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Found 9 papers, 1 papers with code
Anomaly detection for automated inspection of power line insulators
Inspection of insulators is important to ensure reliable operation of the power system.
There is more to graphs than meets the eye: Learning universal features with self-supervision
We hypothesize that leveraging multiple graphs of the same type/class can improve the quality of learnt representations in the model by extracting features that are universal to the class of graphs.
Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines
In such a scenario, the performance of the DNN model will be influenced by the uncertainty in the physics-based model as well as the parameters of the DNN.
Object detection-based inspection of power line insulators: Incipient fault detection in the low data-regime
We curate a large reference dataset of insulator images that can be used to learn robust features for detecting healthy and faulty insulators.
Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields, including pattern recognition, robotics, recommendation-systems, and gaming.
A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks
Graph Neural Networks (GNN) provide a powerful framework that elegantly integrates Graph theory with Machine learning for modeling and analysis of networked data.
Scalable Graph Neural Network-based framework for identifying critical nodes and links in Complex Networks
To overcome these challenges, this article proposes a scalable and generic graph neural network (GNN) based framework for identifying critical nodes/links in large complex networks.
Bayesian Graph Neural Network for Fast identification of critical nodes in Uncertain Complex Networks
Most existing methods of critical node identification are based on an iterative approach that explores each node/link of a graph.
Robust and Efficient Swarm Communication Topologies for Hostile Environments
In several of such applications, the agents face a hostile environment that can result in loss of agents during the search.
