Mechanical Design, Control Choices and first Return of Use of a Prosthetic Arm

In the world of upper limb prostheses, few companies dominate the majority of the market. They propose different kinds of hand, wrist and elbow prostheses but their control is often difficult to understand by the patients. We have decided to develop new myoelectric prosthetic arm (elbow, wrist and hand) by axing our development on the use of new technologies and facility of use for the patient. In this paper, we are explaining in details the different kinds of prostheses currently proposed to the amputees, their advantages and their drawbacks, the descriptions of the patients' needs and the possible improvements of the product. We will develop the designing choices of our prosthesis and the movements it can realize. Then we will explain the simplified control of the product by the patient and its first reactions. Finally, we will conclude by the news ideas and the next researches to concretize.Comment: 6 page

Quantification of Neural Network Uncertainties on the Hydrogeological Predictions by Probability Density Functions

International audienceThe risk of drought impacting the drinking water and agricultural production is worrying in the developed countries, especially in a changing climate context. To manage and prevent this phenomenon, real-time monitoring and predictive systems are emerging as the key solutions. In the field of artificial intelligence, neural networks are one of these predictive systems. This family of parameterized models is a composition of neuronal functions, which apply a non-linear transformation from their inputs to their outputs. These networks are able to learn a hydro(geo)logical system behaviour using a database composed of observed inputs (rainfall, evapotranspiration, etc.) and outputs (groundwater level, discharge, etc.), thanks to an algorithm minimizing a cost function between observed and simulated outputs. However, it remains difficult to assess the uncertainty generated by these models, possibly leading to misinterpretations by the end users. These uncertainties are mainly of three types. The first is related to the input data. Indeed, hydrosystems are surface elements whereas meteorological inputs are punctual elements. The interpolation error can, therefore, be significant because of the lack of knowledge between gauging stations. The second is the neural network model architecture itself. It is possible to deal with this source of uncertainty using regularization methods. Finally, the neural networks are submitted to uncertainties related to parameter initialization, before the training step. The initial parameters may have an important impact on the results. In this paper, we address the prediction of the Blavet groundwater level (Bretagne, France). In order to assess uncertainties, we will first focus on the parameters initialization of the model. Neuronal models are optimized using cross-validation and early stopping. Then, an ensemble model is realized, in which each member is the result of a unique set of parameters initialization. The purpose of the study is to define how many initializations are necessary to obtain a reasonable confidence interval for forecasts, with the smallest interval and the higher rate of observed points inside this interval. The best model will be determined using cross-validation scores thereby ensuring optimal robustness. We show that, in this case study, an ensemble model of 20 different initializations is sufficient to estimate uncertainty while preserving quality. In the second part, the resulting ensemble model will be used to estimate the global model uncertainty using probability density functions (pdf) applied to the distribution of groundwater level data and cross-validation scores of forecasts. It reveals that the groundwater level predictions are composed of two mixed distributions. Therefore, we will use the expectation-maximization algorithm (EM) to obtain parameters of mixed models. Mixed normal and mixed Gumbel laws, among five mixed distributions assessed, give the best groundwater distribution and are able to generate an abacus drawing uncertainty of mode

Relevance of the correlation between precipitation and the 0 C isothermal altitude for extreme flood estimation

Extreme floods are commonly estimated with the help of design storms and hydrological models. In this paper, we propose a new method to take into account the relationship between precipitation intensity (P) and air temperature (T) to account for potential snow accumulation and melt processes during the elaboration of design storms. The proposed method is based on a detailed analysis of this P-T relationship in the Swiss Alps. The region, no upper precipitation intensity limit is detectable for increasing temperature. However, a relationship between the highest measured temperature before a precipitation event and the duration of the subsequent event could be identified. An explanation for this relationship is proposed here based on the temperature gradient measured before the precipitation events. The relevance of these results is discussed for an example of Probable Maximum Precipitation-Probable Maximum Flood (PMPPMF) estimation for the high mountainous Mattmark dam catchment in the Swiss Alps. The proposed method to associate a critical air temperature to a PMP is easily transposable to similar alpine settings where meteorological soundings as well as ground temperature and precipitation measurements are available. In the future, the analyses presented here might be further refined by distinguishing between precipitation event types (frontal versus orographic)

