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.
Details
Title
- Anomaly Mining and Visualization of Autonomous Aerial Vehicles
Contributors
- Elenes Cazares, Jose R (Author)
- Bryan, Chris (Thesis advisor)
- Banerjee, Ayan (Committee member)
- Gonzalez Sanchez, Javier (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2022
Subjects
Resource Type
Collections this item is in
Note
- Partial requirement for: M.S., Arizona State University, 2022
- Field of study: Computer Science