Results 81 to 90 of about 5,698,498 (295)
Degree-Aware Graph Neural Network Quantization
Entropy, 2023 In this paper, we investigate the problem of graph neural network quantization. Despite the great success on convolutional neural networks, directly applying current network quantization approaches to graph neural networks faces two challenges.
Ziqin Fan, Xi Jin
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Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Polynomial-based graph convolutional neural networks for graph classification
Machine Learning, 2021 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pasa L., Navarin N., Sperduti A.
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Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges
Advanced Robotics Research, EarlyView.This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder +3 more
wiley +1 more source
Advances in Multimedia, 2022 In order to improve the classification effect of the 3D CAD model, this paper combines the knowledge recognition algorithm of convolutional neural network to construct the 3D CAD model classification model.
Weiwei Wang, Dandan Sun
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This paper reviews the applications of Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs) in blockchain technology. As the complexity and adoption of blockchain networks continue to grow, traditional analytical methods are proving inadequate in capturing the intricate relationships and dynamic ...
Amy Ancelotti, Claudia Liason
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Processing of Incomplete Images by (Graph) Convolutional Neural Networks [PDF]
, 2020 We investigate the problem of training neural networks from incomplete images without replacing missing values. For this purpose, we first represent an image as a graph, in which missing pixels are entirely ignored. The graph image representation is processed using a spatial graph convolutional network (SGCN) -- a type of graph convolutional networks ...
Tomasz Danel +4 more
openaire +5 more sources
Space Target Material Identification Based on Graph Convolutional Neural Network
, 2023 Under complex illumination conditions, the spectral data distributions of a given material appear inconsistent in the hyperspectral images of the space target, making it difficult to achieve accurate material identification using only spectral features ...
Yun Ma +3 more
core +1 more source
Flexible Sensors for Robotics Tactile Perception: A Review
Advanced Robotics Research, EarlyView.Flexible tactile sensing for robotics is reviewed through four interconnected dimensions. Physical mechanisms include piezoresistive, capacitive, piezoelectric, triboelectric, iontronic, and optical sensing. Structural design includes bioinspired, defect‐based, and MEMS‐based tactile systems.
Yu Song, Ying Chen, Yihao Chen, Xue Feng
wiley +1 more source
Intelligent Smart Grid Energy Management for EV Charging Stations Using GOA–HMGIGCN
AlgorithmsElectric Vehicle Charging Stations (EVCSs) have become increasingly important due to the growing penetration of electric vehicles (EVs) and renewable-based power generation.
Mlungisi Ntombela
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