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2020 – today
- 2024
[j8]Bo Xu
, Qinqin Zhu:
An Efficient Dynamic Auto-Regressive CCA for Time Series Imputation With Irregular Sampling. IEEE Trans Autom. Sci. Eng. 21(1): 442-451 (2024)- 2023
- [c3]Bo Xu, Qinqin Zhu:
Dynamic Probabilistic Latent Variable Model with Exogenous Variables for Dynamic Anomaly Detection. ACC 2023: 3945-3950 - 2022
- [j7]Hussein Saafan, Qinqin Zhu:
Improved manifold sparse slow feature analysis for process monitoring. Comput. Chem. Eng. 164: 107905 (2022) - [j6]Nima Amini, Qinqin Zhu:
Fault detection and diagnosis with a novel source-aware autoencoder and deep residual neural network. Neurocomputing 488: 618-633 (2022) - [j5]Wei Fan, Qinqin Zhu, Shaojun Ren, Liang Zhang, Fengqi Si:
Dynamic Probabilistic Predictable Feature Analysis for Multivariate Temporal Process Monitoring. IEEE Trans. Control. Syst. Technol. 30(6): 2573-2584 (2022) - 2021
- [j4]Wei Fan, Shaojun Ren, Qinqin Zhu, Zhijun Jia, Delong Bai, Fengqi Si:
A Novel Multi-Mode Bayesian Method for the Process Monitoring and Fault Diagnosis of Coal Mills. IEEE Access 9: 22914-22926 (2021) - [c2]Hussein Saafan, Qinqin Zhu:
Comprehensive Monitoring with Incremental Slow Feature Analysis. ACC 2021: 917-922 - 2020
- [j3]Si-Zhao Joe Qin, Yining Dong, Qinqin Zhu, Jin Wang, Qiang Liu:
Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring. Annu. Rev. Control. 50: 29-48 (2020) - [j2]Qinqin Zhu, Si-Zhao Joe Qin, Yining Dong:
Dynamic latent variable regression for inferential sensor modeling and monitoring. Comput. Chem. Eng. 137: 106809 (2020) - [j1]Qinqin Zhu:
Latent variable regression for supervised modeling and monitoring. IEEE CAA J. Autom. Sinica 7(3): 800-811 (2020)
2010 – 2019
- 2017
- [c1]Qinqin Zhu, Qiang Liu, Si-Zhao Joe Qin:
Quality-relevant fault detection of nonlinear processes based on kernel concurrent canonical correlation analysis. ACC 2017: 5404-5409
Coauthor Index
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