Development of a methodology for extreme flood estimation

The development of a methodology for extreme flood estimation is the aim of the project CRUEX++. This project follows the CRUEX project which aimed at the development of a PMP-PMF methodology (PMP=Probable Maximum precipitation, PMF=Probable Maximum Flood). Numerous tools, models and methods have been developed during the last years. The goal of the CRUEX++ project is to combine and enrich these elements leading to a methodology for extreme flood estimations in order to verify dam safety. A PhD thesis has been initiated in 2012 to lead this project and to conclude on a final methodology

Swiss Rainfall Mass Curves and their Influence on Extreme Flood Simulation

Extreme flood estimates for dam safety are routinely obtained from hydrologic simulations driven by selected design storms. The temporal structure of such design storms can be obtained from Rainfall Mass Curves (RMCs), which are adimensionalized curves of the cumulative precipitation depth as a function of event duration. This paper assesses for the first time the spatialand temporal variability of observed RMCs for Switzerland, an Alpine region with complex topography. The relevance of the detected RMC variability for extreme flood estimation is illustrated based on an application to a high elevation catchment, the Mattmark dam catchment in the Swiss Alps. The obtained results underline that quantile RCMs represent a simple yet powerful tool to construct design storms for dam safety verification and that regional, seasonal and event-duration effects on RMCs are small enough to justify the use of a unique set of Swiss-wide quantile RMCs. The presented analysis could be refined in the future by explicitly accounting for orographic, convective or frontal precipitation events

New Approach to Identifying Critical Initial Conditions for Extreme Flood Simulations in a Semicontinuous Simulation Framework

Extreme flood simulation with synthetic extreme precipitation events raises unavoidable questions about the choice of initial conditions. State-of-the-art extreme flood estimation frameworks propose to address these questions with the help of semicontinuous modeling and reanalysis of simulated state variables. In this context, the present work proposes a new method for the selection of initial conditions for extreme flood simulation. The method is based on generating sets of initial conditions from the matrix of state variables corresponding to a long simulation run of the selected hydrological model. Two sets of initial conditions are obtained: a deterministic set composed of selected state variable quantiles and a stochastic set composed of state variable vectors randomly drawn from the complete state variable matrix. The extreme flood simulations corresponding to both sets are compared in detail, and the stochastic simulations are used in a sensitivity analysis to identify the dominant state variables and possible interactions. The aim hereby is to provide a tool to analyze the role of initial conditions and the importance to account for state variable interactions in extreme flood estimation. The proposed method is applied to probable maximum flood estimation for the Swiss Mattmark Dam catchment with a semilumped hydrological model. The obtained results for this case study show that for high flood peak quantiles, the initial soil saturation is dominating other state variables, and deterministic initial conditions are sufficient to generate extreme floods

Development of a methodology for extreme flood estimation

The development of a methodology for extreme flood estimation is the aim of the project CRUEX++. This project follows the CRUEX project which aimed at the development of a PMP-PMF methodology (PMP=Probable Maximum precipitation, PMF=Probable Maximum Flood). Numerous tools, models and methods have been developed during the last years. The goal of the CRUEX++ project is to combine and enrich these elements leading to a methodology for extreme flood estimations in order to verify dam safety. A PhD thesis has been initiated in 2012 to lead this project and to conclude on a final methodology

