5 papers
Cascaded Multi-Granularity Pruning for On-Device LLM Inference in Industrial IoT
Jinghan Wang, Yanjun Chen, Wei Zhang +3
Deploying large language models (LLMs) on Industrial Internet of Things (IIoT) edge devices demands extreme compression, yet existing structured pruning methods collapse at high co…
Digital Twin-Driven Adaptive Sim-to-Real Alignment via Reinforcement Learning for Vibration-Based Bearing Health Monitoring Under Data Scarcity
Jinghan Wang, Yanjun Chen, Wei Zhang +3
Vibration-based health monitoring of rotating machinery requires reliable fault diagnosis under operational data constraints, yet condition assessment remains challenged by structu…
An LLM-based Two-Stage Transformer Framework for Cross-Domain Bearing Fault Diagnosis with Limited Data
Jinghan Wang, Feng Cheng, Wentao Wu +3
Bearing fault diagnosis faces critical challenges when dataset heterogeneity, operating condition variations, and limited labeled data occur simultaneously in industrial environmen…
Progressive Knowledge-Guided Large Language Model Framework for Bearing Fault Diagnosis
Jinghan Wang, Gaoliang Peng, Yanjun Chen +3
Vibration-based bearing fault diagnosis requires resolving three interrelated measurement challenges, including the trade-off between global statistical feature efficiency and loca…
Dimension-variable Mapless Navigation with Deep Reinforcement Learning
Wei Zhang, Yunfeng Zhang, Ning Liu +1
Deep reinforcement learning (DRL) has exhibited considerable promise in the training of control agents for mapless robot navigation. However, DRL-trained agents are limited to the…