The data hungry home

It's said that the pleasure is in the giving, not the receiving. This belief is validated by how humans interact with their family, friends and society as well as their gardens, homes, and pets. Yet for ubiquitous devices, this dynamic is reversed with devices as the donors and owners as the recipients. This paper explores an alternative paradigm where these devices are elevated, becoming members of Data Hungry Homes, allowing us to build relationships with them using the principles that we apply to family, pets or houseplants. These devices are developed to fit into a new concept of the home, can symbiotically interact with us and possess needs and traits that yield unexpected positive or negative outcomes from interacting with them. Such relationships could enrich our lives through our endeavours to “feed” our Data Hungry Homes, possibly leading us to explore new avenues and interactions outside and inside the home

Utilisation d'une méthode basée sur l'intelligence artificielle pour modéliser le comportement de barrages en remblai

International audienceDams reliability and safety must be controlled through their whole life to guarantee the safety of the people and assets located at their downstream, to insure they fulfil the functions for which they were built and to safeguard these infrastructures. As a consequence, it is necessary to develop methods and tools for the control of the reliability and safety of dams. In particular, tasks related to the assessment, the diagnosis and the prognosis of dam behaviour are very important to prevent accidents and determine corrective actions. The analysis and control of the dams' behaviour, with the aim of controlling reliability and safety, require the development of models that represent the dynamics of dams. These models have to represent the complexity of the dam behaviour while being easy to handle and to instantiate to the whole set of dams that present different features. They must admit as inputs, miscellaneous data. Finally, these models must be adequate to carry out diagnosis or prognosis tasks to improve dam reliability and safety. In this paper, a multi-model approach is proposed to consider the past, the present and the future behaviour of the dam. Our approach is based on four models: a structural model describing relations between components, a functional model describing the relations which determine the affectation of a possible value to a variable, a behavioural model describing the states of the system and the discrete events that represent the state transitions and a perception model that defines the process and its operating modes. These models were validated by a panel of experts. The method was applied to a real case study concerning a dam which suffered from an internal erosion mechanism. These models were developed to carry out diagnosis, assessment, prognosis and control tasks. Currently, our works show that the developed models are relevant to perform assessment and diagnosis tasks. Moreover, the multi-model approach presents the advantage to facilitate the knowledge representation and handling as well as the results communication to owners or reservoir operators. One of the perspectives is to use the models to forecast dam reliability and safety through tim

Évaluation et diagnostic de l'état de barrages basés sur une approche multi-modèles

National audienceL'analyse et le contrôle du comportement des barrages en vue de maîtriser leur fiabilité et leur sécurité nécessitent la mise en ½uvre de modèles prenant en compte le passé (diagnostic), le présent (évaluation) et le futur de l'ouvrage (prédiction et proposition d'actions correctives). Nous avons montré au cours de travaux antérieurs qu'il était possible de modéliser le comportement de l'ouvrage par une approche multi-modèle qui rend possible la prise en compte de ces trois dimensions temporelles. Nous nous proposons dans cet article de montrer l'intérêt de cette modélisation pour l'évaluation des barrages en remblai et le diagnostic de leur comportement. / The analysis and control of dams' behaviour, with the aim of controlling reliability and safety, require the development of models that can take into account the present life of the dam (reliability and safety assessment), its past (diagnosis) and its future (prognosis and corrective actions). Previous works we carried out showed that it is possible to model the dam behaviour using a multi-model approach that takes into account the three temporal dimensions. In this paper, we propose to show the interest of such a modelling for the assessment and diagnosis of embankment dams

Modélisation du comportement d'un barrage en remblai par approche multi modèles

[Departement_IRSTEA]RE [TR1_IRSTEA]RIE / SECURENational audienceThis paper presents a modeling method of dynamic system like hydraulic dams based on dated observation for diagnosis. This method called TOM4D, proposes to make explicit the implicit models used by experts when they diagnose a system. This method is based on knowledge used by experts and their level of abstraction. This paper shows the obtained generic models about an embankment dam. Using these models may help the actors in charge of hydraulic engineering to control and diagnose dam.Ce papier présente une méthode de modélisation du comportement des barrages appréhendés comme des systèmes dynamiques. Cette méthode, appelée TOM4D, est basée sur des observations datées pour le diagnostic. Elle propose, à partir des connaissances fournies par des experts et en se plaçant à leur niveau d'abstraction, d'expliciter les modèles implicites sur lesquels ces experts raisonnent lors des tâches de diagnostic. Ce papier présente les modèles génériques établis pour les barrages en remblai. L'utilisation de ces modèles devrait aider les exploitants, les bureaux d'études et les experts à mieux contrôler et diagnostiquer le comportement des ouvrages hydrauliques

A likely universal model of fracture scaling and its consequence for crustal hydromechanics

13 p.International audienceWe argue that most fracture systems are spatially organized according to two main regimes: a "dilute" regime for the smallest fractures, where they can grow independently of each other, and a "dense" regime for which the density distribution is controlled by the mechanical interactions between fractures. We derive a density distribution for the dense regime by acknowledging that, statistically, fractures do not cross a larger one. This very crude rule, which expresses the inhibiting role of large fractures against smaller ones but not the reverse, actually appears be a very strong control on the eventual fracture density distribution since it results in a self-similar distribution whose exponents and density term are fully determined by the fractal dimension D and a dimensionless parameter g that encompasses the details of fracture correlations and orientations. The range of values for D and g appears to be extremely limited, which makes this model quite universal. This theory is supported by quantitative data on either fault or joint networks. The transition between the dilute and dense regimes occurs at about a few tenths of a kilometer for faults systems and a few meters for joints. This remarkable difference between both processes is likely due to a large-scale control (localization) of the fracture growth for faulting that does not exist for jointing. Finally, we discuss the consequences of this model on the flow properties and show that these networks are in a critical state, with a large number of nodes carrying a large amount of flow

From behavior to circuit modeling of light-seeking navigation in zebrafish larvae

International audienceBridging brain-scale circuit dynamics and organism-scale behavior is a central challenge in neuroscience. It requires the concurrent development of minimal behavioral and neural circuit models that can quantitatively capture basic sensorimotor operations. Here, we focus on lightseeking navigation in zebrafish larvae. Using a virtual reality assay, we first characterize how motor and visual stimulation sequences govern the selection of discrete swim-bout events that subserve the fish navigation in the presence of a distant light source. These mechanisms are combined into a comprehensive Markov-chain model of navigation that quantitatively predicts the stationary distribution of the fish's body orientation under any given illumination profile. We then map this behavioral description onto a neuronal model of the ARTR, a small neural circuit involved in the orientation-selection of swim bouts. We demonstrate that this visually-biased decision-making circuit can capture the statistics of both spontaneous and contrast-driven navigation