| 1 |
Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder
Wang YL, Yang HB, Yuan XF, Shardt YAW, Yang CH, Gui WH
Journal of Process Control, 92, 79, 2020 |
| 2 |
Soft sensor design for variable time delay and variable sampling time
Griesing-Scheiwe F, Shardt YAW, Perez-Zuniga G, Yang X
Journal of Process Control, 92, 310, 2020 |
| 3 |
Using normal probability plots to determine parameters for higher-level factorial experiments with orthogonal and orthonormal bases
Donnelly T, Shardt YAW
Canadian Journal of Chemical Engineering, 97(1), 152, 2019 |
| 4 |
Modelling the strip thickness in hot steel rolling mills using least-squares support vector machines
Shardt YAW, Mehrkanoon S, Zhang K, Yang X, Suykens J, Ding SX, Peng KX
Canadian Journal of Chemical Engineering, 96(1), 171, 2018 |
| 5 |
Quantisation and data quality: Implications for system identification
Shardt YAW, Yang X, Ding SX
Journal of Process Control, 40, 13, 2016 |
| 6 |
Improved canonical correlation analysis-based fault detection methods for industrial processes
Chen ZW, Zhang K, Ding SX, Shardt YAW, Hu ZK
Journal of Process Control, 41, 26, 2016 |
| 7 |
Minimal required excitation for closed-loop identification: Some implications for data-driven, system identification
Shardt YAW, Huang BA, Ding SX
Journal of Process Control, 27, 22, 2015 |
| 8 |
Data quality assessment of routine operating data for process identification
Shardt YAW, Huang B
Computers & Chemical Engineering, 55, 19, 2013 |
| 9 |
Tuning a Soft Sensor's Bias Update Term. 1. The Open-Loop Case
Shardt YAW, Huang B
Industrial & Engineering Chemistry Research, 51(13), 4958, 2012 |
| 10 |
Tuning a Soft Sensor's Bias Update Term. 2. The Closed-Loop Case
Shardt YAW, Huang B
Industrial & Engineering Chemistry Research, 51(13), 4968, 2012 |
