A novel teacher-student ensemble of physics-informed deep learning models improves traffic state estimation under varying speed limit conditions by using a classifier to select appropriate physics-constrained models.
Deep learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
The paper surveys AI-driven collaborative spectrum sensing methods categorized by learning paradigms and positions semantic communication as a joint communication-computation framework for improved efficiency.
citing papers explorer
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Physics-Informed Teacher-Student Ensemble Learning for Traffic State Estimation with a Varying Speed Limit Scenario
A novel teacher-student ensemble of physics-informed deep learning models improves traffic state estimation under varying speed limit conditions by using a classifier to select appropriate physics-constrained models.
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Collaborative Spectrum Sensing in Cognitive and Intelligent Wireless Networks: An Artificial Intelligence Perspective
The paper surveys AI-driven collaborative spectrum sensing methods categorized by learning paradigms and positions semantic communication as a joint communication-computation framework for improved efficiency.