Confidence-Aware Fault Trees-Reference-Cited by-同舟云学术

Confidence-Aware Fault Trees

Published:2024-07-30 Issue: Volume: Page:1-7
ISSN:
Container-title:Proceedings of the 19th International Conference on Availability, Reliability and Security
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Author:

Günther Alexander¹^ORCID,Vollmer Sebastian²^ORCID,Liggesmeyer Peter³^ORCID

Affiliation:

1. Department of Software Engineering: Dependability, University of Kaiserslautern-Landau, Germany

2. University of Kaiserslautern-Landau, Germany and Deutsches Forschungszentrum für Künstliche Intelligenz, Germany

3. Department of Software Engineering: Dependability, University of Kaiserslautern-Landau, Germany and Fraunhofer-Institut für Experimentelles Software Engineering IESE, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3664476.3664502

Reference19 articles.

1. The use of not logic in fault tree analysis

2. Methods for the estimation of confidence bounds for the top-event unavailability of fault trees

3. Interval methods for fault-tree analysis in robotics

4. Jeffrey De Fauw Joseph R. Ledsam Bernardino Romera-Paredes Stanislav Nikolov Nenad Tomasev Sam Blackwell Harry Askham Xavier Glorot Brendan O’Donoghue Daniel Visentin George van den Driessche Balaji Lakshminarayanan Clemens Meyer Faith Mackinder Simon Bouton Kareem Ayoub Reena Chopra Dominic King Alan Karthikesalingam Cían O. Hughes Rosalind Raine Julian Hughes Dawn A. Sim Catherine Egan Adnan Tufail Hugh Montgomery Demis Hassabis Geraint Rees Trevor Back Peng T. Khaw Mustafa Suleyman Julien Cornebise Pearse A. Keane and Olaf Ronneberger. 2018. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine 24 9 (01 Sep 2018) 1342–1350. https://doi.org/10.1038/s41591-018-0107-6