Titlebook: Handbook of Dynamic Data Driven Applications Systems; Erik Blasch,Sai Ravela,Alex Aved Book 20181st edition Springer Nature Switzerland AG

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ication system, extends the notion of Smart Computing to span from the high-end to the real-time data acquisition and control, and manages Big Data exploitation with high-dimensional model coordination..? ? ?.978-3-319-95504-9

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Handbook of Dynamic Data Driven Applications Systems

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Dynamic Data-Driven Adaptive Observations in Data Assimilation for Multi-scale Systems from the expected uncertainty minimization criterion, for dynamic sensor selection in filtering problems. It is compared with a strategy based on finite-time Lyapunov exponents of the dynamical system, which provide insight into error growth due to signal dynamics.

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Dynamic Data-Driven Uncertainty Quantification via Polynomial Chaos for Space Situational Awarenessmine the likelihood of satellite collisions in space..The main focus of this chapter is the application of a new Polynomial Chaos based Uncertainty Quantification (UQ) approach for Space Situational Awareness (SSA). The challenge of applying UQ to SSA is the long-term integration problem, where simu

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Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics to minimize least squares error for given training data. The Bayesian approach classifies operating modes according to supervised offline training and can discover new statistically significant modes online. As shown in Tuninter 1153 simulation result, dynamic Bayes classifier finds discrete error

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A Computational Steering Framework for Large-Scale Composite Structuresctures, such as wind turbine blades and towers. The proposed DISCERN framework continuously and dynamically integrates the SHM data into the FSI analysis of these structures. This capability allows one to: (1) Shelter the structures from excessive stress levels during operation; (2) Make informed de

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Dynamic Data-Driven Approach for Unmanned Aircraft Systems and Aeroelastic Response Analysist. In this phase, with the aeroelastic simulator, preliminary stability envelopes are constructed to determine the flutter boundary of the aircraft with damage and without damage to the aircraft. By using available simulation results, an initial meta-model is trained offline. During the online phase