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- W1498334600 abstract "Understanding the relationship between the impact of radiation at the component and system levels is challenging. This paper discusses a hierarchical approach, based on Bayesian theory, to establish a mechanism for determining system health based on the status of, and interactions between, the radiation response of component parts. When the Bayesian network is trained with a combination of experimental data, data from similar parts, simulations, and expert estimates, a quantitative estimate of the Total-Ionizing Dose (TID) response of a system can be obtained. Bayesian networks enable inference about system-level functional performance, the dose exposure, and the sensitivity of different components to TID, thus providing a framework for TID awareness in design and operation of systems. A case study of a robotic system consisting of commercial components is presented." @default.
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- W1498334600 date "2015-08-01" @default.
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- W1498334600 title "System Health Awareness in Total-Ionizing Dose Environments" @default.
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- W1498334600 doi "https://doi.org/10.1109/tns.2015.2440993" @default.
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