Journals

24. Xu, B.* and Zhu, Q., 2022. Multi-step dynamic slow feature analysis for anomaly detection. Submitted to IEEE Transactions on Cybernetics. 

23. Alkabbani, H*, Ramadan, A, Zhu, Q. and Elkamel, A., 2022. An improved air quality index machine learning-based forecasting with multivariate data imputation approach. Submitted to Atmosphere. 

22. Alkabbani, H*, Hourfar F., Zhu, Q., Almansoori, A. and Elkamel, A., 2022. Machine learning-based time series modeling for regional wind power forecasting. Submitted to Applied Energy. 

21. Xu, B.* and Zhu, Q., 2022. An efficient dynamic auto-regressive CCA for time series imputation with irregular samplings. Submitted to IEEE Transactions on Instrumentation & Measurement.

20. Amini, N.* and Zhu, Q., 2022. SA²C: Self-adversarial autoencoding classifier for unsupervised anomaly detection. Submitted to Computers & Chemical Engineering. 

19. Saafan, H.* and Zhu, Q., 2022. Improved sparse slow feature analysis for process monitoring with manifold optimization. Computers & Chemical Engineering, accepted.

18. Fan, W.*, Zhu, Q., Ren, S., Zhang, L. and Si, F., 2022. Dynamic probabilistic predictable feature analysis for high dimensional temporal monitoring. IEEE Transactions on Control Systems Technology. 

17. Fan, W.*, Zhu, Q., Xu, B.*, Ren, S. and Si, F., 2022. Multivariate temporal process monitoring with graph-based predictable feature analysis. Canadian Journal of Chemical Engineering.

16. Fan, W.*, Zhu, Q., Ren, S., Zhang, L. and Si, F., 2022. Robust probabilistic predictable feature analysis and its application for dynamic process monitoring. Journal of Process Control, 112, pp. 21-35. 

15. Zhang, H.* and Zhu, Q., 2022. Concurrent multilayer fault monitoring with nonlinear latent variable regression. Industrial & Engineering Chemistry Research. 

14. Amini, N.* and Zhu, Q., 2022. Fault detection and diagnosis with a novel source-aware auto-encoder and deep residual neural network. Neurocomputing, 488, pp.618-633.

13. Xu, B.* and Zhu, Q., 2021. Concurrent auto-regressive latent variable model for dynamic anomaly detection. Journal of Process Control, 108, pp.1-11.

12. Alkabbani, H.*, Ahmadian, A., Zhu, Q. and Elkamel, A., 2021. Machine learning and metaheuristic methods for renewable power forecasting: A recent review. Frontiers in Chemical Engineering, 3, p.14.

11. Fan, W.*, Ren, S., Zhu, Q., Wang, P., Jia, Z., Bai, D. and Si, F., 2021. A novel multi-mode Bayesian method for the process monitoring and fault diagnosis of coal mills. IEEE Access.

10. Xu, B.* and Zhu, Q., 2020. Online quality-relevant monitoring with dynamic weighted partial least squares. Industrial & Engineering Chemistry Research, 59(48), pp.21124-21132.

9. Shalaby, A.*, Elkamel, A., Douglas, P.L., Zhu, Q. and Zheng, Q.P., 2020. A machine learning approach for modeling and optimization of a CO₂ post-combustion capture unit. Energy, p.119113.

8. Qin, S.J., Dong Y., Zhu, Q., Wang, J. and Liu, Q., 2020. Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring. Annual Reviews in Control.  

7. Zhu, Q., 2020. Auto-regressive modeling with dynamic weighted canonical correlation analysis. Journal of Process Control, 95, pp.32-44.

6. Zhu, Q., 2020. Latent variable regression for supervised modeling and monitoring. IEEE/CAA Journal of Automatica Sinica, 7(3), pp.800-811.

5. Zhu, Q., Qin, S.J. and Dong, Y., 2020. Dynamic latent variable regression for inferential sensor modeling and monitoring. Computers & Chemical Engineering, p.106809.

