5 papers
A Distributed Gradient-Based Deployment Strategy for a Network of Sensors with a Probabilistic Sensing Model
Hesam Mosalli, Amir G. Aghdam
This paper presents a distributed gradient-based deployment strategy to maximize coverage in hybrid wireless sensor networks (WSNs) with probabilistic sensing. Leveraging Voronoi p…
Dynamic Load Balancing for EV Charging Stations Using Reinforcement Learning and Demand Prediction
Hesam Mosalli, Saba Sanami, Yu Yang +2
This paper presents a method for load balancing and dynamic pricing in electric vehicle (EV) charging networks, utilizing reinforcement learning (RL) to enhance network performance…
Demand Forecasting for Electric Vehicle Charging Stations using Multivariate Time-Series Analysis
Saba Sanami, Hesam Mosalli, Yu Yang +2
As the number of electric vehicles (EVs) continues to grow, the demand for charging stations is also increasing, leading to challenges such as long wait times and insufficient infr…
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning
Saba Sanami, Amir G. Aghdam
This paper investigates unsupervised anomaly detection in multivariate time-series data using reinforcement learning (RL) in the latent space of an autoencoder. A significant chall…
Aero-engines Anomaly Detection using an Unsupervised Fisher Autoencoder
Saba Sanami, Amir G. Aghdam
Reliable aero-engine anomaly detection is crucial for ensuring aircraft safety and operational efficiency. This research explores the application of the Fisher autoencoder as an un…