23 citations · 83 across the 26 of their papers we have counts for
28 papers
Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteries
Malik Hassanaly, Corey R. Randall, Peter J. Weddle +4
Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physi…
Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models
Nguyen Do, Truc Nguyen, Malik Hassanaly +3
Despite a plethora of anomaly detection models developed over the years, their ability to generalize to unseen anomalies remains an issue, particularly in critical systems. This pa…
Continual Adversarial Reinforcement Learning (CARL) of False Data Injection detection: forgetting and explainability
Pooja Aslami, Kejun Chen, Timothy M. Hansen +1
False data injection attacks (FDIAs) on smart inverters are a growing concern linked to increased renewable energy production. While data-based FDIA detection methods are also acti…
Adversarial Multi-Agent Reinforcement Learning for Proactive False Data Injection Detection
Kejun Chen, Truc Nguyen, Abhijeet Sahu +1
Smart inverters are instrumental in the integration of distributed energy resources into the electric grid. Such inverters rely on communication layers for continuous control and m…
Detection of False Data Injection Attacks (FDIA) on Power Dynamical Systems With a State Prediction Method
Abhijeet Sahu, Truc Nguyen, Kejun Chen +2
With the deeper penetration of inverter-based resources in power systems, false data injection attacks (FDIA) are a growing cyber-security concern. They have the potential to disru…
Discovery of False Data Injection Schemes on Frequency Controllers with Reinforcement Learning
Romesh Prasad, Malik Hassanaly, Xiangyu Zhang +1
While inverter-based distributed energy resources (DERs) play a crucial role in integrating renewable energy into the power system, they concurrently diminish the grid's system ine…