4. Zhu, Q. and Qin, S.J., 2019. Supervised diagnosis of quality and process faults with canonical correlation analysis. Industrial & Engineering Chemistry Research.

3. Liu, Q., Zhu, Q., Qin, S.J. and Chai, T., 2018. Dynamic concurrent kernel CCA for strip-thickness relevant fault diagnosis of continuous annealing processes. Journal of Process Control, 67, pp.12-22.

2. Zhu, Q., Liu, Q. and Qin, S.J., 2017. Concurrent quality and process monitoring with canonical correlation analysis. Journal of Process Control, 60, pp.95-103.

1. Zhu, Q., Wang, N. and Zhang, L., 2014. Circular genetic operators based RNA genetic algorithm for modeling proton exchange membrane fuel cells. International Journal of Hydrogen Energy, 39(31), pp.17779-17790.

Conferences

14. Xu, B.* and Zhu, Q., 2022. Dynamic probabilistic latent variable model with exogenous variables for dynamic anomaly detection. Submitted to CDC 2022.

13. Amini, N.* and Zhu, Q., 2021. Source-aware auto-encoder and deep residual neural network for fault detection and diagnosis. CCEC 2021, accepted. 

12. Zhu, Q., Xu, B.* and Zhang, H.*, 2021. Modelling and monitoring with dynamic auto-regressive latent variable methods. Invited keynote talk for the session "Applied Artificial Intelligence, Big Data, and Data Analytics Methods for Next-Gen Manufacturing Efficiency II" at AIChE 2021. 

11. Zhu, Q., 2021. Dynamic autoregressive partial least squares for supervised modeling. IFAC-PapersOnLine, 54(7), pp.234-239.

10. Saafan, H.* and Zhu, Q., 2021. Comprehensive monitoring with incremental slow feature analysis. In 2021 American Control Conference (ACC) (pp. 917-922). IEEE.

9. Xu, B.* and Zhu, Q., 2020. Modeling and monitoring with dynamic weighted partial least squares. 2020 AIChE Annual Meeting. 

8. Zhu, Q., Liu, Q. and Qin, S.J., 2020. Dynamic weighted canonical correlation analysis for auto-regressive modeling. IFAC-PapersOnLine, 53(2), pp.200-205.

7. Zhu, Q., 2020. Supervised block-aware factorization machine for multi-block quality-relevant monitoring. IFAC-PapersOnLine, 53(2), pp.11283-11288.

6. Zhu, Q. and Qin, S.J., 2019, July. Latent variable regression for process and quality modeling. In 2019 1st International Conference on Industrial Artificial Intelligence (IAI) (pp. 1-6). IEEE.

5. Zhu, Q., Liu, Q. and Qin, S.J., 2017. Concurrent monitoring and diagnosis of process and quality faults with canonical correlation analysis. IFAC-PapersOnLine, 50(1), pp.7999-8004.

4. Zhu, Q., Liu, Q. and Qin, S.J., 2017, May. Quality-relevant fault detection of nonlinear processes based on kernel concurrent canonical correlation analysis. In 2017 American Control Conference (ACC) (pp. 5404-5409). IEEE.

3. Zhu, Q. and Qin, S.J., 2017. Dynamic latent variable regression for data modeling and monitoring. AIChE Annual Meeting.

2. Liu, Q., Zhu, Q., Qin, S.J. and Xu, Q., 2016, July. A comparison study of data-driven projection to latent structures modeling and monitoring methods on high-speed train operation. In 2016 35th Chinese Control Conference (CCC) (pp. 6734-6739). IEEE.

1. Zhu, Q., Liu, Q. and Qin, S.J., 2016. Concurrent canonical correlation analysis modeling for quality-relevant monitoring. IFAC-PapersOnLine, 49(7), pp.1044-1049.

Patents

1. Zhu, Q. and Wang, N., 2013. Fuel cell optimization modeling method of double loop crossover operation RNA genetic algorithm. CN Patent CN103279659 A.