove some estimates on their energies. Thanks to them, we prove two observability inequalities, and using the notion of solution by transposition, we prove that the initial problems are null controllable
How Null Vector Performs in a Rational Bézier Curve with Mass Points
No full textInternational audienceThis article points out the kinematics in tracing a Bézier curve defined by control mass points. A mass point is a point with a non-positive weight, a non-negative weight or a vector with a null weight. For any Bézier curve, the speeds at endpoints can be modified at the same time for both endpoints. The use of a homographic parameter change allows us to choose any arc of the curve without changing the degree but not offer to change the speeds at both endpoints independently. The homographic parameter change performs weighted points with any non-null real number as weight and also vectors. The curve is thus called a rational Bézier curve with control mass points. In order to build independent stationary points at endpoints, a quadratic parameter change is required. Adding null vectors in the Bézier representation is also an answer. Null vectors are obtained when converting any power function in a rational Bézier curve and their inverse. The authors propose a new approach on placing null vectors in the representation of the rational Bézier curve. It allows us to break free from projective geometry where there is no null vector. The paper ends with some examples of known curves and some perspectives
Newton’s Philosophiae Naturalis Principia Mathematica Geneva Edition ([1739-1742]1822) Phil. Trans. A Special Issue and The State-of-the Art
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Young Professionals Events at the 2024 European Microwave Week [Young Professionals]
No full textInternational audienceProvides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers
An Intellectual Review: History and Historiography of Science and Technology in the Renaissance
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Révolutionner la surveillance vasculaire
No full textInternational audienceAs part of the European Interreg VASCULAI project, IEMN/UPHF is developing an electronic bracelet designed to measure key physiological parameters such as heart rate, blood pressure and blood flow. AI will be used to analyze this data and cross-reference it with databases to help prevent cardiovascular diseases.IEMN/UPHF's work focuses on building initial prototypes using commercial components to study data acquisition and transmission. The prototypes will then be enhanced by developing advanced electronic components based on innovative materials.Concerning the wireless communication, IEMN/UPHF is collaborating with BRIN, the National Research and Innovation Agency from Indonesia, to ensure secure and reliable data transmission over short and long distances
Deep Reinforcement Learning for Joint Time and Power Management in SWIPT-EH CIoT
No full textInternational audienceThis letter presents a novel deep reinforcement learning (DRL) approach for joint time allocation and power control in a cognitive Internet of Things (CIoT) system with simultaneous wireless information and power transfer (SWIPT). The CIoT transmitter autonomously manages energy harvesting (EH) and transmissions using a learnable time switching factor while optimizing power to enhance throughput and lifetime. The joint optimization is modeled as a Markov decision process under small-scale fading, realistic EH, and interference constraints. We develop a double deep Q-network (DDQN) enhanced with an upper confidence bound. Simulations benchmark our approach, showing superior performance over existing DRL methods
La mise en œuvre des sûretés en présence d’une procédure d’insolvabilité
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