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The study considered various UAV standards, attacks, their likelihood and impact, and defensive countermeasures. This research was sponsored by Massachusetts Department of Transportation, Office of Transportation Planning. We worked on developing greedy algorithm based countermeasure portfolio section problem to reduce risk.
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In this work, we consider measures based on concepts from maintenance theory and prognostics to provide a framework for quantitative assessment of degradation models and predictive algorithms over multiple maintenance intervals. This approach quantifi es the impact of prognostic distance on cost-based measures, such as average cost per cycle, utilization, safety, and availability. The proposed method is applied to lithium-ion batteries. The framework and measures are generalized to accommodate a wide range of future models, algorithms, and systems to which PHM methods are applied, such as high fi delity physics of failure models and RUL prediction based on deep learning. The work was done in collaboration with the Center for Advanced Lifecycle Engineering, University of Maryland College Park. A part of this work has been published in IEEE International conference on Prognostics and Health Management, San Francisco, CA, July 2019.
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This work establishes an empirical relationship and preliminary model characterizing the relationship between reliability investment and life-cycle support costs to identify a subsystem-level reliability optimization strategy that minimizes lifecycle cost. The study introduced an additional dimension of reliability engineering into tradespace study. The research was extended to consider the improvement of reliability engineering processes and removed the assumption that parameters of the reliability improvement equations are constant. This material is based upon work supported by the Army Research Laboratory (ARL) through the National Institute of Aerospace (NIA) under Prime Cooperative Agreement.