23 citations · 47 across the 16 of their papers we have counts for
16 papers
Sensing-Assisted Adaptive Beam Probing with Calibrated Multimodal Priors and Uncertainty-Aware Scheduling
Abidemi Orimogunje, Vukan Ninkovic, Ognjen Kundacina +5
Highly directional mmWave/THz links require rapid beam alignment, yet exhaustive codebook sweeps incur prohibitive training overhead. This letter proposes a sensing-assisted adapti…
JuliaGrid: An Open-Source Julia-Based Framework for Power System State Estimation
Mirsad Cosovic, Ognjen Kundacina, Muhamed Delalic +6
Modern electric power systems have an increasingly complex structure due to rise in power demand and integration of diverse energy sources. Monitoring these large-scale systems, wh…
Decoding Quantum LDPC Codes Using Graph Neural Networks
Vukan Ninkovic, Ognjen Kundacina, Dejan Vukobratovic +2
In this paper, we propose a novel decoding method for Quantum Low-Density Parity-Check (QLDPC) codes based on Graph Neural Networks (GNNs). Similar to the Belief Propagation (BP)-b…
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition
Ognjen Kundacina, Vladimir Vincan, Dragisa Miskovic
This paper introduces a novel two-stage active learning (AL) pipeline for automatic speech recognition (ASR), combining unsupervised and supervised AL methods. The first stage util…
Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems
Ognjen Kundacina
This PhD thesis thoroughly examines the utilization of deep learning techniques as a means to advance the algorithms employed in the monitoring and optimization of electric power s…
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…
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.…
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
Mile Mitrovic, Ognjen Kundacina, Aleksandr Lukashevich +4
The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow (CC-OPF) is an open-source Python code developed for solving economic dispatch (ED) problem in modern power gr…
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…
Robust and Fast Data-Driven Power System State Estimator Using Graph Neural Networks
Ognjen Kundacina, Mirsad Cosovic, Dejan Vukobratovic
The power system state estimation (SE) algorithm estimates the complex bus voltages based on the available set of measurements. Because phasor measurement units (PMUs) are becoming…
State Estimation in Electric Power Systems Leveraging Graph Neural Networks
Ognjen Kundacina, Mirsad Cosovic, Dejan Vukobratovic
The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phaso…
A Reinforcement Learning Approach for Fast Frequency Control in Low-Inertia Power Systems
Ognjen Stanojev, Ognjen Kundacina, Uros Markovic +3
The electric grid is undergoing a major transition from fossil fuel-based power generation to renewable energy sources, typically interfaced to the grid via power electronics. The…