Yuri A. W. Shardt

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Soavtorji

Ding, Steven X.AKS, University of Duisburg-EssenPreverjeni e-poštni naslov na uni-due.de
Chunhua YangCentral South UniversityPreverjeni e-poštni naslov na csu.edu.cn
Biao HuangUniversity of Alberta, CanadaPreverjeni e-poštni naslov na ualberta.ca
zhiwen chenCentral South universityPreverjeni e-poštni naslov na csu.edu.cn
Xiaofeng YuanCentral South UniversityPreverjeni e-poštni naslov na csu.edu.cn
Xinrui Gao (高新瑞)TU IlmenauPreverjeni e-poštni naslov na tu-ilmenau.de
Kevin BrooksWits, APC SMARTPreverjeni e-poštni naslov na apc-smart.com
Sirish L ShahUniversity of AlbertaPreverjeni e-poštni naslov na ualberta.ca
chen ouCentral South UniversityPreverjeni e-poštni naslov na csu.edu.cn
Johan SuykensProfessor, KU LeuvenPreverjeni e-poštni naslov na esat.kuleuven.be
Siamak MehrkanoonAssistant Professor, Utrecht UniversityPreverjeni e-poštni naslov na uu.nl
Jiarui CuiUniversity of Science and Technology BeijingPreverjeni e-poštni naslov na ustb.edu.cn
Gustavo Pérez-ZuñigaProfessor, Pontificia Universidad Católica del PerúPreverjeni e-poštni naslov na pucp.pe
Andrei TorgashovIACP FEB RASPreverjeni e-poštni naslov na iacp.dvo.ru
Hao LuoHarbin Institute of TechnologyPreverjeni e-poštni naslov na hit.edu.cn
Zhiqiang GeZhejiang University, Peng Cheng Laboratory, Southeast UniversityPreverjeni e-poštni naslov na seu.edu.cn
Fan YANG (杨帆)Tsinghua UniversityPreverjeni e-poštni naslov na tsinghua.edu.cn
Linlin LiUniversity of duisburg-EssenPreverjeni e-poštni naslov na uni-due.de
Christoph AmentControl Engineering, Univ. AugsburgPreverjeni e-poštni naslov na uni-a.de
Xu YangProfessor, University of Science & Technology BeijingPreverjeni e-poštni naslov na ustb.edu.cn

Spremljaj

Yuri A. W. Shardt

Professor, TU Ilmenau

Preverjeni e-poštni naslov na tu-ilmenau.de - Domača stran

soft sensors data mining system identification robust control fault detection


Naslov Razvrsti po navedbah Razvrsti po letniku Razvrsti po naslovu	Navedeno Navedeno	Leto
Deep learning with spatiotemporal attention-based LSTM for industrial soft sensor model development X Yuan, L Li, YAW Shardt, Y Wang, C Yang IEEE Transactions on Industrial Electronics 68 (5), 4404-4414, 2020	416	2020
A just-in-time-learning-aided canonical correlation analysis method for multimode process monitoring and fault detection Z Chen, C Liu, SX Ding, T Peng, C Yang, W Gui, YAW Shardt IEEE Transactions on Industrial Electronics 68 (6), 5259-5270, 2020	127	2020
Improved canonical correlation analysis-based fault detection methods for industrial processes Z Chen, K Zhang, SX Ding, YAW Shardt, Z Hu Journal of Process Control 41, 26-34, 2016	122	2016
Deep learning for fault-relevant feature extraction and fault classification with stacked supervised auto-encoder Y Wang, H Yang, X Yuan, YAW Shardt, C Yang, W Gui Journal of Process Control 92, 79-89, 2020	115	2020
Soft sensor model for dynamic processes based on multichannel convolutional neural network X Yuan, S Qi, YAW Shardt, Y Wang, C Yang, W Gui Chemometrics and Intelligent Laboratory Systems 203, 104050, 2020	112	2020
Quality-driven regularization for deep learning networks and its application to industrial soft sensors C Ou, H Zhu, YAW Shardt, L Ye, X Yuan, Y Wang, C Yang IEEE Transactions on Neural Networks and Learning Systems, 2022	95	2022
Determining the state of a process control system: Current trends and future challenges Y Shardt, Y Zhao, F Qi, K Lee, X Yu, B Huang, S Shah The Canadian Journal of Chemical Engineering 90 (2), 217-245, 2012	90	2012
A new soft-sensor-based process monitoring scheme incorporating infrequent KPI measurements YAW Shardt, H Hao, SX Ding IEEE Transactions on Industrial Electronics 62 (6), 3843-3851, 2014	86	2014
Statistics for Chemical and Process Engineers YAW Shardt Springer International Publishing, 2022	64	2022
Graph neural network-based fault diagnosis: A review Z Chen, J Xu, C Alippi, SX Ding, Y Shardt, T Peng, C Yang arXiv preprint arXiv:2111.08185, 2021	64	2021
A KPI-based process monitoring and fault detection framework for large-scale processes K Zhang, YAW Shardt, Z Chen, X Yang, SX Ding, K Peng ISA transactions 68, 276-286, 2017	56	2017
An incipient fault detection approach via detrending and denoising Z He, YAW Shardt, D Wang, B Hou, H Zhou, J Wang Control Engineering Practice 74, 1-12, 2018	55	2018
Closed-loop identification with routine operating data: Effect of time delay and sampling time YAW Shardt, B Huang Journal of Process Control 21 (7), 997-1010, 2011	48	2011
Closed-loop identification condition for ARMAX models using routine operating data YAW Shardt, B Huang Automatica 47 (7), 1534-1537, 2011	46	2011
A KPI-based soft sensor development approach incorporating infrequent, variable time delayed measurements X Yang, Y Zhang, YAW Shardt, X Li, J Cui, C Tong IEEE Transactions on Control Systems Technology 28 (6), 2523-2531, 2019	45	2019
Data quality assessment of routine operating data for process identification YAW Shardt, B Huang Computers & chemical engineering 55, 19-27, 2013	45	2013
Dynamic system modelling and process monitoring based on long-term dependency slow feature analysis X Gao, YAW Shardt Journal of Process Control 105, 27-47, 2021	41	2021
Estimating the unknown time delay in chemical processes S Mehrkanoon, YAW Shardt, JAK Suykens, SX Ding Engineering Applications of Artificial Intelligence 55, 219-230, 2016	39	2016
Modelling the strip thickness in hot steel rolling mills using least‐squares support vector machines YAW Shardt, S Mehrkanoon, K Zhang, X Yang, J Suykens, SX Ding, ... The Canadian Journal of Chemical Engineering 96 (1), 171-178, 2018	31	2018
Assessment of T2-and Q-statistics for detecting additive and multiplicative faults in multivariate statistical process monitoring K Zhang, SX Ding, YAW Shardt, Z Chen, K Peng Journal of the Franklin Institute 354 (2), 668-688, 2017	30	2017

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