A distributed architecture to implement a prognostic function for complex systems

The proactivity in maintenance management is improved by the implementation of CBM (Condition-Based Maintenance) principles and of PHM (Prognostic and Health Management). These implementations use data about the health status of the systems. Among them, prognostic data make it possible to evaluate the future health of the systems. The Remaining Useful Lifetimes (RULs) of the components is frequently required to prognose systems. However, the availability of complex systems for productive tasks is often expressed in terms of RULs of functions and/or subsystems; those RULs have to bring information about the components. Indeed, the maintenance operators must know what components need maintenance actions in order to increase the RULs of the functions or subsystems, and consequently the availability of the complex systems for longer tasks or more productive tasks. This paper aims at defining a generic prognostic function of complex systems aiming at prognosing its functions and at enabling the isolation of components that needs maintenance actions. The proposed function requires knowledge about the system to be prognosed. The corresponding models are detailed. The proposed prognostic function contains graph traversal so its distribution is proposed to speed it up. It is carried out by generic agents

A deep learning approach to diabetic blood glucose prediction

We consider the question of 30-minute prediction of blood glucose levels measured by continuous glucose monitoring devices, using clinical data. While most studies of this nature deal with one patient at a time, we take a certain percentage of patients in the data set as training data, and test on the remainder of the patients; i.e., the machine need not re-calibrate on the new patients in the data set. We demonstrate how deep learning can outperform shallow networks in this example. One novelty is to demonstrate how a parsimonious deep representation can be constructed using domain knowledge

A framework for proving the self-organization of dynamic systems

This paper aims at providing a rigorous definition of self- organization, one of the most desired properties for dynamic systems (e.g., peer-to-peer systems, sensor networks, cooperative robotics, or ad-hoc networks). We characterize different classes of self-organization through liveness and safety properties that both capture information re- garding the system entropy. We illustrate these classes through study cases. The first ones are two representative P2P overlays (CAN and Pas- try) and the others are specific implementations of \Omega (the leader oracle) and one-shot query abstractions for dynamic settings. Our study aims at understanding the limits and respective power of existing self-organized protocols and lays the basis of designing robust algorithm for dynamic systems

Automatic Classification of Variable Stars in Catalogs with missing data

We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks, a probabilistic graphical model, that allows us to perform inference to pre- dict missing values given observed data and dependency relationships between variables. To learn a Bayesian network from incomplete data, we use an iterative algorithm that utilises sampling methods and expectation maximization to estimate the distributions and probabilistic dependencies of variables from data with missing values. To test our model we use three catalogs with missing data (SAGE, 2MASS and UBVI) and one complete catalog (MACHO). We examine how classification accuracy changes when information from missing data catalogs is included, how our method compares to traditional missing data approaches and at what computational cost. Integrating these catalogs with missing data we find that classification of variable objects improves by few percent and by 15% for quasar detection while keeping the computational cost the same