BibTeX Export for Property Learning-Based Fault Detection for Liquid Propellant Rocket Engine Control Systems

Copy to Clipboard Download

@InProceedings{urgolo_et_al:OASIcs.DX.2024.15,
  author =	{Urgolo, Andrea and Pill, Ingo and Waxenegger-Wilfing, G\"{u}nther and Freiberger, Manuel},
  title =	{{Property Learning-Based Fault Detection for Liquid Propellant Rocket Engine Control Systems}},
  booktitle =	{35th International Conference on Principles of Diagnosis and Resilient Systems (DX 2024)},
  pages =	{15:1--15:20},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-356-0},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{125},
  editor =	{Pill, Ingo and Natan, Avraham and Wotawa, Franz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.DX.2024.15},
  URN =		{urn:nbn:de:0030-drops-221074},
  doi =		{10.4230/OASIcs.DX.2024.15},
  annote =	{Keywords: Machine learning, Runtime verification, Property learning, Monitoring, Fault detection, Diagnosis, Genetic programming, Explainable AI}
}

The metadata provided by Dagstuhl Publishing on its webpages, as well as their export formats (such as XML or BibTeX) available at our website, is released under the CC0 1.0 Public Domain Dedication license. That is, you are free to copy, distribute, use, modify, transform, build upon, and produce derived works from our data, even for commercial purposes, all without asking permission. Of course, we are always happy if you provide a link to us as the source of the data.

Read the full CC0 1.0 legal code for the exact terms that apply: https://creativecommons.org/publicdomain/zero/1.0/legalcode