2 citations · 2 across the 6 of their papers we have counts for
7 papers
Causal Discovery with Inverted Self-attention for Multivariate Time Series
Yusen Liu, Yong Wang, Yifan Yin +3
Causal discovery in multivariate time series data is challenging due to complex interactions, high dimensionality, and nonlinear dependencies among variables. Existing methods ofte…
pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data
Zhijin Wang, Senzhen Wu, Yue Hu +1
Modern time series analysis demands frameworks that are flexible, efficient, and extensible. However, many existing Python libraries exhibit limitations in modularity and in their…
Prototype-based Heterogeneous Federated Learning for Blade Icing Detection in Wind Turbines with Class Imbalanced Data
Lele Qi, Mengna Liu, Xu Cheng +3
Wind farms, typically in high-latitude regions, face a high risk of blade icing. Traditional centralized training methods raise serious privacy concerns. To enhance data privacy in…
An End-to-End Model for Time Series Classification In the Presence of Missing Values
Pengshuai Yao, Mengna Liu, Xu Cheng +4
Time series classification with missing data is a prevalent issue in time series analysis, as temporal data often contain missing values in practical applications. The traditional…
Disentangling Imperfect: A Wavelet-Infused Multilevel Heterogeneous Network for Human Activity Recognition in Flawed Wearable Sensor Data
Mengna Liu, Dong Xiang, Xu Cheng +4
The popularity and diffusion of wearable devices provides new opportunities for sensor-based human activity recognition that leverages deep learning-based algorithms. Although impr…
High-order Spatial Interactions Enhanced Lightweight Model for Optical Remote Sensing Image-based Small Ship Detection
Yifan Yin, Xu Cheng, Fan Shi +3
Accurate and reliable optical remote sensing image-based small-ship detection is crucial for maritime surveillance systems, but existing methods often struggle with balancing detec…
A Review of Federated Learning in Energy Systems
Xu Cheng, Chendan Li, Xiufeng Liu
With increasing concerns for data privacy and ownership, recent years have witnessed a paradigm shift in machine learning (ML). An emerging paradigm, federated learning (FL), has g…