Pengyu Song

Orcid: 0000-0003-3681-2310

According to our database1, Pengyu Song authored at least 14 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Structure Feature Extraction for Hierarchical Alarm Flood Classification and Alarm Prediction.
IEEE Trans Autom. Sci. Eng., July, 2024

Explicit Representation and Customized Fault Isolation Framework for Learning Temporal and Spatial Dependencies in Industrial Processes.
IEEE Trans. Neural Networks Learn. Syst., March, 2024

Slow Down to Go Better: A Survey on Slow Feature Analysis.
IEEE Trans. Neural Networks Learn. Syst., March, 2024

Multi-scale self-supervised representation learning with temporal alignment for multi-rate time series modeling.
Pattern Recognit., January, 2024

Multimodal Decoupled Representation With Compatibility Learning for Explicit Nonstationary Process Monitoring.
IEEE Trans. Ind. Electron., 2024

Towards consensual representation: Model-agnostic knowledge extraction for dual heterogeneous federated fault diagnosis.
Neural Networks, 2024

Facing spatiotemporal heterogeneity: A unified federated continual learning framework with self-challenge rehearsal for industrial monitoring tasks.
Knowl. Based Syst., 2024

Fusing consensus knowledge: A federated learning method for fault diagnosis via privacy-preserving reference under domain shift.
Inf. Fusion, 2024

2023
MPGE and RootRank: A sufficient root cause characterization and quantification framework for industrial process faults.
Neural Networks, April, 2023

Spatiotemporal Multiscale Correlation Embedding With Process Variable Reorder for Industrial Soft Sensing.
IEEE Trans. Instrum. Meas., 2023

2022
Sparse and Time-Varying Predictive Relation Extraction for Root Cause Quantification of Nonstationary Process Faults.
IEEE Trans. Instrum. Meas., 2022

SFNet: A slow feature extraction network for parallel linear and nonlinear dynamic process monitoring.
Neurocomputing, 2022

2020
Parallel Extraction of Long-term Trends and Short-term Fluctuation Framework for Multivariate Time Series Forecasting.
CoRR, 2020

Multivariate Time Series Forecasting Based on Causal Inference with Transfer Entropy and Graph Neural Network.
CoRR, 2020


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