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Nowadays, we are witnesses of a transformation in the e-learning arena. This transformation has different drivers involving all the actors in the learning value chain, from final users to learning institutions through technology providers. All those actors share a common goal: making the learning processes more effective through the information and communication technologies. This is happening through the promotion of a paradigm shift from content-centered to process-centered solutions. In this paper, we present the results from the European Learning Grid Infrastructure project concerning models, processes, and services supported by a service-oriented software architecture for creating dynamic and adaptive virtual organizations for learning using Grid technologies. [ABSTRACT FROM AUTHOR]

This paper deals with the design of robust model-based fault detection and isolation (FDI) systems for atmospheric reentry vehicles. This work draws expertise from actions undertaken within a project at the European level, which develops a collaborative effort between the University of Bordeaux, the European Space Agency, and European Aeronautic Defence and Space Company Astrium on innovative and robust strategies for reusable launch vehicles (RLVs) autonomy. Using an H∞/H- setting, a robust residual-based scheme is developed to diagnose faults on the vehicle wing-flap actuators. This design stage is followed by an original and specific diagnosis-oriented analysis phase based on the calculation of the generalized structured singular value. The latter provides a necessary and sufficient condition for robustness and FDI fault sensitivity over the whole vehicle flight trajectory. A key feature of the proposed approach is that the coupling between the in-plane and out-of-plane vehicle motions, as well as the effects that faults could have on the guidance, navigation, and control performances, are explicitly taken into account within the design procedure. The faulty situations are selected by a prior trimmability analysis to determine those for which the remaining healthy control effectors are able to maintain the vehicle around its center of gravity. Finally, some performance indicators including detection time, required onboard computational effort, and CPU time consumption are assessed and discussed. Simulation results are based on a nonlinear benchmark of the HL-20 vehicle under realistic operational conditions during the autolanding phase. The Monte Carlo results are quite encouraging, illustrating clearly the effectiveness of the proposed technique and suggesting that this solution could be considered as a viable candidate for future RLV programs. [ABSTRACT FROM AUTHOR]

It is commonly accepted that the requirements for maintenance and diagnosis should be considered at the earliest stages of design. For this reason, methods for analyzing the diagnosability of a system and determining which sensors are needed to achieve the desired degree of diagnosability are highly valued. This paper clarifies the different diagnosability properties of a system and proposes a model-based method for: 1) assessing the level of discriminability of a system, i.e., given a set of sensors, the number of faults that can be discriminated, and its degree of diagnosability, i.e., the discriminability level related to the total number of anticipated faults; and 2) characterizing and determining the minimal additional sensors that guarantee a specified degree of diagnosability. The method takes advantage of the concept of component- supported analytical redundancy relation, which considers recent results crossing over the fault detection and isolation and diagnosis communities. It uses a model of the system to analyze in an exhaustive manner the analytical redundancies associated with the availability of sensors and performs from that a full diagnosability assessment. The method is applied to an industrial smart actuator that was used as a benchmark in the Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems European project. [ABSTRACT FROM AUTHOR]