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Jie Zhang 0005
Person information
- affiliation: University of Newcastle, UK
Other persons with the same name
- Jie Zhang — disambiguation page
- Jie Jennifer Zhang
(aka: Jie Zhang 0001) — University of Texas at Arlington (and 1 more)
- Jie Zhang 0002
- Jie Zhang 0003
- Jie Zhang 0004 — State Key Laboratory of Networking and Switching Technology, BUPT
- Jie Zhang 0006
- Jie Zhang 0007 (aka: Jie (Jerry) Zhang) — Google (and 1 more)
- Jie Zhang 0008
- Jie Zhang 0009 — University of British Columbia (and 1 more)
- Jie Zhang 0010
- Jie Zhang 0011 — Institute for Infocomm Research
- Jie Zhang 0012
- Jie Zhang 0013 — University of Maryland, Department of Geographical Sciences (and 1 more)
- Jie Zhang 0014 — North China University of Technology
- Jie Zhang 0015 — Magister Solutions (and 1 more)
- Jie Zhang 0016 — Sichuan Electric Power Research Institute, State Grid Corporation of China, Chengdu, China (and 1 more)
- Jie Zhang 0018 — University of Maryland, School of Business
- Jie Zhang 0019
- Jie Zhang 0020 — Beijing Institute of Electronic System Engineering, China
- Jie Zhang 0021 — Southeast University, Nanjing
- Jie Zhang 0022
- Jie Zhang 0023
- Jie Zhang 0024 — University of Technology, Sydney
- Jie Zhang 0025 — Oracle (and 1 more)
- Jie Zhang 0026 — Apple, CA, USA (and 2 more)
- Jie Zhang 0027
- Jie Zhang 0028
- Jie Zhang 0029
- Jie Zhang 0030
- Jie Zhang 0031
- Jie Zhang 0032
- Jie Zhang 0033
- Jie Zhang 0034
- Jie Zhang 0035 — Clemson University, Department of Physics, Computational Biophysics and Bioinformatics / Computer Science, SC, USA
- Jie Zhang 0036 — Wuhan University of Technology, School of Computer Science & Technology, China
- Jie Zhang 0038
- Jie Zhang 0039
- Jie Zhang 0040
- Jie Zhang 0041
- Jie Zhang 0042
- Jie Zhang 0043 — Hunan University, College of Computer Science and Electronics Engineering, Changsha, China
- Jie Zhang 0044
- Jie Zhang 0045 — Ohio State University, Department of Computer Science and Engineering, Columbus, OH, USA
- Jie Zhang 0046 — Chinese University of Hong Kong, Department of Computer Science and Engineering, CUhk REliable Computing Laboratory, Hong Kong
- Jie Zhang 0047 — City University London, UK
- Jie Zhang 0048
- Jie Zhang 0049
- Jie Zhang 0050
- Jie Zhang 0051
- Jie Zhang 0052
- Jie Zhang 0053
- Jie Zhang 0054
- Jie Zhang 0055
- Jie Zhang 0056
- Jie Zhang 0057 — Southwest University, School of Electronic and Information Engineering, Chongqing, China
- Jie Zhang 0058
- Jie Zhang 0059
- Jie Zhang 0060
- Jie Zhang 0061
- Jie Zhang 0062
- Jie Zhang 0063
- Jie Zhang 0064
- Jie Zhang 0065
- Jie Zhang 0066
- Jie Zhang 0067
- Jie Zhang 0068
- Jie Zhang 0069
- Jie Zhang 0070
- Jie Zhang 0071
- Jie Zhang 0072
- Jie Zhang 0073
- Jie Zhang 0074
- Jie Zhang 0075
- Jie Zhang 0076
- Jie Zhang 0077
- Jie Zhang 0078 — Tsinghua University, Department of Computer Science, Beijing, China
- Jie Zhang 0079
- Jie Zhang 0080
- Jie Zhang 0081
- Jie Zhang 0082
- Jie Zhang 0083
- Jie Zhang 0084
- Jie Zhang 0085 — Dalian University of Technology, School of Software Technology, Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, China
- Jie Zhang 0086
- Jie Zhang 0087
- Jie Zhang 0088
- Jie Zhang 0089
- Jie Zhang 0090
- Jie Zhang 0091 — Xidian University, Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xi'an, China
- Jie Zhang 0092 — University of Kentucky, Department of Radiology, Lexington, KY, USA
- Jie Zhang 0093
- Jie Zhang 0094
- Jie Zhang 0095
- Jie Zhang 0096 — University of Washington, WA, USA
- Jie Zhang 0097
- Jie Zhang 0098
- Jie Zhang 0099
- Jie Zhang 0100
- Jie Zhang 0101
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2020 – today
- 2024
