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ORCIDS. Joe Qin
Si-Zhao Joe Qin
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2020 – today
- 2024
[j46]Qiang Liu
, Chao Yang, S. Joe Qin:
Semi-Supervised Dynamic Latent Variable Regression for Prediction and Quality-Relevant Fault Monitoring. IEEE Trans. Control. Syst. Technol. 32(4): 1156-1168 (2024)- [j45]Xu Chen, Xiao He, Si-Zhao Joe Qin:
Bidirectional Dynamic Latent Variable Analysis for Closed-Loop Process Monitoring. IEEE Trans. Ind. Electron. 71(8): 9823-9833 (2024) - [c48]Xinting Zhu, Lishuai Li, Yanfang Mo, Yining Dong, Xuejin X. Shen, Xiaoyu Chen, S. Joe Qin:
Data-driven approaches for satellite SADA system health monitoring with limited data. CASE 2024: 3225-3230 - [c47]Shiqin Tang, Yining Dong, S. Joe Qin:
A PLS-Integrated Lasso Method With Application in Index Tracking. ICASSP 2024: 6520-6524 - [i9]Yanfang Mo, S. Joe Qin:
Probabilistic Reduced-Dimensional Vector Autoregressive Modeling with Oblique Projections. CoRR abs/2401.07206 (2024) - [i8]Lingling Xu, Haoran Xie, S. Joe Qin, Fu Lee Wang, Xiaohui Tao:
Exploring ChatGPT-based Augmentation Strategies for Contrastive Aspect-based Sentiment Analysis. CoRR abs/2409.11218 (2024) - 2023
- [j44]Jicheng Li, S. Joe Qin:
Applying and dissecting LSTM neural networks and regularized learning for dynamic inferential modeling. Comput. Chem. Eng. 175: 108264 (2023) - [j43]Yongjian Wang, Cheng Qian, Si-Zhao Joe Qin:
Attention-mechanism based DiPLS-LSTM and its application in industrial process time series big data prediction. Comput. Chem. Eng. 176: 108296 (2023) - [j42]Yiren Liu, S. Joe Qin:
Knowledge-Informed Sparse Learning for Relevant Feature Selection and Optimal Quality Prediction. IEEE Trans. Ind. Informatics 19(12): 11499-11507 (2023) - [c46]Jiaxin Yu, S. Joe Qin:
Low-Dimensional Latent State Space Identification with Application to the Shell Control Process. CCTA 2023: 1004-1009 - [c45]Jiaxin Yu, Yanfang Mo, S. Joe Qin:
Latent Dynamic Networked System Identification with High-Dimensional Networked Data. CDC 2023: 461-466 - [c44]Yanfang Mo, Jiaxin Yu, S. Joe Qin:
Probabilistic Reduced-Dimensional Vector Autoregressive Modeling for Dynamics Prediction and Reconstruction with Oblique Projections. CDC 2023: 7623-7628 - [c43]Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S. Joe Qin, Hongwei Zhao:
MLPST: MLP is All You Need for Spatio-Temporal Prediction. CIKM 2023: 3381-3390 - [i7]Yanfang Mo, Jiaxin Yu, S. Joe Qin:
Probabilistic Reduced-Dimensional Vector Autoregressive Modeling for Dynamics Prediction and Reconstruction with Oblique Projections. CoRR abs/2309.01161 (2023) - [i6]Zijian Zhang, Ze Huang, Zhiwei Hu, Xiangyu Zhao, Wanyu Wang, Zitao Liu, Junbo Zhang, S. Joe Qin, Hongwei Zhao:
MLPST: MLP is All You Need for Spatio-Temporal Prediction. CoRR abs/2309.13363 (2023) - [i5]Jiaxin Yu, Yanfang Mo, S. Joe Qin:
Latent Dynamic Networked System Identification with High-Dimensional Networked Data. CoRR abs/2309.17136 (2023) - [i4]Lingling Xu, Haoran Xie, Si-Zhao Joe Qin, Xiaohui Tao, Fu Lee Wang:
Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment. CoRR abs/2312.12148 (2023) - 2022
- [j41]Jiaxin Yu, S. Joe Qin:
Latent State Space Modeling of High-Dimensional Time Series With a Canonical Correlation Objective. IEEE Control. Syst. Lett. 6: 3469-3474 (2022) - [j40]Jicong Fan, Tommy W. S. Chow, S. Joe Qin:
Kernel-Based Statistical Process Monitoring and Fault Detection in the Presence of Missing Data. IEEE Trans. Ind. Informatics 18(7): 4477-4487 (2022) - 2021
- [j39]Si-Zhao Joe Qin, Yingxiang Liu, Yining Dong:
Plant-wide troubleshooting and diagnosis using dynamic embedded latent feature analysis. Comput. Chem. Eng. 152: 107392 (2021) - [j38]Si-Zhao Joe Qin, Siyi Guo, Zheyu Li, Leo H. Chiang, Ivan Castillo, Birgit Braun, Zhenyu Wang:
Integration of process knowledge and statistical learning for the Dow data challenge problem. Comput. Chem. Eng. 153: 107451 (2021) - [j37]Tao Yang, Jinliang Ding, Kyriakos G. Vamvoudakis, S. Joe Qin:
Guest Editorial: Industrial Artificial Intelligence for Smart Manufacturing. IEEE Trans. Ind. Informatics 17(12): 8319-8323 (2021) - [j36]Feng Qian, Yaochu Jin, Si-Zhao Joe Qin, Kai Sundmacher:
Guest Editorial Special Issue on Deep Integration of Artificial Intelligence and Data Science for Process Manufacturing. IEEE Trans. Neural Networks Learn. Syst. 32(8): 3294-3295 (2021) - [c42]S. Joe Qin:
A Non-iterative Partial Least Squares Algorithm for Supervised Learning with Collinear Data. CDC 2021: 3683-3688 - [c41]S. Joe Qin:
Latent Vector Autoregressive Modeling for Reduced Dimensional Dynamic Feature Extraction and Prediction. CDC 2021: 3689-3694 - [c40]Yanfang Mo, Qiulin Lin, Minghua Chen, Si-Zhao Joe Qin:
Optimal Online Algorithms for Peak-Demand Reduction Maximization with Energy Storage. e-Energy 2021: 73-83 - [c39]Yanfang Mo, Qiulin Lin, Minghua Chen, Si-Zhao Joe Qin:
Optimal Peak-Minimizing Online Algorithms for Large-Load Users with Energy Storage. INFOCOM Workshops 2021: 1-2 - [i3]Yanfang Mo, Qiulin Lin, Minghua Chen, Si-Zhao Joe Qin:
Online Peak-Demand Minimization Using Energy Storage. CoRR abs/2103.00005 (2021) - [i2]Yanfang Mo, Qiulin Lin, Minghua Chen, Si-Zhao Joe Qin:
Optimal Online Algorithms for Peak-Demand Reduction Maximization with Energy Storage. CoRR abs/2105.04728 (2021) - 2020
- [j35]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) - [j34]Qinqin Zhu, Si-Zhao Joe Qin, Yining Dong:
Dynamic latent variable regression for inferential sensor modeling and monitoring. Comput. Chem. Eng. 137: 106809 (2020) - [j33]Juergen Hahn, Michael Baldea, Si-Zhao Joe Qin:
Editorial for Tom Edgar special issue in Computers & Chemical Engineering. Comput. Chem. Eng. 141: 107011 (2020) - [j32]Yuan Jin, Si-Zhao Joe Qin, Qiang Huang:
Modeling inter-layer interactions for out-of-plane shape deviation reduction in additive manufacturing. IISE Trans. 52(7): 721-731 (2020) - [j31]Ruonan Liu, Fei Wang, Boyuan Yang, Si-Zhao Joe Qin:
Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary Conditions. IEEE Trans. Ind. Informatics 16(6): 3797-3806 (2020) - [j30]Yining Dong, Yingxiang Liu, Si-Zhao Joe Qin:
Efficient Dynamic Latent Variable Analysis for High-Dimensional Time Series Data. IEEE Trans. Ind. Informatics 16(6): 4068-4076 (2020) - [c38]Yining Dong, Si-Zhao Joe Qin:
Dynamic-Inner Canonical Correlation Analysis based Process Monitoring. ACC 2020: 3553-3558 - [i1]Sungho Shin, Alex D. Smith, Si-Zhao Joe Qin, Victor M. Zavala:
On the Convergence of the Dynamic Inner PCA Algorithm. CoRR abs/2003.05928 (2020)
2010 – 2019
- 2019
- [j29]Si-Zhao Joe Qin, Leo H. Chiang:
Advances and opportunities in machine learning for process data analytics. Comput. Chem. Eng. 126: 465-473 (2019) - [j28]Zaiyue Yang, Tianci Guo, Pengcheng You, Yunhe Hou, Si-Zhao Joe Qin:
Distributed Approach for Temporal-Spatial Charging Coordination of Plug-in Electric Taxi Fleet. IEEE Trans. Ind. Informatics 15(6): 3185-3195 (2019) - [c37]Le Zhou, Zhiqiang Ge, Zhihuan Song, Si-Zhao Joe Qin:
Dynamic Processes Modeling and Monitoring based on a Novel Dynamic Latent Variable Model. CAA SAFEPROCESS 2019: 589-595 - 2018
- [j27]Yining Dong, Si-Zhao Joe Qin:
Dynamic latent variable analytics for process operations and control. Comput. Chem. Eng. 114: 69-80 (2018) - 2017
- [j26]Le Zhou, Gang Li, Zhihuan Song, Si-Zhao Joe Qin:
Autoregressive Dynamic Latent Variable Models for Process Monitoring. IEEE Trans. Control. Syst. Technol. 25(1): 366-373 (2017) - [j25]Qiang Liu, Si-Zhao Joe Qin, Tianyou Chai:
Unevenly Sampled Dynamic Data Modeling and Monitoring With an Industrial Application. IEEE Trans. Ind. Informatics 13(5): 2203-2213 (2017) - [c36]Zhaohui Zhang, Ruilong Deng, Tao Yuan, Si-Zhao Joe Qin:
Distributed optimization of multi-building energy systems with spatially and temporally coupled constraints. ACC 2017: 2913-2918 - [c35]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 - 2016
- [j24]Ning Sheng, Qiang Liu, Si-Zhao Joe Qin, Tianyou Chai:
Comprehensive Monitoring of Nonlinear Processes Based on Concurrent Kernel Projection to Latent Structures. IEEE Trans Autom. Sci. Eng. 13(2): 1129-1137 (2016) - [c34]Tao Yuan, Gang Li, Zhaohui Zhang, Si-Zhao Joe Qin:
Deep causal mining for plant-wide oscillations with multilevel Granger causality analysis. ACC 2016: 5056-5061 - [c33]Yuan Jin, Si-Zhao Joe Qin, Qiang Huang:
Prescriptive analytics for understanding of out-of-plane deformation in additive manufacturing. CASE 2016: 786-791 - [c32]Jian Tang, Tianyou Chai, Si-Zhao Joe Qin, Zhuo Liu, Wen Yu:
Feature selection based on concurrent projection to latent structures for high dimensional spectra data. ICIA 2016: 234-239 - 2015
- [c31]Yuan Jin, Si-Zhao Joe Qin, Qiang Huang:
Out-of-plane geometric error prediction for additive manufacturing. CASE 2015: 918-923 - [r1]Thomas A. Badgwell, Si-Zhao Joe Qin:
Model-Predictive Control in Practice. Encyclopedia of Systems and Control 2015 - 2014
