Towards an Embedded System for Failure Diagnosis in Drones Using AI and SAC-DM on FPGA
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
Abstract
We present a way of failure detection in real-time unmanned aerial vehicles (UAVs) by integrating Chaos Theory and AI techniques on an FPGA board. The Signal Analysis based on Chaos using the Density of Maxima (SAC-DM) validates the input of the Machine Learning (ML) model due to the relation between the density of maxima and autocorrelation length. While the accuracies achieved solely by SAC-DM are not remarkably high, the ML model demonstrates an accuracy of 92.46% when utilizing sac-dm results as inputs. The unprecedented integration of SAC-DM on FPGA board serves as a solution for high-speed onboard processing, parallel integrated data synchronization and fusion, and an enhanced low-power architecture.
Details
Originalsprache | Englisch |
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Titel | 2024 Design, Automation & Test in Europe Conference & Exhibition (DATE) |
Herausgeber (Verlag) | IEEE |
Seiten | 1-2 |
Seitenumfang | 2 |
ISBN (elektronisch) | 978-3-9819263-8-5 |
ISBN (Print) | 979-8-3503-4860-6 |
Publikationsstatus | Veröffentlicht - 10 Juni 2024 |
Peer-Review-Status | Ja |
Konferenz
Titel | 2024 Design, Automation and Test in Europe Conference and Exhibition |
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Kurztitel | DATE 2024 |
Veranstaltungsnummer | 27 |
Dauer | 25 - 27 März 2024 |
Webseite | |
Ort | Palacio De Congresos De Valencia |
Stadt | Valencia |
Land | Spanien |
Externe IDs
ORCID | /0000-0003-2571-8441/work/176859793 |
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ORCID | /0000-0002-6311-3251/work/176862432 |
unpaywall | 10.23919/date58400.2024.10546792 |
Mendeley | ad9a325a-2e6a-3349-a0a1-73c07bcca7cc |
Schlagworte
Schlagwörter
- Chaos Theory, FPGA boards, Real-time, Failure detection, UAVs