Search Results for author:
Found 11 papers, 3 papers with code
Decoding Quantum LDPC Codes Using Graph Neural Networks
In this paper, we propose a novel decoding method for Quantum Low-Density Parity-Check (QLDPC) codes based on Graph Neural Networks (GNNs).
Combining X-Vectors and Bayesian Batch Active Learning: Two-Stage Active Learning Pipeline for Speech Recognition
The second stage incorporates a supervised AL strategy, with a batch AL method specifically developed for ASR, aimed at selecting diverse and informative batches of samples.
Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems
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 systems.
Graph Neural Networks on Factor Graphs for Robust, Fast, and Scalable Linear State Estimation with PMUs
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 is needed.
Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms
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.
Scalability and Sample Efficiency Analysis of Graph Neural Networks for Power System State Estimation
Additionally, to evaluate the scalability of the GNN model, we conduct experiments on power systems of various sizes.
GP CC-OPF: Gaussian Process based optimization tool for Chance-Constrained Optimal Power Flow
The developed tool presents a novel data-driven approach based on the GP regression model for solving the CC-OPF problem with a trade-off between complexity and accuracy.
Distributed Nonlinear State Estimation in Electric Power Systems using Graph Neural Networks
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 iterative Gauss-Newton method.
Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks
Firstly, in a tutorial fashion, we present an overview on how distributed SE can be integrated with the elements of the 5G core network and radio access network architecture.
Robust and Fast Data-Driven Power System State Estimator Using Graph Neural Networks
The power system state estimation (SE) algorithm estimates the complex bus voltages based on the available set of measurements.
State Estimation in Electric Power Systems Leveraging Graph Neural Networks
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.
