Titlebook: Handbook of Dynamic Data Driven Applications Systems; Volume 1 Erik P. Blasch,Frederica Darema,Alex J. Aved Book 2022Latest edition This is

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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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Markov Modeling via Spectral Analysis: Application to Detecting Combustion Instabilitiesence for a first-order Markov model of the symbol sequence. Then, a graphical method is used to cluster the states of the corresponding high-order Markov model to infer a reduced-size Markov model with a non-deterministic algebraic structure. A Bayesian inference rule captures the parameters of the

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A Computational Steering Framework for Large-Scale Composite Structures: Part I—Parametric-Based Desles, and civil structures, such as wind turbine blades and towers. The proposed DISCERN framework continuously and dynamically integrates the SHM data into the analysis of these structures. This capability allows one to: (1) Shelter the structures from excessive stress levels during operation; (2) M

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Development of Intelligent and Predictive Self-Healing Composite Structures Using Dynamic Data-Driveg process. For this purpose, double-cantilever beam (DCB) tests were carried out to quantify the healing efficiency in terms of Mode-I interlaminar fracture toughness (..) following the ASTM D5528-13 testing protocol, and the healing efficiencies of seven different healing cycles were assessed to te

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Dynamic Data Driven Application Systems for Identification of Biomarkers in DNA Methylation this goal, we propose a novel high dimensional learning framework inspired by the dynamic data driven application systems paradigm to identify the biomarkers, determine the outlier(s) and improve the quality of the resultant disease detection. The proposed framework starts with a principal componen

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Photometric Stereopsis for 3D Reconstruction of Space Objectse utility of the proposed algorithms.The proposed framework results in a estimates of the surface shape of the target object, which can subsequently used in forward models for prediction, data assimilation and subsequent sensor tasking operations. Sensitivity analysis is used to quantify the uncerta

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Aided Optimal Search: Data-Driven Target Pursuit from On-Demand Delayed Binary Observationse the belief on the trajectory of the target, and the searcher actively steers the measurement process to improve its knowledge about the location of the target. In particular, we make two main contributions. The first regards the target trajectory estimation. We show how to perform optimal Bayesian