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2020 – today
- 2024
[j20]Wenxue Han
, Weiming Shao, Chihang Wei, Wei Song, Can Chen, Junghui Chen:
A novel semi-supervised robust learning framework for dynamic generative latent variable models and its application to industrial virtual metrology. Adv. Eng. Informatics 62: 102775 (2024)- [j19]Weiming Shao, Xu Li, Yupeng Xing, Junghui Chen:
A mixture of shallow neural networks for virtual sensing: Could perform better than deep neural networks. Expert Syst. Appl. 256: 124870 (2024) - [j18]Yating Yao, Yupeng Xing, Ziteng Zuo, Chihang Wei, Weiming Shao:
Virtual Sensing of Key Variables in the Hydrogen Production Process: A Comparative Study of Data-Driven Models. Sensors 24(10): 3143 (2024) - 2023
- [c3]Zhisheng Lv, Delin Chen, Weiming Shao:
Research and Development of a Multi-State Non-Invasive Monitoring System for Cluster Motors. IECON 2023: 1-6 - 2022
- [j17]Saite Fan, Xinmin Zhang, Zhihuan Song, Weiming Shao:
Cumulative dual-branch network framework for long-tailed multi-class classification. Eng. Appl. Artif. Intell. 114: 105080 (2022) - [j16]Weiming Shao, Yougao Li, Wenxue Han, Dongya Zhao:
Block-Wise Parallel Semisupervised Linear Dynamical System for Massive and Inconsecutive Time-Series Data With Application to Soft Sensing. IEEE Trans. Instrum. Meas. 71: 1-14 (2022) - 2021
- [j15]Weiming Shao, Zhiqiang Ge, Zhihuan Song:
Semisupervised Bayesian Gaussian Mixture Models for Non-Gaussian Soft Sensor. IEEE Trans. Cybern. 51(7): 3455-3468 (2021) - [j14]Chihang Wei, Weiming Shao, Zhihuan Song:
Virtual Sensor Development for Multioutput Nonlinear Processes Based on Bilinear Neighborhood Preserving Regression Model With Localized Construction. IEEE Trans. Ind. Informatics 17(4): 2500-2510 (2021) - [j13]Jingbo Wang, Weiming Shao, Xinmin Zhang, Zhihuan Song:
Dynamic Variational Bayesian Student's T Mixture Regression With Hidden Variables Propagation for Industrial Inferential Sensor Development. IEEE Trans. Ind. Informatics 17(8): 5314-5324 (2021) - [j12]Le Yao, Weiming Shao, Zhiqiang Ge:
Hierarchical Quality Monitoring for Large-Scale Industrial Plants With Big Process Data. IEEE Trans. Neural Networks Learn. Syst. 32(8): 3330-3341 (2021) - 2020
- [j11]Kang Liu, Guoming Chen, Weiming Shao:
An Efficient Modeling and Optimization Approach for Pressure Testing During Reentry Operation of Deepwater Drilling Riser System. IEEE Access 8: 74759-74770 (2020) - [j10]Weiming Shao, Zhiqiang Ge, Le Yao, Zhihuan Song:
Bayesian Nonlinear Gaussian Mixture Regression and its Application to Virtual Sensing for Multimode Industrial Processes. IEEE Trans Autom. Sci. Eng. 17(2): 871-885 (2020) - [j9]Weiming Shao, Zhiqiang Ge, Zhihuan Song:
Bayesian Just-in-Time Learning and Its Application to Industrial Soft Sensing. IEEE Trans. Ind. Informatics 16(4): 2787-2798 (2020) - [j8]Weiming Shao, Zhiqiang Ge, Zhihuan Song, Jingbo Wang:
Semisupervised Robust Modeling of Multimode Industrial Processes for Quality Variable Prediction Based on Student's t Mixture Model. IEEE Trans. Ind. Informatics 16(5): 2965-2976 (2020)
2010 – 2019
- 2019
- [j7]Weiming Shao, Hongwei Zhang, Zhihuan Song:
Robust Supervised Probabilistic Factor Analysis and Its Application to Industrial Soft Sensor Modeling. IEEE Access 7: 184038-184052 (2019) - [j6]Jingbo Wang, Weiming Shao, Zhihuan Song:
Robust inferential sensor development based on variational Bayesian Student's-t mixture regression. Neurocomputing 369: 11-28 (2019) - [j5]Weiming Shao, Zhiqiang Ge, Zhihuan Song:
Soft-Sensor Development for Processes With Multiple Operating Modes Based on Semisupervised Gaussian Mixture Regression. IEEE Trans. Control. Syst. Technol. 27(5): 2169-2181 (2019) - [j4]Weiming Shao, Le Yao, Zhiqiang Ge, Zhihuan Song:
Parallel Computing and SGD-Based DPMM For Soft Sensor Development With Large-Scale Semisupervised Data. IEEE Trans. Ind. Electron. 66(8): 6362-6373 (2019) - [c2]Weiming Shao, Zhiqiang Ge, Zhihuan Song, Li Zhao:
Nonlinear Inferential Sensor Development Based on GMM-ELM. ASCC 2019: 867-872 - [c1]Jingbo Wang, Weiming Shao, Zhihuan Song:
Robust Soft Sensing for Multi-mode Processes Based on Bayesian Regularized Student's-t Mixture Regression. ASCC 2019: 873-878 - 2018
- [j3]Weiming Shao, Sheng Chen, Chris J. Harris:
Adaptive Soft Sensor Development for Multi-Output Industrial Processes Based on Selective Ensemble Learning. IEEE Access 6: 55628-55642 (2018) - [j2]Jingbo Wang, Weiming Shao, Zhihuan Song:
Student's-t Mixture Regression-Based Robust Soft Sensor Development for Multimode Industrial Processes. Sensors 18(11): 3968 (2018) - 2017
- [j1]Weiming Shao, Xuemin Tian:
Semi-supervised selective ensemble learning based on distance to model for nonlinear soft sensor development. Neurocomputing 222: 91-104 (2017)
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