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A neural network VME-module for recognizing AC current demand signatures in space shuttle telemetry data
KTH, School of Engineering Sciences (SCI), Physics.
2021 (English)In: World Congress on Neural Networks, Taylor & Francis, 2021, Vol. 2, p. II.631-II.640Chapter in book (Other academic)
Abstract [en]

An implementation of an analog neural network trained to identify signatures from the AC electrical power system on the Space Shuttle Orbiter is described. This demonstration project shows that a small stand alone system in the form of a VME-module can be designed, constructed and tested within days, provided a proper set of training vectors are available.

Place, publisher, year, edition, pages
Taylor & Francis, 2021. Vol. 2, p. II.631-II.640
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-316144Scopus ID: 2-s2.0-84914888299OAI: oai:DiVA.org:kth-316144DiVA, id: diva2:1689923
Note

QC 20220824

Chapter in book: ISBN 978-131578407-6, 978-080581745-4

Available from: 2022-08-24 Created: 2022-08-24 Last updated: 2022-08-24Bibliographically approved

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Lindblad, Thomas

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CiteExportLink to record
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