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Simulation of fluid driven crack propagation along metal/elastomer interface: Application to the numerical analysis of the rubber cord adhesion inflation test
No full textInternational audienceThe Rubber Cord Adhesion Inflation Test (RCAIT) has been proposed as an alternative technique to more standard pull out protocols such as H, T or pull out tests for characterizing the adhesion between cord/cable reinforcement and rubber. During this test, a fluid is injected in between a wire and a rubber cylindrical envelope to provoke the interface separation once a critical pressure is reached. A simple energy balance analysis is used to evaluate the critical strain energy release rate, GC, which drives the crack propagation from measurable quantities. However, some assumptions should be assessed to ensure reliable GC evaluation. Then, a predictive finite elements simulation of the RCAIT is proposed to simulate the fluid driven crack nucleation and propagation process along the rubber cord interface. These results are compared with the ones obtained from the RCAIT simplified analysis
Multi-performance assessment of hydrate slurries for secondary refrigeration in a modelled industrial case study
No full textInternational audienceStudies have shown that the use of Phase Change Materials (PCMs) in secondary refrigeration could improve the efficiency of refrigeration systems. In this study, the focus is made on CO 2 hydrate slurry. Despite the demonstration of the interest of such a system, there is a barrier to their industrial development. The work presented in this paper analyses the behaviour of hydrate slurries in an industrial context, here the air conditioning of supermarket, and proposes a multi-performance evaluation of hydrate slurry systems. Starting from the modelling of the rheological and thermal properties of the slurries established by experimental studies in the laboratory, their theoretical energy behaviour in an industrial case can be analysed. In addition, the environmental, economic and operational performance of the refrigeration system are simulated. This innovative architecture using hydrate slurries is compared to systems already implemented in industry such as centralised direct expansion systems using HFC as primary refrigerant and secondary loop using glycol water
Interlaminar strength characterization of Tensylon® UHMWPE under multimodal loads
No full textInternational audienc
SIAMESE NEURAL NETWORK FOR AUTOMATIC TARGET RECOGNITION USING SYNTHETIC APERTURE RADAR
No full textInternational audienceAutomatic Target Recognition (ATR) is an interest problem in various application fields (security, surveillance, automotive, environment, medicine, communications, remote sensing, ...). Thus, SAR (Synthetic Aperture Radar) and ISAR (Inverse Synthetic Aperture Radar) radar images provide rich visual information about the observed radar target. From these radar images, several methods have been proposed to meet the expected requirements in several application domains, including target recognition, which is one of the main issues addressed in the present work. Traditional standard image classification techniques are not suitable for efficient classification of SAR images due to the limited data available in some classes (unbalanced data). To solve these problems, we introduce a deep learning model, the Siamese network with multiclass classification, built from a pre-trained model to improve the model performances on unbalanced classes. To evaluate the proposed method, the MSTAR dataset is used. The proposed method improves the recognition rate from 95,18% to 97.16%
Machine Learning Assessment of Anti-Spoofing Techniques for GNSS Receivers
No full textInternational audienceGlobal Navigation Satellite Systems (GNSS) are often the target of malicious attacks and interferences, mainly spoofing, thus posing a significant threat to both civilian and military equipment, and therefore necessitating effective detection and identification of such attacks. In this 'Work-in-Progress' paper, we propose the application of Machine Learning neural networks, a methodology proven highly effective in fields like cyberattack detection, to identify spoofing events across various scenarios. Our approach consists in computing non-time related metrics from a dataset of known spoofed signals, using the observables and signal-level measurements provided by a GNSS software receiver. The training is validated on both spoofed and clean scenarios to ensure a comprehensive approach. Furthermore, we provide a description of the feature's importance in the decision-making process of the model
Optimizing Variational Circuits for Higher-Order Binary Optimization
