![]() IEEE National Aerospace and Electronics Systems Conference. IEEE National Aerospace and Electronics Systems Conference (2018)īlasch, E., Pokines, B.: Analytical Science for Autonomy Evaluation. Artech House, Norwood, MA (2012)īlasch, E., Liu, S., Liu, Z., Zheng, Y.: Deep Learning Measures of Effectiveness. ![]() 18(2), 217 (2018)īlasch, E., Bosse, E., Lambert, D.A.: High-level Information Fusion Management and Systems Design. Hong, J., Laflamme, S., Dodson, J., Joyce, B.: Introduction to state estimation of high-rate system dynamics. Lee, S.J., Jang, M.S., Kim, Y.G., Park, G.T.: Stereovision-based real-time occupant classification system for advanced airbag systems. Wadley, H.N., Dharmasena, K.P., He, M., McMeeking, R.M., Evans, A.G., Bui-Thanh, T., Radovitzky, R.: An active concept for limiting injuries caused by air blasts. (eds.): Handbook of Dynamic Data Driven Applications Systems. DESTech, Lancaster (2013)īlasch, E., Ravela, S., Aved, A. 30(4), 04015072 (2016)Ĭhang, F.K.: Structural Health Monitoring 2013: A Roadmap to Intelligent Structures. Seo, J., Hu, J.W., Lee, J.: Summary review of structural health monitoring applications for highway bridges. Keywordsįarrar, C., Worden, K.: An introduction to structural health monitoring. Quantification of uncertainty, both aleatory and epistemic, is necessary for real-time state estimation to be connected with the confidences to integrate risks into the decision-making. Technologies such as machine learning and edge-computing can be further harnessed to enable structural and functional prognostics for high-rate dynamic systems. To address the grand challenge, we propose physics-informed real-time fusion (PIRF) of high-speed dynamic data. These constraints must be coupled to allow for high-rate implementation that is robust, adaptable, and beneficial to the missions of interest. The temporal issue includes the sensor type (e.g., THz) as well as multiple sources of uncertainty. The spatial issues include the resolution of the area monitored, the communication distance, and the number of edge sensors. The paper defines the high-rate timescale as 1 ms on the integrated paradigm including data acquisition, assessment execution, and decision-making. Key issues to address in such challenges include the time duration of the event, timescales of the physics, multiple sources of uncertainty, as well as limited spatiotemporal constraints for hardware execution. The paper defines the technical area of high-rate structural health monitoring and prognostics and presents the HR-SHM technical grand challenges including multi-timescales of the problem, adequate sensor network and response, real-time assessment, and decision-making with quantified uncertainty and risk. With the advent of real-time sensing, edge-computing, and high-bandwidth computer memory, there is an ability to enable high-rate SHM (HR-SHM). Structural health monitoring (SHM) includes both static and highly dynamic engineering systems.
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