154 citations · 538 across the 74 of their papers we have counts for
9 papers · 1 filter
Efficient Split Learning LSTM Models for FPGA-based Edge IoT Devices
Romina Soledad Molina, Vukan Ninkovic, Dejan Vukobratovic +2
Split Learning (SL) recently emerged as an efficient paradigm for distributed Machine Learning (ML) suitable for the Internet Of Things (IoT)-Cloud systems. However, deploying SL o…
Graph Neural Networks on Factor Graphs for Robust, Fast, and Scalable Linear State Estimation with PMUs
Ognjen Kundacina, Mirsad Cosovic, Dragisa Miskovic +1
As phasor measurement units (PMUs) become more widely used in transmission power systems, a fast state estimation (SE) algorithm that can take advantage of their high sample rates…
Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimation
Ognjen Kundacina, Gorana Gojic, Mirsad Cosovic +2
Data-driven state estimation (SE) is becoming increasingly important in modern power systems, as it allows for more efficient analysis of system behaviour using real-time measureme…
Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms
Ognjen Kundacina, Gorana Gojic, Mile Mitrovic +2
Electrical power systems are increasing in size, complexity, as well as dynamics due to the growing integration of renewable energy resources, which have sporadic power generation.…
Machine Learning Methods for Device Identification Using Wireless Fingerprinting
Srđan Šobot, Vukan Ninković, Dejan Vukobratović +2
Industrial Internet of Things (IoT) systems increasingly rely on wireless communication standards. In a common industrial scenario, indoor wireless IoT devices communicate with acc…
Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks
Ognjen Kundacina, Miodrag Forcan, Mirsad Cosovic +5
Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for…