Weiming Shao
Orcid: 0000-0002-1291-3072
According to our database1,
Weiming Shao
authored at least 23 papers
between 2017 and 2024.
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Bibliography
2024
Virtual Sensing of Key Variables in the Hydrogen Production Process: A Comparative Study of Data-Driven Models.
Sensors, May, 2024
A mixture of shallow neural networks for virtual sensing: Could perform better than deep neural networks.
Expert Syst. Appl., 2024
A novel semi-supervised robust learning framework for dynamic generative latent variable models and its application to industrial virtual metrology.
Adv. Eng. Informatics, 2024
2023
Research and Development of a Multi-State Non-Invasive Monitoring System for Cluster Motors.
Proceedings of the 49th Annual Conference of the IEEE Industrial Electronics Society, 2023
2022
Block-Wise Parallel Semisupervised Linear Dynamical System for Massive and Inconsecutive Time-Series Data With Application to Soft Sensing.
IEEE Trans. Instrum. Meas., 2022
Eng. Appl. Artif. Intell., 2022
2021
Hierarchical Quality Monitoring for Large-Scale Industrial Plants With Big Process Data.
IEEE Trans. Neural Networks Learn. Syst., 2021
Virtual Sensor Development for Multioutput Nonlinear Processes Based on Bilinear Neighborhood Preserving Regression Model With Localized Construction.
IEEE Trans. Ind. Informatics, 2021
Dynamic Variational Bayesian Student's T Mixture Regression With Hidden Variables Propagation for Industrial Inferential Sensor Development.
IEEE Trans. Ind. Informatics, 2021
IEEE Trans. Cybern., 2021
2020
Semisupervised Robust Modeling of Multimode Industrial Processes for Quality Variable Prediction Based on Student's t Mixture Model.
IEEE Trans. Ind. Informatics, 2020
IEEE Trans. Ind. Informatics, 2020
Bayesian Nonlinear Gaussian Mixture Regression and its Application to Virtual Sensing for Multimode Industrial Processes.
IEEE Trans Autom. Sci. Eng., 2020
An Efficient Modeling and Optimization Approach for Pressure Testing During Reentry Operation of Deepwater Drilling Riser System.
IEEE Access, 2020
2019
Parallel Computing and SGD-Based DPMM For Soft Sensor Development With Large-Scale Semisupervised Data.
IEEE Trans. Ind. Electron., 2019
Soft-Sensor Development for Processes With Multiple Operating Modes Based on Semisupervised Gaussian Mixture Regression.
IEEE Trans. Control. Syst. Technol., 2019
Robust inferential sensor development based on variational Bayesian Student's-<i>t</i> mixture regression.
Neurocomputing, 2019
Robust Supervised Probabilistic Factor Analysis and Its Application to Industrial Soft Sensor Modeling.
IEEE Access, 2019
Robust Soft Sensing for Multi-mode Processes Based on Bayesian Regularized Student's-t Mixture Regression.
Proceedings of the 12th Asian Control Conference, 2019
Proceedings of the 12th Asian Control Conference, 2019
2018
Student's-<i>t</i> Mixture Regression-Based Robust Soft Sensor Development for Multimode Industrial Processes.
Sensors, 2018
Adaptive Soft Sensor Development for Multi-Output Industrial Processes Based on Selective Ensemble Learning.
IEEE Access, 2018
2017
Semi-supervised selective ensemble learning based on distance to model for nonlinear soft sensor development.
Neurocomputing, 2017