- [c55]Samuel Sells, Jie Zhang:
Hybrid Mechanistic Neural Network Modelling of the Degree of Cure of Polymer Composite. ICAART (2) 2024: 614-621 - 2023
- [j47]Ruosen Qi, Jie Zhang
A Review on Data-Driven Condition Monitoring of Industrial Equipment. Algorithms 16(1): 9 (2023) - [j46]Danny Hartanto Djarum, Zainal Ahmad
Reduced Bayesian Optimized Stacked Regressor (RBOSR): A highly efficient stacked approach for improved air pollution prediction. Appl. Soft Comput. 144: 110466 (2023) - [j45]Qiangda Yang
A collaborative cuckoo search algorithm with modified operation mode. Eng. Appl. Artif. Intell. 121: 106006 (2023) - [j44]Jie Zhang, Shengbin Liao, Haofeng Zhang, Yang Long, Zheng Zhang
Data driven recurrent generative adversarial network for generalized zero shot image classification. Inf. Sci. 625: 536-552 (2023) - [j43]Fahad Al Kalbani, Jie Zhang
Inferential Composition Control of a Distillation Column Using Active Disturbance Rejection Control with Soft Sensors. Sensors 23(2): 1019 (2023) - [j42]Dehbiya Gherdaoui, Madiha Melha Yahoum, Selma Toumi, Hichem Tahraoui
The Effect of the Stationary Phase on Resolution in the HPLC-Based Separation of Racemic Mixtures Using Vancomycin as a Chiral Selector: A Case Study with Profen Nonsteroidal Anti-Inflammatory Drugs. Symmetry 15(12): 2154 (2023) - 2022
- [j41]Yiming Tian
A Novel Selective Ensemble Learning Method for Smartphone Sensor-Based Human Activity Recognition Based on Hybrid Diversity Enhancement and Improved Binary Glowworm Swarm Optimization. IEEE Access 10: 125027-125041 (2022) - [j40]Yiming Tian
Group Decision Making-Based Fusion for Human Activity Recognition in Body Sensor Networks. Sensors 22(21): 8225 (2022) - 2021
- [j39]Yiming Tian
A Novel Sensor-Based Human Activity Recognition Method Based on Hybrid Feature Selection and Combinational Optimization. IEEE Access 9: 107235-107249 (2021) - [j38]Qiangda Yang, Yichuan Fu
Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search. Neural Comput. Appl. 33(12): 6487-6509 (2021) - [c54]Shengkai Wang, Jie Zhang:
A Fault Diagnosis Strategy based on Qualitative Trend Analysis Integrating Andrews Plot for Industrial Processes. ICIT 2021: 973-978 - [c53]Jie Zhang, Haofeng Zhang, Bingzhang Hu:
Dual Prototype Relaxation for Generalized Zero Shot Learning. ICME 2021: 1-6 - [c52]Shengkai Wang, Jie Zhang:
Enhanced Data-Driven Fault Diagnosis of Chemical Process via Information Fusion in Multiple Neural Networks and Andrews Plot. ICAC 2021: 1-6 - 2020
- [j37]Kai Liu, Jie Zhang:
Nonlinear process modelling using echo state networks optimised by covariance matrix adaption evolutionary strategy. Comput. Chem. Eng. 135: 106730 (2020) - [j36]Jeremiah Corrigan
Integrating dynamic slow feature analysis with neural networks for enhancing soft sensor performance. Comput. Chem. Eng. 139: 106842 (2020) - [j35]Changhao Zhu
Developing Soft Sensors for Polymer Melt Index in an Industrial Polymerization Process Using Deep Belief Networks. Int. J. Autom. Comput. 17(1): 44-54 (2020) - [j34]Fanglin Mu, Yu Gu, Jie Zhang
Milk Source Identification and Milk Quality Estimation Using an Electronic Nose and Machine Learning Techniques. Sensors 20(15): 4238 (2020) - [j33]Xinxin Li
Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization. Sensors 20(22): 6533 (2020) - [c51]Kazeem Alli, Jie Zhang:
Adaptive Modelling of Fed-batch Processes with Extreme Learning Machine and Recursive Least Square Technique. ICAART (2) 2020: 668-674 - [c50]Ruosen Qi, Jie Zhang:
Data-driven fault diagnosis and prognosis for process faults using principal component analysis and extreme learning machine. INDIN 2020: 775-780 - [c49]Shengkai Wang, Jie Zhang:
