Contrôle de l'équilibre des humains virtuels

Ce chapitre aborde le problème de l'analyse de l'équilibre et de la synthèse par la ommande de la coordination des mouvements d'humains virtuels pour la simulation réaliste d'activités physiques quotidiennes ou professionnelles. nous discutons dans un premier temps les notions d'équilibre postural et de stabilité de cet équilibre dans le cadre particulier des mannequins numériques. Nous introduisons un modèle mécanique pour des mannequins en interaction physique avec l'environnement. À partir d'une formulation générale du problème de l'équilibre, nous examinons un certain nombre de moyens proposés pour caractériser et quantifier la stabilité de l'équilibre des systèmes mécaniques contraints. Nous introduisons la notion de perturbation admissible pour la dynamique posturale vis-à-vis des contraintes de persistance et de non-glissement des appuis. Enfin, nous proposons des techniques de synthèse par la commande de fonctions motrices pour une coordination de l'ensemble du système postural et de manipulation satisfaisant explicitement les contraintes d'équilibre des appuis

Assimilation de données ensembliste et couplage de modèles hydrauliques 1D-2D pour la prévision des crues en temps réel. Application au réseau hydraulique "Adour maritime"

Les inondations sont un risque naturel majeur pour les biens et les personnes. Prévoir celles-ci, informer le grand public et les autorités sont de la responsabilité des services de prévision des crues. Pour ce faire ils disposent d'observations in situ et de modèles numériques. Néanmoins les modèles numériques sont une représentation simplifiée et donc entachée d'erreur de la réalité. Les observations quant à elle fournissent une information localisée et peuvent être également entachées d'erreur. Les méthodes d'assimilation de données consistent à combiner ces deux sources d'information et sont utilisées pour réduire l'incertitude sur la description de l'état hydraulique des cours d'eau et améliorer les prévisisons. Ces dernières décennies l'assimilation de données a été appliquée avec succès à l'hydraulique fluviale pour l'amélioration des modèles et pour la prévision des crues. Cependant le développement de méthodes d'assimilation pour la prévision en temps réel est contraint par le temps de calcul disponible et par la conception de la chaîne opérationnelle. Les méthodes en question doivent donc être performantes, simples à implémenter et peu coûteuses. Un autre défi réside dans la combinaison des modèles hydrauliques de dimensions différentes développés pour décrire les réseaux hydrauliques. Un modèle 1D est peu coûteux mais ne permet pas de décrire des écoulement complexes, contrairement à un modèle 2D. Le simple chainage des modèles 1D et 2D avec échange des conditions aux limites n'assure pas la continuité de l'état hydraulique. Il convient alors de coupler les modèles, tout en limitant le coût de calcul. Cette thèse a été financée par la région Midi-Pyrénées et le SCHAPI (Service Central d'Hydrométéorolgie et d'Appui à la Prévisions des Inondations) et a pour objectif d'étudier l'apport de l'assimilation de données et du couplage de modèles pour la prévision des crues. Elle se décompose en deux axes : Un axe sur l'assimilation de données. On s'intéresse à l'émulation du filtre de Kalman d'Ensemble (EnKF) sur le modèle d'onde de crue. On montre, sous certaines hypothèses, qu'on peut émuler l'EnKF avec un filtre de Kalman invariant pour un coût de calcul réduit. Dans un second temps nous nous intéressons à l'application de l'EnKF sur l'Adour maritime avec un modèle Saint-Venant. Nous en montrons les limitations dans sa version classique et montrons les avantages apportés par des méthodes complémentaires d'inflation et d'estimation des covariances d'erreur d'observation. L'apport de l'assimilation des données in situ de hauteurs d'eau sur des cas synthétiques et sur des crues réelles a été démontré et permet une correction spatialisée des hauteurs d'eau et des débits. En conséquence, on constate que les prévisions à court terme sont améliorées. Nous montrons enfin qu'un système de prévisions probabilistes sur l'Adour dépend de la connaissance que l'on a des forçages amonts ; un axe sur le couplage de modèles hydrauliques. Sur l'Adour 2 modèles co-existent : un modèle 1D et un modèle 2D au niveau de Bayonne. Deux méthodes de couplage ont été implémentées. Une première méthode, dite de "couplage à interfaces", combine le 1D décomposé en sous-modèles couplés au 2D au niveau frontières liquides de ce dernier. Une deuxième méthode superpose le 1D avec le 2D sur la zone de recouvrement ; le 1D force le 2D qui, quand il est en crue, calcule les termes d'apports latéraux pour le 1D, modélisant les échanges entre lit mineur et lit majeur. Le coût de calcul de la méthode par interfaces est significativement plus élevé que celui associé à la méthode de couplage par superposition, mais assure une meilleure continuité des variables. En revanche, la méthode de superposition est immédiatement compatible avec l'approche d'assimilation de données sur la zone 1D. L'apport, sur la zone 2D, de l'assimilation des observations in situ des hauteurs d'eau sur la zone 1D a été mis en évidence pour un fort événement de crue de la Nive en Janvier 2014. ABSTRACT : Floods represent a major threat for people and society. Flood forecasting agencies are in charge of floods forecasting, risk assessment and alert to governmental authorities and population. To do so, flood forecasting agencies rely on observations and numerical models. However numerical models and observations provide an incomplete and inexact description of reality as they suffer from various sources of uncertianties. Data assimilation methods consists in optimally combining observations with models in order to reduce both uncertainties in the models and in the observations, thus improving simulation and forecast. Over the last decades, the merits of data assimilation has been greatly demonstrated in the field of hydraulics and hydrology, partly in the context of model calibration or flood forecasting. Yet, the implementation of such methods for real application, under computational cost constraints as well as technical constraints remains a challenge. An other challenge arises when the combining multidimensional models developed over partial domains of catchment. For instance, 1D models describe the mono-dimensional flow in a river while 2D model locally describe more complex flows. Simply chaining 1D and 2D with boundary conditions exchange does not suffice to guarantee the coherence and the continuity of both water level and discharge variables between 1D and 2D domains. The solution lies in dynamical coupling of 1D and 2D models, yet an other challenge when computational cost must be limited. This PhD thesis was funded by Midi-Pyrénées region and the french national agency for flood forecasting SCHAPI. It aims at demonstrating the merits of data assimilation and coupling methods for floof forecasting in the framework of operational application. This thesis is composed of two parts : A first part dealing with data assimilation. It was shown that, under some simplifying assumptions, the Ensemble Kalman filter algorithm (EnKF) can be emulated with a cheaper algorithm : the invariant Kalman filter. The EnKF was then implemented ovr the "Adour maritime" hydraulic network on top of the MASCARET model describing the shallow water equations. It was found that a variance inflation algorithm can further improve data assimlation results with the EnKF. It was shown on synthetical and real cases experiments that data assimilation provides an hydraulic state that is in great agreement with water level observations. As a consequence of the sequential correction of the hydraulic state over time, the forecasts were also greatly improved by data assimilation over the entire hydraulic network for both assimilated and nonassimilated variables, especially for short term forecasts. It was also shown that a probabilistic prediction system relies on the knowledge on the upstream forcings ; A second part focusses on hydraulic models coupling. While the 1D model has a great spatial extension and describes the mono-dimensional flow, the 2D model gives a focus on the Adour-Nive confluence in the Bayonne area. Two coupling methods have been implemented in this study : a first one based on the exchange of the state variables at the liquid boundaries of the models and a second one where the models are superposed. While simple 1D or chained 1D-2D solutions provide an incomplete or discontinuous description of the hydraulic state, both coupling methods provide a full and dynamically coherent description of water level and discharge over the entire 1D-2D domain. On the one hand, the interface coupling method presents a much higher computational cost than the superposition methods but the continuity is better preserved. On the other hand, the superposition methods allows to combine data assimilation of the 1D model and 1D-2D coupling. The positive impact of water level in-situ observations in the 1D domain was illustrated over the 2D domain for a flood event in 2014

