216 citations · 562 across the 33 of their papers we have counts for
8 papers · 1 filter
A Survey on Graph Neural Networks for Remaining Useful Life Prediction: Methodologies, Evaluation and Future Trends
Yucheng Wang, Min Wu, Xiaoli Li +2
Remaining Useful Life (RUL) prediction is a critical aspect of Prognostics and Health Management (PHM), aimed at predicting the future state of a system to enable timely maintenanc…
Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation
Yucheng Wang, Peiliang Gong, Min Wu +4
Time-Series (TS) data has grown in importance with the rise of Internet of Things devices like sensors, but its labeling remains costly and complex. While Unsupervised Domain Adapt…
EverAdapt: Continuous Adaptation for Dynamic Machine Fault Diagnosis Environments
Edward, Mohamed Ragab, Yuecong Xu +4
Unsupervised Domain Adaptation (UDA) has emerged as a key solution in data-driven fault diagnosis, addressing domain shift where models underperform in changing environments. Howev…
LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices
Ruibing Jin, Qing Xu, Min Wu +4
Limited by the scale and diversity of time series data, the neural networks trained on time series data often overfit and show unsatisfacotry performances. In comparison, large lan…
Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation
Mohamed Ragab, Peiliang Gong, Emadeldeen Eldele +6
Source-free domain adaptation (SFDA) aims to adapt a model pre-trained on a labeled source domain to an unlabeled target domain without access to source data, preserving the source…
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks
Xue Geng, Zhe Wang, Chunyun Chen +10
Deep neural networks (DNNs) have been widely used in many artificial intelligence (AI) tasks. However, deploying them brings significant challenges due to the huge cost of memory,…
TSLANet: Rethinking Transformers for Time Series Representation Learning
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +2
Time series data, characterized by its intrinsic long and short-range dependencies, poses a unique challenge across analytical applications. While Transformer-based models excel at…
Graph Generation Powered with LLMs for Boosting Multivariate Time-Series Representation Learning
Yucheng Wang, Min Wu, Ruibing Jin +3
Sourced from multiple sensors and organized chronologically, Multivariate Time-Series (MTS) data involves crucial spatial-temporal dependencies. To capture these dependencies, Grap…