- [j23]Tianyou Chai, Si-Zhao Joe Qin, Hong Wang:
Optimal operational control for complex industrial processes. Annu. Rev. Control. 38(1): 81-92 (2014) - [j22]Tianyou Chai, Hong Wang, Si-Zhao Joe Qin, Tongwen Chen, Sirish L. Shah:
Guest Editorial Integrated Optimization of Industrial Automation. IEEE Trans Autom. Sci. Eng. 11(4): 963-964 (2014) - [j21]Qiang Liu, Si-Zhao Joe Qin, Tianyou Chai:
Multiblock Concurrent PLS for Decentralized Monitoring of Continuous Annealing Processes. IEEE Trans. Ind. Electron. 61(11): 6429-6437 (2014) - [j20]Gang Li, Si-Zhao Joe Qin, Donghua Zhou:
A New Method of Dynamic Latent-Variable Modeling for Process Monitoring. IEEE Trans. Ind. Electron. 61(11): 6438-6445 (2014) - [c30]Gang Li, Si-Zhao Joe Qin, Tianyou Chai:
Multi-directional reconstruction based contributions for root-cause diagnosis of dynamic processes. ACC 2014: 3500-3505 - 2013
- [j19]Tim Salsbury, Prashant Mhaskar, Si-Zhao Joe Qin:
Predictive control methods to improve energy efficiency and reduce demand in buildings. Comput. Chem. Eng. 51: 77-85 (2013) - [j18]Qiang Liu, Si-Zhao Joe Qin, Tianyou Chai:
Decentralized Fault Diagnosis of Continuous Annealing Processes Based on Multilevel PCA. IEEE Trans Autom. Sci. Eng. 10(3): 687-698 (2013) - [c29]Jingran Ma, Si-Zhao Joe Qin, Tim Salsbury:
Experimental study of economic model predictive control in building energy systems. ACC 2013: 3753-3758 - 2012
- [j17]Si-Zhao Joe Qin:
Survey on data-driven industrial process monitoring and diagnosis. Annu. Rev. Control. 36(2): 220-234 (2012) - [j16]Ashish Singhal, Si-Zhao Joe Qin:
Discussion on: "Post-Optimality Approach to Prevent Cycling in Linear MPC Target Calculation". Eur. J. Control 18(6): 570-571 (2012) - [j15]Yiwei Cai, Erhan Kutanoglu, John J. Hasenbein, Si-Zhao Joe Qin:
Single-machine scheduling with advanced process control constraints. J. Sched. 15(2): 165-179 (2012) - [c28]Zhijie Sun, Si-Zhao Joe Qin, Ashish Singhal, Larry Megan:
Control performance monitoring via model residual assessment. ACC 2012: 2800-2805 - [c27]Jingran Ma, Si-Zhao Joe Qin, Tim Salsbury:
Model predictive control of building energy systems with balanced model reduction. ACC 2012: 3681-3686 - [c26]Yu Zhao, Zhijie Sun, Si-Zhao Joe Qin, Tianyou Chai:
Non-stationary Kalman filter parametrization of subspace models with applications to MPC. ACC 2012: 4813-4818 - [c25]Yefeng Liu, Tianyou Chai, Si-Zhao Joe Qin, Quan-Ke Pan, Shengxiang Yang:
Improved genetic algorithm for magnetic material two-stage multi-product production scheduling: A case study. CDC 2012: 2521-2526 - [c24]Si-Zhao Joe Qin, Yingying Zheng:
Concurrent projection to latent structures for output-relevant and input-relevant fault monitoring. CDC 2012: 7018-7023 - [c23]Jian Tang, Lijie Zhao, Yi-miao Li, Tianyou Chai, Si-Zhao Joe Qin:
Feature Selection of Frequency Spectrum for Modeling Difficulty to Measure Process Parameters. ISNN (2) 2012: 82-91 - 2011
- [j14]Gang Li, Carlos F. Alcala, Si-Zhao Joe Qin, Donghua Zhou:
