The proposed Bayesian optimization-based approach enhances micromixer performance by optimizing geometric parameters, significantly reducing required number of simulations, and accelerating the design process compared to conventional methods.

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as variables based on available set measurements in power system. Because phasor measurement units (PMUs) are increasingly being used transmission systems, there a need for fast SE solver that can take advantage high sampling rates PMUs. This paper proposes training graph neural network (GNN) learn estimates given PMU voltage and current inputs, with intent obtaining accurate predictions during evaluation...

The electric grid is undergoing a major transition from fossil fuel-based power generation to renewable energy sources, typically interfaced the via electronics. future systems are thus expected face increased control complexity and challenges pertaining frequency stability due lower levels of inertia damping. As result, development novel ancillary services becoming imperative. This paper proposes data-driven scheme, based on Reinforcement Learning (RL), for grid-forming Voltage Source...

Emphasizing a data-centric AI approach, this paper introduces novel two-stage active learning (AL) pipeline for automatic speech recognition (ASR), combining unsupervised and supervised AL methods. The first stage utilizes by using x-vectors clustering diverse sample selection from unlabeled data, thus establishing robust initial dataset the subsequent AL. second incorporates strategy, with batch method specifically developed ASR, aimed at selecting informative batches of samples. Here,...

Nonlinear state estimation (SE), with the goal of estimating complex bus voltages based on all types measurements available in power system, is usually solved using iterative Gauss-Newton (GN) method. The nonlinear SE presents some difficulties when considering inputs from both phasor measurement units and supervisory control data acquisition system. These include numerical instabilities, convergence time depending starting point method, quadratic computational complexity a single iteration...

Fifth-Generation (5G) networks have a potential to accelerate power system transition flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G are expected enable novel data-centric Smart Grid (SG) services. In this paper, we explore how data-driven SG services could be integrated with ML/AI-enabled in symbiotic relationship. We focus on the State Estimation (SE) function as key element of...

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as variables based on available set measurements in power system. Because phasor measurement units (PMUs) are increasingly being used transmission systems, there a need for fast SE solver that can take advantage high sampling rates PMUs. This paper proposes training graph neural network (GNN) learn estimates given PMU voltage and current inputs, with intent obtaining accurate predictions during evaluation...

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)-based QLDPC decoders, proposed GNN-based decoder exploits sparse graph structure of and can be implemented as message-passing algorithm. We compare algorithm against selected classes both conventional neural-enhanced algorithms across several code designs. The simulation results demonstrate excellent performance...

The power system state estimation (SE) algorithm estimates the complex bus voltages based on available set of measurements. Because phasor measurement units (PMUs) are becoming more widely employed in transmission systems, a fast SE solver capable exploiting PMUs' high sample rates is required. To accomplish this, we present method for training model graph neural networks (GNNs) to learn from PMU voltage and current measurements, which, once it trained, has linear computational complexity...

The Serbian Electromagnetic Field Monitoring Network - SEMONT has started with test monitoring of electromagnetic field (EMF) in a closed room. In this paper, the case study high-frequency electric strength is presented, utilizing Narda AMB 8057/03 station. procedure been simultaneously performed frequency ranges 100 kHz-7 GHz, GSM 900, 1800 and UMTS 2100, room largest amphitheater Faculty Technical Science, University Novi Sad. was conducted during real daily conditions student's presence...

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 grids. In recent years, integrating a significant amount of renewables into grid causes high fluctuations and thus brings lot uncertainty to operations. This fact makes the conventional model-based CC-OPF non-convex computationally complex solve. tool presents novel data-driven approach on GP regression model with...

Electrical power systems are increasing in size, complexity, as well dynamics due to the growing integration of renewable energy resources, which have sporadic generation. This necessitates development near real-time system algorithms, demanding lower computational complexity regarding size. Considering trend collection historical measurement data and recent advances rapidly developing deep learning field, main goal this paper is provide a review learning-based monitoring optimization...

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 measurement data. This paper thoroughly evaluates a phasor unit-only estimator based on graph neural networks (GNNs) applied over factor graphs. To assess the sample efficiency GNN model, we perform multiple training experiments various set sizes. Additionally, to evaluate scalability conduct systems Our results show that...

Electrical power systems are increasing in size, complexity, as well dynamics due to the growing integration of renewable energy resources, which have sporadic generation. This necessitates development near real-time system algorithms, demanding lower computational complexity regarding size. Considering trend collection historical measurement data and recent advances rapidly developing deep learning field, main goal this paper is provide a review learning-based monitoring optimization...

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. To accomplish this, we present method uses graph neural networks (GNNs) to learn complex bus voltage estimates from PMU and current measurements. We propose an original implementation GNNs over the system's factor simplify integration various types quantities measurements on system buses branches....

Methods for automated retinal vessel segmentation play an important role in the treatment and diagnosis of many eye systemic diseases. With fast development deep learning methods, more methods are implemented as neural networks. In this paper, we provide a brief review recent from highly influential journals conferences. The objectives are: (1) to assess design characteristics latest (2) report analyze quantitative values performance evaluation metrics, (3) advantages disadvantages solutions.

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 measurement data. This paper thoroughly evaluates a phasor unit-only estimator based on graph neural networks (GNNs) applied over factor graphs. To assess the sample efficiency GNN model, we perform multiple training experiments various set sizes. Additionally, to evaluate scalability conduct systems Our results show that...