Flash floods forecasting using neural networks : generalizing to ungauged basins

Dans les régions méditerranéennes françaises, des épisodes pluvieux diluviens se produisent régulièrement et provoquent des crues très rapides et volumineuses que l'on appelle crues éclair. Elles font fréquemment de nombreuses victimes et peuvent, sur un seul évènement, coûter plus d'un milliard d'euros. Face à cette problématique, les pouvoirs publics mettent en place des parades parmi lesquelles la prévision hydrologique tient une place essentielle.C'est dans ce contexte que le projet BVNE (Bassin Versant Numérique Expérimental) a été initié par le SCHAPI (Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations) dans le but d'améliorer la prévision des crues rapides. Ces travaux s'inscrivent dans le cadre de ce projet et ont trois objectifs principaux : réaliser des prévisions sur des bassins capables de ces réactions qu'ils soient correctement jaugés, mal jaugés ou non jaugés.La zone d'étude choisie, le massif des Cévennes, concentre la majorité de ces épisodes hydrométéorologiques intenses en France. Ce mémoire la présente en détails, mettant en avant ses caractéristiques les plus influentes sur l'hydrologie de surface. Au regard de la complexité de la relation entre pluie et débit dans les bassins concernés et de la difficulté éprouvée par les modèles à base physique à fournir des informations précises en mode prédictif sans prévision de pluie, l'utilisation de l'apprentissage statistique par réseaux de neurones s'est imposée dans la recherche d'une solution opérationnelle.C'est ainsi que des modèles à réseaux de neurones ont été synthétisés et appliqués à un bassin de la zone cévenole, dans des contextes bien et mal jaugés. Les bons résultats obtenus ont été le point de départ de la généralisation à 15 bassins de la zone d'étude. A cette fin, une méthode de généralisation est développée à partir du modèle élaboré sur le bassin jaugé et de corrections estimées en fonction des caractéristiques physiques des bassins. Les résultats de l'application de cette méthode sont de bonne qualité et ouvrent la porte à de nombreux axes de recherche pour l'avenir, tout en démontrant encore que l'utilisation de l'apprentissage statistique pour l'hydrologie peut constituer une solution pertinente.In the French Mediterranean regions, heavy rainfall episodes regularly occur and induce very rapid and voluminous floods called flash floods. They frequently cause fatalities and can cost more than one billion euros during only one event. In order to cope with this issue, the public authorities' implemented countermeasures in which hydrological forecasting plays an essential role.In this contexte, the French Flood Forecasting Service (called SCHAPI for Service Central d'Hydrométéorologie et d'Appui à la Prévision des Inondations) initiated the BVNE (Digital Experimental Basin, for Bassin Versant Numérique Expérimental) project in order to enhance flash flood forecasts. The present work is a part of this project and aim at three main purposes: providing flash flood forecasts on well-gauged basins, poorly gauged basins and ungauged basins.The study area chosen, the Cévennes range, concentrates the major part of these intense hydrometeorological events in France. This dissertation presents it precisely, highlighting its most hydrological-influent characteristics.With regard to the complexity of the rainfall-discharge relation in the focused basins and the difficulty experienced by the physically based models to provide precise information in forecast mode without rainfall forecasts, the use of neural networks statistical learning imposed itself in the research of operational solutions.Thus, the neural networks models were designed and applied to a basin of the Cévennes range, in the well-gauged and poorly gauged contexts. The good results obtained have been the start point of a generalization to 15 basins of the study area.For this purpose, a generalization method was developed from the model created on the gauged basin and from corrections estimated as a function of basin characteristics.The results of this method application are of good quality and open the door to numerous pats of inquiry for the future, while demonstrating again that the use of statistical learning for hydrology can be a relevant solution

Prévision des Crues Éclair par Réseaux de Neurones : Généralisation aux Bassins non Jaugés