No full text6 pages plus references. Conference: IEEE QCE 2023International audienceVariational quantum algorithms have been advocated as promising candidates to solve combinatorial optimization problems on near-term quantum computers. Their methodology involves transforming the optimization problem into a quadratic unconstrained binary optimization (QUBO) problem. While this transformation offers flexibility and a ready-to-implement circuit involving only two-qubit gates, it has been shown to be less than optimal in the number of employed qubits and circuit depth, especially for polynomial optimization. On the other hand, strategies based on higher-order binary optimization (HOBO) could save qubits, but they would introduce additional circuit layers, given the presence of higher-than-two-qubit gates. In this paper, we study HOBO problems and propose new approaches to encode their Hamiltonian into a ready-to-implement circuit involving only two-qubit gates. Our methodology relies on formulating the circuit design as a combinatorial optimization problem, in which we seek to minimize circuit depth. We also propose handy simplifications and heuristics that can solve the circuit design problem in polynomial time. We evaluate our approaches by comparing them with the state of the art, showcasing clear gains in terms of circuit depth
Sensitivity of Shipborne GNSS Estimates to Processing Modeling Based on Simulated Dataset
No full textInternational audienceThe atmospheric water vapor is commonly monitored from ground Global Navigation Satellite System (GNSS) measurements, by retrieving the tropospheric delay under the Zenith Wet Delay (ZWD) component, linked to the water vapor content in the atmosphere. In recent years, the GNSS ZWD retrieval has been performed on shipborne antennas to gather more atmospheric data above the oceans for climatology and meteorology study purposes. However, when analyzing GNSS data acquired by a moving antenna, it is more complex to decorrelate the height of the antenna and the ZWD during the Precise Point Positioning (PPP) processing. Therefore, the observation modeling and processing parametrization must be tuned. This study addresses the impact of modeling on the estimation of height and ZWD from the simulation of shipborne GNSS measurements. The GNSS simulation is based on an authors-designed simulator presented in this article. We tested different processing models (elevation cut-off angle, elevation weighting function, and random walk of ZWD) and simulation configurations (the constellations used, the sampling of measurements, the location of the antenna, etc.). According to our results, we recommend processing shipborne GNSS measurements with 3° of cut-off angle, elevation weighting function square root of sine, and an average of 5 mm·h−1/2 of random walk on ZWD, the latter being specifically adapted to mid-latitudes but which could be extended to other areas. This processing modeling will be applied in further studies to monitor the distribution of water vapor above the oceans from systematic analysis of shipborne GNSS measurements
Optimizing the trench shaped film cooling design
No full textInternational audienceIn this numerical study an optimized trench film cooling design is determined using a Bayesian algorithm and neural network trained RANS model. Three objective functions were considered, the area-averaged film cooling efficiency, spatial standard deviation of film cooling efficiency and hot gas ingestion into the trench. Nine geometrical design parameters were varied to allow for a 3D trench shape and to find an optimal trench design based on the initial parametrization of the trench. Jet-engine like inflow boundary conditions with respect to turbulence intensity and length scales were applied. The investigated momentum ratios (I) were 1 and 8 at a main flow Reynolds number (ReD) of 2500. For each design the steady state Reynolds Averaged Navier-Stokes (RANS) equations were solved using the commercial Computational Fluid Dynamics (CFD) code Ansys Fluent V2022 R1. The turbulence model coefficients of the generalized k−ω (GEKO) model were tuned to approximate the time-averaged 3D temperature field from a predictive Large Eddy Simulation (LES) and trained by a neural network to improve the prediction capability. The tuned GEKO model shows improved agreement with experimental data of a literature case compared to the standard GEKO model. With this tuned RANS model optimized trench designs are found and validated by additional LES's. The optimized designs include angled side walls to improve former trench designs, particularly in mitigating hot gas entrainment into the trench, which could be omitted almost entirely
Agents artificiels autotelic et sociaux : formation et exploitation de conventions culturelles chez les agents artificiels autonomes incarnés
No full textOne of the fundamental goals of Artificial Intelligence (ai) is to design embodied autonomous agents that can evolve in various environments, perform a multitude of tasks and interact with humans. To this end, ai researchers employ various approaches, with two primary methods standing out: developmental robotics and standard ai paradigms. While developmental robotics models agents’ cognitive development in simplified environments, standard AI paradigms focus on algorithmic contributions in precise and technical benchmarks. In this thesis, we extend upon recent calls to bridge these two fields and investigate the role of cultural conventions in the development of artificial agents using state-of-the-art ai algorithms. This research leverages work from developmental psychology and focuses on two crucial aspects of human development, namely autotelic and social learning. The former enables agents to form open-ended repertoires of skills by inventing and pursuing their own goals while the latter enables them to communicate, cooperate, teach, and organize their thoughts. Our contributions are organized around two fundamental