| Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring SJ Qin, Y Dong, Q Zhu, J Wang, Q Liu Annual Reviews in Control 50, 29-48, 2020 | 115 | 2020 |
| Concurrent quality and process monitoring with canonical correlation analysis Q Zhu, Q Liu, SJ Qin Journal of Process Control 60, 95-103, 2017 | 104 | 2017 |
| Dynamic concurrent kernel CCA for strip-thickness relevant fault diagnosis of continuous annealing processes Q Liu, Q Zhu, SJ Qin, T Chai Journal of process control 67, 12-22, 2018 | 61 | 2018 |
| Dynamic latent variable regression for inferential sensor modeling and monitoring Q Zhu, SJ Qin, Y Dong Computers & Chemical Engineering 137, 106809, 2020 | 46 | 2020 |
| Fault detection and diagnosis with a novel source-aware autoencoder and deep residual neural network N Amini, Q Zhu Neurocomputing 488, 618-633, 2022 | 45 | 2022 |
| A machine learning approach for modeling and optimization of a CO2 post-combustion capture unit A Shalaby, A Elkamel, PL Douglas, Q Zhu, QP Zheng Energy 215, 119113, 2020 | 45 | 2020 |
| Machine learning and metaheuristic methods for renewable power forecasting: a recent review H Alkabbani, A Ahmadian, Q Zhu, A Elkamel Frontiers in Chemical Engineering 3, 665415, 2021 | 43 | 2021 |
| Supervised diagnosis of quality and process faults with canonical correlation analysis Q Zhu, SJ Qin Industrial & Engineering Chemistry Research 58 (26), 11213-11223, 2019 | 43 | 2019 |
| Circular genetic operators based RNA genetic algorithm for modeling proton exchange membrane fuel cells Q Zhu, N Wang, L Zhang International journal of hydrogen energy 39 (31), 17779-17790, 2014 | 40 | 2014 |
| An improved air quality index machine learning-based forecasting with multivariate data imputation approach H Alkabbani, A Ramadan, Q Zhu, A Elkamel Atmosphere 13 (7), 1144, 2022 | 34 | 2022 |
| Concurrent canonical correlation analysis modeling for quality-relevant monitoring Q Zhu, Q Liu, SJ Qin IFAC-PapersOnLine 49 (7), 1044-1049, 2016 | 32 | 2016 |
| A novel multi-mode Bayesian method for the process monitoring and fault diagnosis of coal mills W Fan, S Ren, Q Zhu, Z Jia, D Bai, F Si IEEE Access 9, 22914-22926, 2021 | 23 | 2021 |
| Online quality-relevant monitoring with dynamic weighted partial least squares B Xu, Q Zhu Industrial & Engineering Chemistry Research 59 (48), 21124-21132, 2020 | 19 | 2020 |
| Quality-relevant fault detection of nonlinear processes based on kernel concurrent canonical correlation analysis Q Zhu, Q Liu, SJ Qin 2017 American Control Conference (ACC), 5404-5409, 2017 | 17 | 2017 |
| Dynamic probabilistic predictable feature analysis for multivariate temporal process monitoring W Fan, Q Zhu, S Ren, L Zhang, F Si IEEE Transactions on Control Systems Technology 30 (6), 2573-2584, 2022 | 16 | 2022 |
| Latent variable regression for supervised modeling and monitoring Q Zhu IEEE/CAA Journal of Automatica Sinica 7 (3), 800-811, 2020 | 13 | 2020 |
| Concurrent monitoring and diagnosis of process and quality faults with canonical correlation analysis Q Zhu, Q Liu, SJ Qin IFAC-PapersOnLine 50 (1), 7999-8004, 2017 | 13 | 2017 |
| Concurrent auto-regressive latent variable model for dynamic anomaly detection B Xu, Q Zhu Journal of Process Control 108, 1-11, 2021 | 11 | 2021 |
| Improved manifold sparse slow feature analysis for process monitoring H Saafan, Q Zhu Computers & Chemical Engineering 164, 107905, 2022 | 8 | 2022 |
| Latent Variable Regression for Process and Quality Modeling Q Zhu, J Qin 2019 1st International Conference on Industrial Artificial Intelligence (IAI), 2019 | 8 | 2019 |
S. Joe QinLingnan University, Hong Kong, President, Member of EASA, Fellow of HKAES, NAI, IEEE, IFAC, AIChEVerified email at ln.edu.hk
