Frontiers

Graph Learning and Deep Unfolding for Electromagnetic Field Modelling and Antenna Systems

Traditional computational electromagnetics (CEM) methods—such as MoM, FEM, or FDTD—offer high fidelity, but struggle to scale ...
IEEE

Uncovering Subtle GNSS Spoofing by Decomposing the Complex Cross-Ambiguity Function

Abstract: In this article, we introduce a method to uncover global navigation satellite system spoofing by directly decomposing the complex cross-ambiguity function (CCAF) into its authentic and ...
Scientific Research Publishing

On the Medication Rules in Shang Han Lun Based on Complex Network Analysis ()

In order to explore the medication rules of Shang Han Lun, this article conducted complex network analysis and cluster analysis on the 112 prescriptions in Shang Han Lun. Statistical and network ...
techxplore

New graph attention network models higher-order relationships in complex graph data

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
IEEE

Causal Disentangled Graph Neural Network for Fault Diagnosis of Complex Industrial Process

Abstract: Graph neural networks (GNNs) are good at capturing the intricate topologies and dependencies among components and are outstanding in fault diagnosis tasks of complex industrial process. Bias ...
GitHub

Hierarchical behavior models for complex decision-making and behavior generation in robotics!

Behavior components address How to do it? and Can we do it?, while Arbitrators decide What to do? 🧠 Meta-framework Integrate diverse methods in one decision-making framework. Why not combine ...