scientific questions: 1) the formation of cultural conventions within populations of artificial agents, and 2) the exploitation of cultural conventions in their cognitive development. The first part of this manuscript deals with the formation of cultural conventions. It builds on recent studies in the field of emergent communication to propose two computational studies. The first one investigates the formation of cultural conventions in the ecological context where artificial agents communicate via a graphical sensory-motor channel. The second one draws inspiration from experimental semiotics and studies the emergence of communication in the architect-builder problem: a novel interactive learning paradigm where agents have asymmetries of information and affordances which makes the application of standard Multi-Agent Reinforcement Learning impossible. The second part focuses on the exploitation of cultural conventions. Inspired by the pioneering work of Vygotsky and other psychologists we first introduce the Vygotskian Autotelic ai Framework. This framework enables Reinforcement Learning agents to internalize social interactions in order to transform their cognitive abilities enabling them to form abstract representations, achieve systematic generalization, and creatively explore their environment. Following this conceptual contribution, we propose two computational studies. The first one explores the role of inductive biases in the language grounding problem where agents need to align their physical experience of the world with linguistic inputs provided by social partners. Our final computational contribution introduces the imagine agent: a Vygotskian autotelic agent that converts linguistic descriptions given by a social partner into targetable goals. imagine leverages language productivity and systematic generalization to grow an open-ended repertoire of skills in a creative way.L'un des objectifs fondamentaux de l'Intelligence Artificielle (IA) est de concevoir des agents autonomes incarnés capables d'évoluer dans divers environnements, d'accomplir une multitude de tâches et d'interagir avec les humains. À cette fin, les chercheurs en IA utilisent différentes approches, avec deux méthodes principales se distinguant : la robotique développementale et les paradigmes d'IA standard. Alors que la robotique développementale modélise le développement cognitif des agents dans des environnements simplifiés, les paradigmes d'IA standard se concentrent sur les contributions algorithmiques dans des benchmarks précis et techniques. Dans cette thèse, nous prolongeons les appels récents à combler ces deux domaines et examinons le rôle des conventions culturelles dans le développement d'agents artificiels en utilisant des algorithmes d'IA de pointe.Cette recherche s'appuie sur le travail de la psychologie du développement et se concentre sur deux aspects cruciaux du développement humain, à savoir l'apprentissage autotélique et social. Le premier permet aux agents de former des répertoires de compétences ouverts en inventant et en poursuivant leurs propres objectifs, tandis que le second leur permet de communiquer, de coopérer, d'enseigner et d'organiser leurs pensées.Nos contributions sont organisées autour de deux questions scientifiques fondamentales : 1) la formation de conventions culturelles au sein de populations d'agents artificiels, et 2) l'exploitation de conventions culturelles dans leur développement cognitif.La première partie de ce manuscrit traite de la formation de conventions culturelles. Elle s'appuie sur des études récentes dans le domaine de la communication émergente pour proposer deux études computationnelles. La première étudie la formation de conventions culturelles dans le contexte écologique où les agents artificiels communiquent via un canal sensorimoteur graphique. La seconde s'inspire de la sémiotique expérimentale et étudie l'émergence de la communication dans le problème de l'architecte-constructeur : un nouveau paradigme d'apprentissage interactif où les agents ont des asymétries d'information et des affordances qui rendent l'application de l'apprentissage par renforcement multi-agent standard impossible.La seconde partie se concentre sur l'exploitation de conventions culturelles. Inspirés par les travaux pionniers de Vygotsky et d'autres psychologues, nous introduisons d'abord le framework Vygotskien d'IA autotélique. Ce framework permet aux agents d'apprentissage par renforcement d'intérioriser les interactions sociales afin de transformer leurs capacités cognitives, leur permettant de former des représentations abstraites, d'atteindre une généralisation systématique et d'explorer leur environnement de manière créative. À la suite de cette contribution conceptuelle, nous proposons deux études computationnelles. La première explore le rôle des biais inductifs dans le problème d'ancrage de langage où les agents doivent aligner leur expérience physique du monde avec les entrées linguistiques fournies par des partenaires sociaux. Notre dernière contribution computationnelle introduit l'agent IMAGINE : un agent autotélique Vygotskien qui convertit les descriptions linguistiques données par un partenaire social en objectifs atteignables. Imagine tire parti de la productivité linguistique et de la généralisation systématique pour développer de manière créative un répertoire de compétences ouvert