Chenliang Liu
Orcid: 0000-0003-2983-3105
According to our database1,
Chenliang Liu
authored at least 28 papers
between 2021 and 2024.
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Bibliography
2024
Operating Condition Recognition of Industrial Flotation Processes Using Visual and Acoustic Bimodal Autoencoder With Manifold Learning.
IEEE Trans. Ind. Informatics, May, 2024
Blackout Missing Data Recovery in Industrial Time Series Based on Masked-Former Hierarchical Imputation Framework.
IEEE Trans Autom. Sci. Eng., April, 2024
Multiscale Feature Fusion and Semi-Supervised Temporal-Spatial Learning for Performance Monitoring in the Flotation Industrial Process.
IEEE Trans. Cybern., February, 2024
Interpretable Prediction Modeling for Froth Flotation via Stacked Graph Convolutional Network.
IEEE Trans. Artif. Intell., January, 2024
Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes.
IEEE CAA J. Autom. Sinica, January, 2024
Multirate-Former: An Efficient Transformer-Based Hierarchical Network for Multistep Prediction of Multirate Industrial Processes.
IEEE Trans. Instrum. Meas., 2024
Residual-aware deep attention graph convolutional network via unveiling data latent interactions for product quality prediction in industrial processes.
Expert Syst. Appl., 2024
Anomaly detection using large-scale multimode industrial data: An integration method of nonstationary kernel and autoencoder.
Eng. Appl. Artif. Intell., 2024
A task-oriented deep learning framework based on target-related transformer network for industrial quality prediction applications.
Eng. Appl. Artif. Intell., 2024
Genetic Algorithm Driven by Translational Mutation Operator for the Scheduling Optimization in the Steelmaking-Continuous Casting Production.
Proceedings of the Intelligent Information Processing XII, 2024
2023
Semi-supervised deep embedded clustering with pairwise constraints and subset allocation.
Neural Networks, July, 2023
Inf. Sci., June, 2023
Revolutionizing Flotation Process Working Condition Identification Based on Froth Audio.
IEEE Trans. Instrum. Meas., 2023
Data Mode Related Interpretable Transformer Network for Predictive Modeling and Key Sample Analysis in Industrial Processes.
IEEE Trans. Ind. Informatics, 2023
Semi-supervised LSTM with historical feature fusion attention for temporal sequence dynamic modeling in industrial processes.
Eng. Appl. Artif. Intell., 2023
Promoting Decision-Making in Industrial Flotation Process by Collaborating Multiple Flotation Cells.
Proceedings of the 49th Annual Conference of the IEEE Industrial Electronics Society, 2023
Evaluate the Correlation Between Electrocardiogram Age and Cardiovascular Disease Using a 12-lead ECG Dataset.
Proceedings of the Fuzzy Systems and Data Mining IX, 2023
Reinforcement Learning-based Operational Decision-Making in the Process Industry Using Multi-View Data.
Proceedings of the 62nd IEEE Conference on Decision and Control, 2023
Communication and Self-Learning Strategies Incorporated State Transition Algorithm for Optimization of Complex Systems.
Proceedings of the CAA Symposium on Fault Detection, 2023
2022
Layer-Wise Residual-Guided Feature Learning With Deep Learning Networks for Industrial Quality Prediction.
IEEE Trans. Instrum. Meas., 2022
Learning Deep Multimanifold Structure Feature Representation for Quality Prediction With an Industrial Application.
IEEE Trans. Ind. Informatics, 2022
A reduced nonstationary discrete convolution kernel for multimode process monitoring.
Int. J. Mach. Learn. Cybern., 2022
Dynamic historical information incorporated attention deep learning model for industrial soft sensor modeling.
Adv. Eng. Informatics, 2022
A multi-source transfer learning method for new mode monitoring in industrial processes.
Proceedings of the 8th International Conference on Control, 2022
2021
Deep Nonlinear Dynamic Feature Extraction for Quality Prediction Based on Spatiotemporal Neighborhood Preserving SAE.
IEEE Trans. Instrum. Meas., 2021
Deep learning with neighborhood preserving embedding regularization and its application for soft sensor in an industrial hydrocracking process.
Inf. Sci., 2021
Deep learning with nonlocal and local structure preserving stacked autoencoder for soft sensor in industrial processes.
Eng. Appl. Artif. Intell., 2021
Proceedings of the ICBBT 2021: 13th International Conference on Bioinformatics and Biomedical Technology, Xi'an, China, May 21, 2021