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Uncertainties in numerical predictions and experimental characterization of wind farm noise
No full textInternational audiencePIBE (Prévoir l’Impact du Bruit des Éoliennes – Predicting the impact of wind-turbine noise) is a research project funded by ANR (French National Research Agency) which main objectives are to better understand the noise levels created by wind-turbines and assess noise-reduction solutions. One topic of this project focuses on the propagation of uncertainty. Code_TYMPAN™ is an open-source software for calculating industrial noise in the environment. Its open architecture, implemented by a Python API, allows advanced users to build and solve models programmatically. This open architecture made possible the implementation of a parametrical computational tool (OCP – Outil de Calcul Paramétrique). This tool generates input data, launches the calculations with Code_TYMPAN™, generates and post-processes output data. Thus, sensitivity analysis and propagation of uncertainty were performed on an industrial test case. This development demonstrates the feasability of introducing uncertainties in acoustic engineering studies
High harmonic generation in solids driven by a high energy fiber laser source
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Shape and parameter identification by the linear sampling method for a restricted Fourier integral operator
No full textInternational audienceIn this paper we provide a new linear sampling method based on the same data but a different definition of the data operator for two inverse problems: the multi-frequency inverse source problem for a fixed observation direction and the Born inverse scattering problems. We show that the associated regularized linear sampling indicator converges to the average of the unknown in a small neighborhood as the regularization parameter approaches to zero. We develop both a shape identification theory and a parameter identification theory which are stimulated, analyzed, and implemented with the help of the prolate spheroidal wave functions and their generalizations. We further propose a prolate-based implementation of the linear sampling method and provide numerical experiments to demonstrate how this linear sampling method is capable of reconstructing both the shape and the parameter
A Riemannian multimodal representation to classify parkinsonism-related patterns from noninvasive observations of gait and eye movements
No full textInternational audienceParkinson's disease is a neurodegenerative disorder principally manifested as motor disabilities. In clinical practice, diagnostic rating scales are available for broadly measuring, classifying, and characterizing the disease progression. Nonetheless, these scales depend on the specialist's expertise, introducing a high degree of subjectivity. Thus, diagnosis and motor stage identification may be affected by misinterpretation, leading to incorrect or misguided treatments. This work addresses how to learn multimodal representations based on compact gait and eye motion descriptors whose fusion improves disease diagnosis prediction. This work introduces a noninvasive multimodal strategy that combines gait and ocular pursuit motion modalities into a geometrical Riemannian Neural Network for PD quantification and diagnostic support. Markerless gait and ocular pursuit videos were first recorded as Parkinson's observations, which are represented at each frame by a set of frame convolutional deep features. Then, Riemannian means are computed per modality using frame-level covariances coded from convolutional Deep features. Thus, a geometrical learning representation is adjusted by Riemannian means, following early, intermediate, and late fusion alternatives. The adjusted Riemannian manifold combines input modalities to obtain PD prediction. The geometrical multimodal approach was validated in a study involving 13 control subjects and 19 PD patients, achieving a mean accuracy of 96% for early and intermediate fusion and 92% for late fusion, increasing the unimodal accuracy results obtained in the gait and eye movement modalities by 6 and 8%, respectively. The proposed method was able to discriminate Parkinson's patients from healthy subjects using multimodal geometrical configurations based on covariances descriptors. The covariance representation of video descriptors is highly compact (with an input size of 625 and an output size of 256 (1 BiRe)), facilitating efficient learning with a small number of samples, a crucial aspect in medical applications.</div
Leveraging a realistic synthetic database to learn Shape-from-Shading for estimating the colon depth in colonoscopy images
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Long-term performance of innovation systems in East Asian developing countries: A structural analysis
No full textInternational audienceTo measure the performance of East Asian developing countries’ innovation systems, we adopt a product space approach by considering the import and export flows linking each country to its trading partners over the long term (1980-2018). We apply economic dominance theory to construct and analyze this space. Using an extension of Pavitt’s (1984) taxonomy proposed by Castellacci (2008), we classify manufactured goods into categories distinguished by the technological intensity required for their development, production, and diffusion. We propose indices of economic complexity to position countries in relation to each other, to identify significant developments, and to infer the capacity of a national innovation system to deepen a country’s insertion into the global value chain based on the mastery of complex technologies. In this way, we identify three groups of countries: those whose current innovation system favors moving up the value chain, those caught in the middle-income trap and struggling to renew their innovation system, and those without a clearly structured innovation system
