Buch, Englisch, 649 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 984 g
Volume VI
Buch, Englisch, 649 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 984 g
Reihe: Lecture Notes in Mechanical Engineering
ISBN: 978-981-953036-6
Verlag: Springer
This book includes original, peer-reviewed research papers from the 2nd Aerospace Frontiers Conference (AFC 2025), held in Beijing, China on Apr. 11-14, 2025. The topics covered include but are not limited to: Overall Design and Autonomous Intelligence Technology of Intelligent Unmanned Aerial Vehicles, Aerodynamics of Aerospace Vehicles, New Aerospace Propulsion Systems, Intelligent and Morphing Aircraft Technology, Recent Advances and New Challenges in Aircraft Strength Technology, Complexity Sciences in Aerospace, Intelligent Control and Trajectory Planning of Aerospace Vehicles, Frontiers in Low-Altitude Traffic, Intelligent Sensing and Processing of Aerospace Information, Space Intelligent Control Technology, Dynamics and Control of Space Unmanned Systems, Lightweight Design and Evaluation in Aerospace Equipment, making the book a valuable asset for researchers, engineers, and university students alike.
Zielgruppe
Research
Fachgebiete
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
- Technische Wissenschaften Technik Allgemein Konstruktionslehre und -technik
- Technische Wissenschaften Sonstige Technologien | Angewandte Technik Astronautik
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
- Technische Wissenschaften Verkehrstechnik | Transportgewerbe Luft- und Raumfahrttechnik, Luftverkehr
Weitere Infos & Material
A Deep Learning-Based Method for Cloud Detection in Satellite Remote Sensing Images.- Layout Optimization of Cockpit Equipment Based on Particle Swarm Optimization.- Study of Test Method for Three Component Aerodynamic Characteristic for Irregular Shaped Sand Particles.- Progressive damage analysis of the composite rotor blade under quasi-static and fatigue loadings.- Semi-Differential Reynolds-Stress Model Based on Symbolic Regression Training.
