Detecting Anomalies in Small Unmanned Aerial Systems via Graphical Normalizing Flows
Author:
Affiliation:
1. University of Notre Dame, Notre Dame, IN, USA
Funder
NASA
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Artificial Intelligence,Computer Networks and Communications
Link
http://xplorestaging.ieee.org/ielx7/9670/10111508/10061588.pdf?arnumber=10061588
Reference20 articles.
1. Density estimation using real NVP;dinh,2016
2. RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers
3. Attention is all you need;vaswani;Proc Adv Neural Inf Process Syst,2017
4. Masked autoregressive flow for density estimation;papamakarios;Proc Adv Neural Inf Process Syst,2017
5. Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
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1. ADAM: Adaptive Monitoring of Runtime Anomalies in Small Uncrewed Aerial Systems;Proceedings of the 19th International Symposium on Software Engineering for Adaptive and Self-Managing Systems;2024-04-15
2. An Exploratory Investigation of Log Anomalies in Unmanned Aerial Vehicles;Proceedings of the IEEE/ACM 46th International Conference on Software Engineering;2024-04-12
3. Towards an Annotated All-Weather Dataset of Flight Logs for Small Uncrewed Aerial System;AIAA AVIATION 2023 Forum;2023-06-08
4. Deep Anomaly Analytics: Advancing the Frontier of Anomaly Detection;IEEE Intelligent Systems;2023-03
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