CrackGeoFM is a multi-task framework that adapts a frozen visual foundation model with FCEM, CFAM, and SMTD modules for crack mask prediction, skeleton reconstruction, and uncertainty estimation, reporting SOTA results across 20 datasets including few-shot settings.
A deep reinforcement learning-based intelligent fault diagnosis framework for rolling bearings under imbalanced datasets,
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2026 2verdicts
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Reinforcement learning formulates sim-to-real feature alignment as a Markov decision process to improve vibration-based bearing fault diagnosis under data scarcity.
citing papers explorer
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Multi-Task Crack Foundation Model for Engineering-Reliable Crack Representation and Topology Preservation in Civil Infrastructure
CrackGeoFM is a multi-task framework that adapts a frozen visual foundation model with FCEM, CFAM, and SMTD modules for crack mask prediction, skeleton reconstruction, and uncertainty estimation, reporting SOTA results across 20 datasets including few-shot settings.
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Digital Twin-Driven Adaptive Sim-to-Real Alignment via Reinforcement Learning for Vibration-Based Bearing Health Monitoring Under Data Scarcity
Reinforcement learning formulates sim-to-real feature alignment as a Markov decision process to improve vibration-based bearing fault diagnosis under data scarcity.