In the French Mediterranean regions, heavy rainfall episodes regularly occur and induce very rapid and voluminous floods called flash floods. hey frequently cause fatalities and can cost more than one billion euros during only one event. In order to cope with this issue, the public authorities’ implemented countermeasures in which hydrological forecasting plays an essential role. In this contexte, the French Flood Forecasting Service (called SCHAPI for Service Central d’Hydrométéorologie et d’Appui à la Prévision des Inondations) initiated the BVNE (Digital Experimental Basin, for Bassin Versant Numérique Expérimental) project in order to enhance flash flood forecasts. The present work is a part of this project and aim at three main purposes: providing flash flood forecasts on well-gauged basins, poorly gauged basins and engauged basins. The study area chosen, the Cévennes range, concentrates the major part of these intense hydrometeorological events in France. This dissertation presents it precisely, highlighting its most hydrological-influent characteristics. With regard to the complexity of the rainfall-discharge relation in the focused basins and the difficulty experienced by the physically based models to provide precise information in forecast mode without rainfall forecasts, the use of neural networks statistical learning imposed itself in the research of operational solutions. Thus, the neural networks models were designed and applied to a basin of the Cévennes range, in the well-gauged and poorly gauged contexts. The good results obtained have been the start point of a generalization to 15 basins of the study area. For this purpose, a generalization method was developed from the model created on the gauged basin and from corrections estimated as a function of basin characteristics. The results of this method application are of good quality and open the door to numerous pats of inquiry for the future, while demonstrating again that the use of statistical learning for hydrology can be a relevant solution.Dans les régions méditerranéennes françaises, des épisodes pluvieux diluviens se produisent régulièrement et provoquent des crues très rapides et volumineuses que l’on appelle crues éclair. Elles font fréquemment de nombreuses victimes et peuvent, sur un seul évènement, coûter plus d’un milliard d’euros. Face à cette problématique, les pouvoirs publics mettent en place des parades parmi lesquelles la prévision hydrologique tient une place essentielle. C’est dans ce contexte que le projet BVNE (Bassin Versant Numérique Expérimental) a été initié par le SCHAPI (Service Central d’Hydrométéorologie et d’Appui à la Prévision des Inondations) dans le but d’améliorer la révision des crues rapides. Ces travaux s’inscrivent dans le cadre de ce projet et ont trois objectifs principaux : réaliser des prévisions sur des bassins capables de ces réactions qu’ils soient correctement jaugés, mal jaugés ou non jaugés. La zone d’étude choisie, le massif des Cévennes, concentre la majorité de ces épisodes hydrométéorologiques intenses en France. Ce mémoire la présente en détails, mettant en avant ses caractéristiques les plus influentes sur l’hydrologie de surface. Au regard de la complexité de la relation entre pluie et débit dans les bassins concernés et de la difficulté éprouvée par les modèles à base physique à fournir des informations précises en mode prédictif sans prévision de pluie, l’utilisation de l’apprentissage statistique par réseaux de neurones s’est imposée dans la recherche d’une solution opérationnelle. C’est ainsi que des modèles à réseaux de neurones ont été synthétisés et appliqués à un bassin de la zone cévenole, dans des contextes bien et mal jaugés. Les bons résultats obtenus ont été le point de départ de la généralisation à 15 bassins de la zone d’étude. A cette fin, une méthode de généralisation est développée à partir du modèle élaboré sur le bassin jaugé et de corrections estimées en fonction des caractéristiques physiques des bassins. Les résultats de l’application de cette méthode sont de bonne qualité et ouvrent la porte à de nombreux axes de recherche pour l’avenir, tout en démontrant encore que l’utilisation de l’apprentissage statistique pour l’hydrologie peut constituer une solution pertinente

Choix d'une commande adaptée à l'utilisation d'une prothèse myoélectrique de membre supérieur

International audiencePour retrouver indépendance mais également dignité et pour avoir accès aux prothèses à prix plus abordables et à utilisation adaptée, nous avons réalisé une prothèse myoélectrique complète de membre supérieur en axant notre développement sur l'utilisation de nouvelles méthodes de conception et technologies de fabrication. Nous détaillons les moyens mis en oeuvre pour améliorer le design et la fonctionnalité de la prothèse. Nous abordons les points d'innovation mécanique et d'intégration de l'utilisateur en conception. Nous avons ainsi proposé un diagramme de fonctionnement de la prothèse mais surtout un moyen de commande mieux adapté à ses capacités physiques. Nous démontrons la nécessité d'utilisation d'un logiciel adapté pour une compréhension initiale du produit. L'utilisateur peut ainsi mieux adapter la prothèse à ses stimuli en agissant directement sur certains paramètres fonctionnels de cette dernière. La mise en confiance et l'adaptabilité de la prothèse avec son porteur sont donc essentiels