Vlontzos, Athanasios, Sutherland, Gabriel, Ganju, Siddha et al. (1 more author) (2021) Next-Gen Machine Learning Supported Diagnostic Systems for Spacecraft. [Preprint]
Abstract
Future short or long-term space missions require a new generation of monitoring and diagnostic systems due to communication impasses as well as limitations in specialized crew and equipment. Machine learning supported diagnostic systems present a viable solution for medical and technical applications. We discuss challenges and applicability of such systems in light of upcoming missions and outline an example use case for a next-generation medical diagnostic system for future space operations. Additionally, we present approach recommendations and constraints for the successful generation and use of machine learning models aboard a spacecraft.
Metadata
Item Type: | Preprint |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Accepted in the AI for Spacecraft Longevity Workshop at IJCAI2021 |
Keywords: | cs.LG,cs.AI |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 24 Mar 2025 17:30 |
Last Modified: | 24 Mar 2025 17:30 |
Status: | Published |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:224732 |