4 papers
Dynamic Graph-Like Learning with Contrastive Clustering on Temporally-Factored Ship Motion Data for Imbalanced Sea State Estimation in Autonomous Vessel
Kexin Wang, Mengna Liu, Xu Cheng +3
Accurate sea state estimation is crucial for the real-time control and future state prediction of autonomous vessels. However, traditional methods struggle with challenges such as…
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…