Parametric Kalman filter for chemical transport models

A computational simplification of the Kalman filter (KF) is introduced – the parametric Kalman filter (PKF). The full covariance matrix dynamics of the KF, which describes the evolution along the analysis and forecast cycle, is replaced by the dynamics of the error variance and the diffusion tensor, which is related to the correlation length-scales. The PKF developed here has been applied to the simplified framework of advection–diffusion of a passive tracer, for its use in chemical transport model assimilation. The PKF is easy to compute and computationally cost-effective than an ensemble Kalman filter (EnKF) in this context. The validation of the method is presented for a simplified 1-D advection–diffusion dynamics

Data assimilation with the 1D hydraulic code Mascaret and multi-dimensional coupling between Telemac and Mascaret

Water Qualit

DATA ASSIMILATION ON A FLOOD WAVE PROPAGATION MODEL : EMULATION OF A KALMAN FILTER ALGORITHM

International audienceThis study describes the assimilation of synthetically-generated river water level observations in a flood wave propagation model. For this approach to be applied in the framework of real-time flood forecasting, the cost of the data assimilation procedure, mostly related to the estimation of the background error covariance matrix, should be bound. An Ensemble Kalman Filter (EnKF) algorithm is applied, with a steady observation network, to demonstrate how the assimilation modifies the background correlation function at the observation point. It is shown that an initially Gaussian correlation function turns into an anisotropic function at the observation point, with a shorter correlation length-scale downstream of the observation point than upstream, and that the variance of the error in the water level state is significantly reduced downstream of the observation point. The covariance matrix resulting from the EnKF is then used as an invariant background error covariance matrix for a series of successive Best Linear Unbiased Estimation (BLUE) algorithms which emulate an EnKF at a lower cost. This study shows how the background error covariance matrix can be computed off-line, with an advanced algorithm, and then used with a cheaper algorithm for real-time application

