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The Application of Artificial Intelligence and Deep Learning to Visually Identify Micrometeoroid and Orbital Debris ImpactsRecent advancements in Artificial Intelligence (AI) have made Machine Learning (ML) techniques readily available for practical applications while using a fraction of time that was previously required. In particular, the use of Deep Learning (DL) algorithms has advanced the field of image and pattern recognition. With the use of Deep Learning algorithms, Micrometeoroid and Orbital Debris(MMOD) penetrations can be identified with high accuracy and give possibilities to new understandings of hypervelocity impacts.
Document ID
20240010307
Acquisition Source
Johnson Space Center
Document Type
Conference Paper
Authors
Cameron M Collins
(Jacobs (United States) Dallas, Texas, United States)
Dana M Lear
(Johnson Space Center Houston, United States)
Kenton R Fisher
(Johnson Space Center Houston, United States)
Date Acquired
August 8, 2024
Subject Category
Cybernetics, Artificial Intelligence and Robotics
Space Transportation and Safety
Report/Patent Number
HVIS2024-045
Meeting Information
Meeting: 17th Hypervelocity Impact Symposium (HVIS)
Location: Tsukuba
Country: JP
Start Date: September 9, 2024
End Date: September 13, 2024
Sponsors: The Hypervelocity Impact Society
Funding Number(s)
CONTRACT_GRANT: 80JSC022DA035
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
Deep Learning
Artificial Intelligence
Machine Learning
Hypervelocity Impacts
Pattern Recognition
Micrometeoroid and Orbital Debris
MMOD Shielding
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