CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13492 > Article
Paper
13 December 2024 Research on integrated training-inference technology for intelligent interpretation based on aerospace big data
Bo Wang, Weizhe Wang, Hongliang Liu, Qingjian Li, Huimin Guo, Jinhua Liu, Haitong Li
Author Affiliations +
Proceedings Volume 13492, AOPC 2024: Laser Technology and Applications; 134920K (2024) https://doi.org/10.1117/12.3046930
Event: Applied Optics and Photonics China 2024 (AOPC2024), 2024, Beijing, China
Abstract
In recent years, the scale of space-ground systems has been continuously expanding, and data acquisition capabilities have experienced explosive growth. The large-scale Aerospace observation data provide the research foundation for the application of artificial intelligence technologies in the field of intelligent interpretation. However, the current business workflow and practical applications of Aerospace Intelligent Interpretation still have significant shortcomings, such as insufficient timeliness and weak target detection capabilities, which severely restrict the efficiency of Aerospace information acquisition. To address the current issues, this study proposes an Integrated Training-Inference Architecture for intelligent interpretation based on Aerospace Big Data. By constructing sample database, algorithm library, and visualization system supporting human-machine collaboration, this research proposes workflow which including algorithm recommendation based on collaborative-filtering, continuous iteration optimization and autonomous training of algorithm models, intelligent interpretation led by human-machine collaboration, and multi-task-oriented performance evaluation for intelligent interpretation. The process is verified through typical Aerospace Information Intelligent Application Tasks, demonstrating the feasibility of the Training-Inference Integration architecture in intelligent interpretation domain. By employing this method, the capability and efficiency of Aerospace Big Data Intelligent Interpretation have been effectively enhanced, enabling the proactive release of intelligent algorithms services and achieving human-machine collaboration mode in intelligent interpretation tasks.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Bo Wang, Weizhe Wang, Hongliang Liu, Qingjian Li, Huimin Guo, Jinhua Liu, and Haitong Li "Research on integrated training-inference technology for intelligent interpretation based on aerospace big data", Proc. SPIE 13492, AOPC 2024: Laser Technology and Applications, 134920K (13 December 2024); https://doi.org/10.1117/12.3046930
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
INSTITUTIONAL
Select your institution to access the SPIE Digital Library.
SELECT YOUR INSTITUTION
PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
PERSONAL SIGN IN
No SPIE Account? Create one
PURCHASE THIS CONTENT
SUBSCRIBE TO DIGITAL LIBRARY
50 downloads per 1-year subscription
Members: $195
Non-members: $335 ADD TO CART
25 downloads per 1 - year subscription
Members: $145
Non-members: $250 ADD TO CART
PURCHASE SINGLE ARTICLE
Includes PDF, HTML & Video, when available
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 12 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Aerospace engineering
Detection and tracking algorithms
Mathematical optimization
Data modeling
Remote sensing
Target recognition
Evolutionary algorithms
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top