Generalized Reconstruction-Based Contributions for Output-Relevant Fault Diagnosis With Application to the Tennessee Eastman Process. IEEE Trans. Control. Syst. Technol. 19(5): 1114-1127 (2011) - [j13]Gang Li, Baosheng Liu, Si-Zhao Joe Qin, Donghua Zhou:
Quality Relevant Data-Driven Modeling and Monitoring of Multivariate Dynamic Processes: The Dynamic T-PLS Approach. IEEE Trans. Neural Networks 22(12): 2262-2271 (2011) - [j12]Qiang Liu, Tianyou Chai, Hong Wang, Si-Zhao Joe Qin:
Data-Based Hybrid Tension Estimation and Fault Diagnosis of Cold Rolling Continuous Annealing Processes. IEEE Trans. Neural Networks 22(12): 2284-2295 (2011) - [c22]Wei Wang, Tianyou Chai, Lijie Zhao, Si-Zhao Joe Qin:
Soft sensing of sodium aluminate solution component concentrations via on-line clustering and fuzzy modeling. ACC 2011: 2468-2473 - [c21]Weijian Cheng, Jinliang Ding, Weijian Kong, Tianyou Chai, S. Joe Qin:
An adaptive chaotic PSO for parameter optimization and feature extraction of LS-SVM based modelling. ACC 2011: 3263-3268 - [c20]Zhijie Sun, S. Joe Qin, Ashish Singhal, Larry Megan:
Control performance monitoring of LP-MPC cascade systems. ACC 2011: 4422-4427 - [c19]Ricardo Dunia, Gary T. Rochelle, Si-Zhao Joe Qin:
Subspace system identification for CO2 recovery processes. CACSD 2011: 846-851 - [c18]Zhijie Sun, Yu Zhao, Si-Zhao Joe Qin:
Improving industrial mpc performance with data-driven disturbance modeling. CDC/ECC 2011: 1922-1927 - [c17]Jian Tang, Tianyou Chai, Wen Yu, Lijie Zhao, Si-Zhao Joe Qin:
KPCA based multi-spectral segments feature extraction and GA based Combinatorial optimization for frequency spectrum data modeling. CDC/ECC 2011: 5193-5198 - [c16]Ricardo Dunia, Gary T. Rochelle, Si-Zhao Joe Qin:
Modeling CO2 recovery for optimal dynamic operations. CDC/ECC 2011: 6475-6480 - 2010
- [j11]Gang Li, Si-Zhao Joe Qin, Donghua Zhou:
Geometric properties of partial least squares for process monitoring. Autom. 46(1): 204-210 (2010) - [j10]Yingwei Zhang, Hong Zhou, Si-Zhao Joe Qin, Tianyou Chai:
Decentralized Fault Diagnosis of Large-Scale Processes Using Multiblock Kernel Partial Least Squares. IEEE Trans. Ind. Informatics 6(1): 3-10 (2010) - [c15]Quan Shen, S. Joe Qin, Ken J. Doniger:
Online dropout detection in subcutaneously implanted continuous glucose monitoring. ACC 2010: 4373-4378 - [c14]Carlos F. Alcala, S. Joe Qin:
Reconstruction-based contribution for process monitoring with kernel principal component analysis. ACC 2010: 7022-7027 - [c13]Si-Zhao Joe Qin, Yu Zhao, Zhijie Sun, Tao Yuan:
Progressive Parametrization in Subspace Identification Models with finite horizons. CDC 2010: 2819-2824 - [c12]Qiang Liu, Tianyou Chai, Hong Wang, Si-Zhao Joe Qin:
Tension soft sensor of continuous annealing lines using cascade frequency domain observer with combined PCA and neural networks error compensation. CDC 2010: 6528-6533
2000 – 2009
- 2009
- [j9]Carlos F. Alcala, Si-Zhao Joe Qin:
Reconstruction-based contribution for process monitoring. Autom. 45(7): 1593-1600 (2009) - 2007
