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Classification, detection and prediction of adverse and anomalous events in medical robots.

作者:Feng Cao,
畢業學校:Case Western Reserve University
出版單位:Case Western Reserve University / OhioLINK
核准日期:2012-08-24
類型:Electronic Thesis or Dissertation
權限:unrestricted.This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.....

英文摘要

In this project we propose a framework to model the behavior and evaluate the reliability and safety of robotic surgery systems. A software simulator and associated user interface tools are used to generate the simulated hardware/software data of a robotic system performing interventions on small animals. The main contributions of this work are the usage of Dynamic Bayesian Networks (DBN) to model both software and hardware dynamics of the robotic surgery system, as well as detecting adverse and anomalous (A&A) events in the system. We show empirically that the model can accurately capture aspects of the software/hardware dynamics. Furthermore, we show that the models are able to accurately classify, detect and predict certain kinds of A&A events. Finally, comparison between different models demonstrates the usefulness of modeling both hardware and software state, as opposed to using only hardware state alone.


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