Ensemble-based data assimilation for operational flood forecasting – On the merits of state estimation for 1D hydrodynamic forecasting through the example of the “Adour Maritime” river

This study presents the implementation and the merits of an Ensemble Kalman Filter (EnKF) algorithm with an inflation procedure on the 1D shallow water model MASCARET in the framework of operational flood forecasting on the “Adour Maritime” river (South West France). In situ water level observations are sequentially assimilated to correct both water level and discharge. The stochastic estimation of the background error statistics is achieved over an ensemble of MASCARET integrations with perturbed hydrological boundary conditions. It is shown that the geometric characteristics of the network as well as the hydrological forcings and their temporal variability have a significant impact on the shape of the univariate (water level) and multivariate (water level and discharge) background error covariance functions and thus on the EnKF analysis. The performance of the EnKF algorithm is examined for observing system simulation experiments as well as for a set of eight real flood events (2009–2014). The quality of the ensemble is deemed satisfactory as long as the forecast lead time remains under the transfer time of the network, when perfect hydrological forcings are considered. Results demonstrate that the simulated hydraulic state variables can be improved over the entire network, even where no data are available, with a limited ensemble size and thus a computational cost compatible with operational constraints. The improvement in the water level Root-Mean-Square Error obtained with the EnKF reaches up to 88% at the analysis time and 40% at a 4-h forecast lead time compared to the standalone model

Bragg-Scattering conversion at telecom wavelengths towards the photon counting regime

9openopenKatarzyna Krupa; Alessandro Tonello; Victor Kozlov; Vincent Couderc; Philippe Di Bin; Stefan Wabnitz; Alain Barthelemy; Laurent Labonte; Sebastien TanzilliKatarzyna, Krupa; Alessandro, Tonello; Kozlov, Victor; Vincent, Couderc; Philippe Di, Bin; Wabnitz, Stefan; Alain, Barthelemy; Laurent, Labonte; Sebastien, Tanzill

Brain alterations associated with overweight evaluated by body mass index or body fat index in an elderly population: the PROOF study

Background/objectivesObesity is a complex health issue in which the brain plays a role yet to be determined, especially in the elderly. Indeed, in the ageing population, the balance between fat and lean mass is different; thus, the co-influence between the brain and obesity may differ between the elderly and younger subjects. Our main goal is thus to explore the relationship between the brain and obesity using two different approaches to measure obesity: body mass index (BMI) and an index centred on fat mass, the body fat index (BFI).Subjects/methodsAmong the 1,011 subjects of the PROOF population, 273 subjects aged 75 years underwent 3D magnetic resonance imaging as well as dual-energy X-ray absorptiometry to assess fat mass. Voxel-based morphometry was used to explore the local differences in brain volume with obesity.ResultsHigher BMI and BFI were associated with higher grey matter (GM) volume in the left cerebellum. Higher BMI and BFI were mainly associated with higher white matter volume in the left and right cerebellum and near the right medial orbital gyrus. Higher BMI was also associated with higher GM volume in the brainstem, whereas higher BFI was associated with higher GM volume in the left middle temporal gyrus. No decrease in white matter was associated with BMI or BFI.ConclusionIn the elderly, the relationship between the brain and obesity does not depend on the marker of obesity. Supra-tentorial brain structures seem to be slightly associated with obesity, whereas the cerebellum seems to be one of the key structures related to obesity

Data Assimilation And Multidimensional Model Coupling On The Adour Catchment, South-West Of France

This study focuses on the hydrodynamic modelling of the tidally influenced “Adour maritime” catchment located in South-West of France. The local SPC is in charge of operational flood-forecasting and for that purpose, the 1D hydraulic software MASCARET developed at EDF is used. Due to various sources of uncertainties on the hydraulic parameters and hydrological forcing, the free-run is imperfect and should be corrected for improved forecast of extreme events. An Ensemble Kalman Filter algorithm was implemented on top of the hydraulic model using the OpenPalm coupler to assimilate hourly water level in-situ data from observing stations. The water-level and discharge state is sequentially corrected thus providing and improved initial state for short to medium range forecast (3h-6h). The impact of the data assimilation analysis on the entire network and for the non-assimilated variables is investigated for several major flood events. It was also shown that the reliability of the forecast closely relates to the number of members used in the EnKF algorithm. In the present study, about 20 members are necessary. As these members can be run in parallel, the cost of the ensemble-based assimilation remains compatible with real-time flood-forecasting constraints. The corrected 1D simulation provides the boundary conditions for a limited-area 2D model. The TELEMAC software, developed at EDF, is used to represent the flow for the confluence between Nive and Adour rivers (in the center of the Bayonne city) as well as in the Plaine d\u27Ansot (located upstream of Bayonne) where the flow is no longer mono-dimensional. The 1D (with data assimilation) and the 2D model overlap, they are coupled in order to build a data-driven high-fidelity model for the Adour catchment for operational use in the framework of flood-forecasting