- [j8]Si-Zhao Joe Qin:
Subspace methods for system identification: Tohru Katayama; Springer-Verlag, ISBN: 1-85233-981-0. Autom. 43(4): 748-749 (2007) - [c11]Gregory A. Cherry, Si-Zhao Joe Qin:
Monitoring Non-normal Data with Principal Component Analysis and Adaptive Density Estimation. CDC 2007: 352-359 - 2006
- [j7]Jin Wang, Si-Zhao Joe Qin:
Closed-loop subspace identification using the parity space. Autom. 42(2): 315-320 (2006) - [j6]Si-Zhao Joe Qin:
An overview of subspace identification. Comput. Chem. Eng. 30(10-12): 1502-1513 (2006) - 2005
- [j5]Si-Zhao Joe Qin, Weilu Lin, Lennart Ljung:
A novel subspace identification approach with enforced causal models. Autom. 41(12): 2043-2053 (2005) - 2004
- [j4]Insik Chin, Si-Zhao Joe Qin, Kwang S. Lee, Moonki Cho:
A two-stage iterative learning control technique combined with real-time feedback for independent disturbance rejection. Autom. 40(11): 1913-1922 (2004) - [j3]D. Wang, D. H. Zhou, Y. H. Jin, Si-Zhao Joe Qin:
A strong tracking predictor for nonlinear processes with input time delay. Comput. Chem. Eng. 28(12): 2523-2540 (2004) - [c10]Weilu Lin, S. Joe Qin, Lennart Ljung:
On consistency of closed-loop subspace identification with innovation estimation. CDC 2004: 2195-2200 - [c9]Christopher A. Harrison, Richard Good, Daniel Kadosh, S. Joe Qin:
A multi-step supervisory control strategy for semiconductor device manufacturing. CDC 2004: 4237-4242 - 2003
- [c8]Henry G. Potrykus, Frank Allgöwer, Si-Zhao Joe Qin:
The Character of an Idempotent-analytic Nonlinear Small Gain Theorem. POSTA 2003: 361-368 - 2002
- [j2]Josep Maria Mirats i Tur, François E. Cellier, Rafael M. Huber, Si-Zhao Joe Qin:
On the selection of variables for qualitative modelling of dynamical systems. Int. J. Gen. Syst. 31(5): 435-467 (2002) - [c7]Richard Good, S. Joe Qin:
Stability analysis of double EWMA run-to-run control with metrology delay. ACC 2002: 2156-2161 - [c6]S. Joe Qin, Glen W. Scheid, Terrence J. Riley:
Adaptive run to run control for intermittent batch operations. ACC 2002: 2168-2173 - [c5]Richard Good, Jürgen Hahn, Thomas Edison, S. Joe Qin:
Drug dosage adjustment via run-to-run control. ACC 2002: 4044-4049 - [c4]Elaine T. Hale, S. Joe Qin:
Subspace model predictive control and a case study. ACC 2002: 4758-4763 - 2001
- [c3]Uwe Kruger, X. Wang, Q. Chen, S. Joe Qin:
An alternative PLS algorithm for the monitoring of industrial process. ACC 2001: 4455-4459 - [c2]Sergio Valle, S. Joe Qin, Michael J. Piovoso:
Extracting fault subspaces for fault identification of a polyester film process. ACC 2001: 4466-4471 - [c1]Jin Wang, S. Joe Qin:
Principal component analysis for errors-in-variables subspace identification. CDC 2001: 3936-3941
1990 – 1999
- 1994
- [j1]Si-Zhao Joe Qin, Guy Borders:
A multiregion fuzzy logic controller for nonlinear process control. IEEE Trans. Fuzzy Syst. 2(1): 74-81 (1994)
Coauthor Index
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