Full metadata
Title
Anomaly Mining and Visualization of Autonomous Aerial Vehicles
Description
The drone industry is worth nearly 50 billion dollars in the public sector, and drone flight anomalies can cost up to 12 million dollars per drone. The project's objective is to explore various machine-learning techniques to identify anomalies in drone flight and express these anomalies effectively by creating relevant visualizations. The research goal is to solve the problem of finding anomalies inside drones to determine severity levels. The solution was visualization and statistical models, and the contribution was visualizations, patterns, models, and the interface.
Date Created
2022
Contributors
- Elenes Cazares, Jose R (Author)
- Bryan, Chris (Thesis advisor)
- Banerjee, Ayan (Committee member)
- Gonzalez Sanchez, Javier (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
81 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.2.N.171520
Level of coding
minimal
Cataloging Standards
Note
Partial requirement for: M.S., Arizona State University, 2022
Field of study: Computer Science
System Created
- 2022-12-20 12:33:10
System Modified
- 2022-12-20 12:52:47
- 1 year 10 months ago
Additional Formats