Stratégies d’optimisation des hyper-paramètres de réseaux de neurones appliqués aux signaux temporels biomédicaux
No full textThis thesis focuses on optimizing the hyperparameters of convolutional neural networks (CNNs) in the medical domain, proposing an innovative approach to improve the performance of decision-making models in the biomedical field. Through the use of a hybrid approach, GS-TPE, to effectively adjust the hyperparameters of complex neural network models, this research has demonstrated significant improvements in the classification of temporal biomedical signals, such as vigilance states, from physiological signals such as electroencephalogram (EEG). Furthermore, by introducing a new DNN architecture, STGCN, for the classification of gestures associated with pathologies such as knee osteoarthritis and Parkinson's disease from video gait analysis, these works offer new perspectives for enhancing medical diagnosis and management through advancements in artificial intelligence.Cette thèse est axée sur l'optimisation des hyperparamètres des réseaux de neurones à convolution (CNN) dans le domaine médical, proposant une approche innovante visant à améliorer la performance des modèles décisionnels dans le domaine biomédical. Grâce à l'utilisation d'une approche hybride, GS-TPE, pour ajuster efficacement les hyperparamètres des modèles de réseaux de neurones complexes , cette recherche a démontré des améliorations significatives dans la classification des signaux biomédicaux temporels, à savoir les états de vigilance, à partir de signaux physiologiques tels que l'électroencéphalogramme (EEG). De plus, grâce à l'introduction d'une nouvelle architecture de DNN, STGCN, pour la classification de gestes associés à des pathologies telles que l'arthrose du genou et la maladie de Parkinson à partir d'analyses vidéo de la marche, ces travaux offrent de nouvelles perspectives pour l'amélioration du diagnostic et de la prise en charge médicale grâce aux progrès dans le domaine de l'IA
DVT Diagnosis Based on HOS and Scattering Operators
No full textInternational audienceDeep venous thrombosis (DVT) is an inappropriate formation of a thrombus in a deep vein. Three physio-pathological mechanisms can contribute, isolated or combined, to the development of a DVT: venous stasis, endothelial injury and hypercoagulability. The main aim of our project is to characterize the structure of DVT in order to identify one or more factors responsible for its formation. In this project, we developed feature extraction, identification and classification approaches based on scattering operators and statistical methods in order to characterize DVT or phlebitis. It is worth highlighting that the detachment and migration of the thrombosis formed in a vein from the lower extremities to the pulmonary arteries can cause sudden partial or total obliteration of the artery. This obliteration is identified as a major complication called Pulmonary Embolism. In our project, we are looking for the relationship between DVT epidemiology and the thrombus structure. We extract features from ultrasound images using a scattering operator and high-order statistics. Then, the obtained features are analyzed using several classification technics to find the main cause of the DVT or the presence of PE. Experimental results are presented and discusse
High Cycle Fatigue Performance of Bare 300M Steel by Self-Heating Tests Under Cyclic Loadings
No full textInternational audience300M, an ultra -high strength steel used in aeronautics for landing gears, is generally shot-peened and coated with High Velocity Oxygen Fuel sprayed WC-10Co-4Cr to improve corrosion resistance and tribological behavior. In the literature, few process parameters have been tested to characterize the effect of surface integrity on fatigue properties. The self-heating method under cyclic loadings is used to quickly determine fatigue properties by coupling the fatigue mechanisms with dissipation mechanisms. This paper focusses on the methodology and results obtained on bare 300M. After implanting and validating a test and post-processing protocol, a self-heating model has been applied. The model shows a good description of the self-heating curve and a great correlation with conventional fatigue data . Mean properties as its scattering have been determined by self-heating tests. Then, geometric and loading parameters have been tested, such as the loading ratio and the coupon volume, showing various influence on self-heating curves.</div
Médiation dans le numérique, une pédagogie gagnante ?
No full textInternational audienceL’accompagnement en sciences et technologies à l’école primaire (ASTEP) est une forme de médiation scientifique qui associe élèves, corps enseignant et personnes scientifiques extérieures à l’école. Notre recherche porte sur les modalités d’ajustement, de reconfigurations et de déplacements identitaires du corps enseignant et des tiers scientifiques qui interviennent en classe. La médiation modifie le rôle et la place du corps enseignant et implique des tiers médiateurs qui font évoluer le triangle pédagogique. Notre article interroge les pratiques, les produits et les acquis du collectif de cette forme pédagogique. Il en ressort que le collectif constitué par la médiation aide les médiatrices à se sentir légitimes face au numérique, tout comme il revitalise l’école publique face aux transitions numériques. L’activité de médiation, elle-même accompagnée d’un changement du corps enseignant, a un caractère émancipateur