5 accepted papers
Graph Neural Networks (GNNs) have received increasing attention due to their ability to handle graph-structured data, yet their explainability remains a significant challenge. An effective solution is to provide the GNN models with counterfactual explanations, which aim to answer “How should the in
With the increasing research on underwater robots, the research on the stable motion mode and control strategy of underwater robots that consider practical constraints and the underwater disturbance from complex environment is still immature. There is a lack of quantitative analysis and comparison o
The magnetic dipole model exhibits significant deviations from real-world sensor data due to neglected material nonlinearities and environmental interference. This paper proposed a Physics-Informed Residual Network (PIRNet) that adaptively corrected simulated magnetic field data by integrating dipol
Underwater Object Detection (UOD) techniques are critical for Autonomous Underwater Vehicle (AUV), which must operate in harsh underwater environments characterized by low visibility while satisfying the lightweight and real-time constraints required for vehicle-mounted systems. Current methods typi
A compact and lightweight hybrid aerial underwater vehicle(HAUV) named YingLong-I is proposed in this letter. The system primarily consists of an integrated coaxial counter-rotating propulsion module, a rudder-based air-water compatible attitude control module, and an attitude switching module, with