Chengjun Li

Data of the paper: "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines"

Yasmin Ali, Ahmed Elgammal, Chengjun Li, Junlin Heng, Kaoshan Dai

These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".

Also available via: European Organization for Nuclear Research

Single-Teacher View Augmentation: Enhancing Knowledge Distillation with Student-Guided Perturbations

Xuyi Yu, Yaohua Liu, Chengjun Li, Qiang Tang, Shuzhe Tang, Kuizhi Mei

Knowledge distillation (KD) typically relies on the fixed perspective of a single teacher, limiting the diversity of supervisory signals. While multi-teacher distillation addresses this by aggregating knowledge from multiple models, it incurs prohibitive computational and storage…Read more

DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network

Chengjun Li, Liguo Yao, Yao Lu, Songsong Zhang, Taihua Zhang

In the digital twin system of mechatronics engineering, the scale and accuracy of models are continually improving. Nevertheless, this growth can hinder real-time interaction and decision-making accuracy within digital twins. The resulting delay impacts the entire system’s reliab…Read more