1 citations · 3 across the 6 of their papers we have counts for
6 papers
Overview of Deep Learning Methods for Retinal Vessel Segmentation
Gorana Gojić, Ognjen Kundačina, Dragiša Mišković +1
Methods for automated retinal vessel segmentation play an important role in the treatment and diagnosis of many eye and systemic diseases. With the fast development of deep learnin…
Non-adversarial Robustness of Deep Learning Methods for Computer Vision
Gorana Gojić, Vladimir Vincan, Ognjen Kundačina +2
Non-adversarial robustness, also known as natural robustness, is a property of deep learning models that enables them to maintain performance even when faced with distribution shif…
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.…
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…
Distributed Nonlinear State Estimation in Electric Power Systems using Graph Neural Networks
Ognjen Kundacina, Mirsad Cosovic, Dragisa Miskovic +1
Nonlinear state estimation (SE), with the goal of estimating complex bus voltages based on all types of measurements available in the power system, is usually solved using the iter…