Predicting particle accelerator failures using binary classifiers-Reference-Cited by-同舟云学术

Predicting particle accelerator failures using binary classifiers

Published:2020-03 Issue: Volume:955 Page:163240
ISSN:0168-9002
Container-title:Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
language:en
Short-container-title:Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

Author:

Rescic Miha,Seviour Rebecca,Blokland Willem

Publisher

Elsevier BV

Subject

Instrumentation,Nuclear and High Energy Physics

Reference74 articles.

1. Risk assessment for civil engineering facilities: critical overview and discussion;Faber;Reliab. Eng. Syst. Saf.,2003

2. Realistic and efficient reliability estimation for aerospace structures;Pradlwarter;Comput. Methods Appl. Mech. Eng.,2005

3. Reliability studies of the PDS-XADS accelerator;Pierini,2003

4. Paolo Pierini, Luciano Burgazzi, ADS accelerator reliability activities in Europe, in: Utilisation and Reliability of High Power Proton Accelerators: Workshop Proceedings, 2004.

5. NEA accelerator-driven systems (ADS) and fast reactors (FR) in advanced nuclear fuel cycles: A comparative study,2002

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