A5058728771- Advanced Graph Neural Networks
- Advanced Computing and Algorithms
- Adversarial Robustness in Machine Learning
- Face and Expression Recognition
- Complex Network Analysis Techniques
- Machine Learning and Algorithms
- Transportation Planning and Optimization
- Advanced Image and Video Retrieval Techniques
- Remote-Sensing Image Classification
- Transportation and Mobility Innovations
- Urban Transport and Accessibility
- Collaboration in agile enterprises
- Distributed Control Multi-Agent Systems
- Optimization and Packing Problems
Xidian University
2020-2021
Zhejiang University of Technology
2020-2021
Hubei University Of Economics
2015-2020
This study develops a mixed behavioural equilibrium model with explicit consideration of mode choice (MBE-MC) in transportation system where fully automated vehicles (AV) coexist conventional human-driven (HV). For the choice, travellers select among three options, following logit modal split: driving their private HV, or taking an AV mobility service provided by either firm government. route HV drivers follow random utility maximisation principle while central agents passengers Cournot Nash...
Graph Neural Networks (GNNs) has achieved tremendous development on perceptual tasks in recent years, such as node classification, graph link prediction, etc. However, studies show that deep learning models of GNNs are incredibly vulnerable to adversarial attacks, so enhancing the robustness remains a significant challenge. In this paper, we propose subgraph based sample detection against perturbations. To best our knowledge, is first work deep-learning classification models, using Subgraph...