Improved Process Fault Diagnosis by Using Neural Networks with Andrews Plot and Autoencoder. INDIN 2020: 787-792
2010 – 2019
- 2019
- [j32]Yiming Tian
Single Wearable Accelerometer-Based Human Activity Recognition via Kernel Discriminant Analysis and QPSO-KELM Classifier. IEEE Access 7: 109216-109227 (2019) - [j31]Qiangda Yang, Jie Zhang
Predicting molten steel endpoint temperature using a feature-weighted model optimized by mutual learning cuckoo search. Appl. Soft Comput. 83 (2019) - [j30]Yiming Tian, Jie Zhang
Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition. Sensors 19(16): 3468 (2019) - [c48]Peng Zhang, Jie Zhang, Bingzhang Hu, Yang Long:
Optimization control of a fed-batch process using an improved reinforcement learning algorithm. CCTA 2019: 314-319 - [c47]Jeremiah Corrigan
Nonlinear Data-driven Process Modelling using Slow Feature Analysis and Neural Networks. ICINCO (1) 2019: 439-446 - [c46]Shengkai Wang, Jie Zhang:
An Intelligent Process Fault Diagnosis System Integrating Andrews Plot, PCA and Neural Networks. ICAC 2019: 1-6 - [c45]Changhao Zhu, Jie Zhang:
Developing Robust Nonlinear Models through Bootstrap Aggregated Deep Belief Networks. ICAC 2019: 1-6 - [c44]Shengkai Wang, Jie Zhang:
Enhanced Process Fault Diagnosis through Integrating Neural Networks and Andrews Plot. MMAR 2019: 36-41 - [c43]Peng Zhang, Jie Zhang, Yang Long, Bingzhang Hu:
An improved reinforcement learning control strategy for batch processes. MMAR 2019: 360-365 - [c42]Ruosen Qi, Jie Zhang:
Process Fault Detection and Reconstruction by Principal Component Analysis. MMAR 2019: 594-599 - 2018
- [c41]Kai Liu, Jie Zhang:
Optimization of Echo State Networks by Covariance Matrix Adaption Evolutionary Strategy. ICAC 2018: 1-6 - [c40]Xiangfeng Shen, Zhihua Xiong, Jie Zhang:
Point-to-Point Iterative Learning Control with Piecewise Constant Inputs. ICAC 2018: 1-6 - [c39]Yiming Tian, Xitai Wang, Peng Yang, Jie Wang, Jie Zhang:
A Single Accelerometer-based Robust Human Activity Recognition via Wavelet Features and Ensemble Feature Selection. ICAC 2018: 1-6 - [c38]Changhao Zhu, Jie Zhang:
Inferential Estimation of Polymer Melt Index Using Deep Belief Networks. ICAC 2018: 1-6 - 2017
- [j29]Funmilayo N. Osuolale, Jie Zhang
Thermodynamic optimization of atmospheric distillation unit. Comput. Chem. Eng. 103: 201-209 (2017) - [j28]Sulaiman Ayobami Lawal, Jie Zhang
Actuator fault monitoring and fault tolerant control in distillation columns. Int. J. Autom. Comput. 14(1): 80-92 (2017) - [c37]Carolina Maria Cardona Baron, Jie Zhang
Re-optimisation Control of a Fed-batch Fermentation Process using Bootstrap Aggregated Extreme Learning Machine. ICINCO (1) 2017: 165-176 - [c36]Carolina Maria Cardona Baron, Jie Zhang:
Reliable On-Line Re-Optimization Control of a Fed-Batch Fermentation Process Using Bootstrap Aggregated Extreme Learning Machine. ICINCO (Selected Papers) 2017: 272-294 - 2016
- [c35]Zhihua Xiong, Yingdong Hong, Chen Chen, Jie Zhang
Convergence analysis of integrated predictive iterative learning control based on two-dimensional theory. ACC 2016: 1259-1264 - [c34]Fahad Khamis Al Kalbani, Jie Zhang
Inferential active disturbance rejection control of a heat integrated distillation column. ICARCV 2016: 1-6 - [c33]Simon William Jeffries, Shamsa Al-Abri, Jie Zhang
Inferential disturbance observer based control of a binary distillation column. IECON 2016: 190-195 - [c32]Fahad Al Kalbani, Jie Zhang
Active disturbance rejection control of a heat integrated distillation column. MMAR 2016: 278-283 - [c31]Sulaiman Ayobami Lawal, Jie Zhang
Fault monitoring and fault tolerant control in distillation columns. MMAR 2016: 865-870 - [c30]Fei Li, Jie Zhang
Modelling of a post-combustion CO2 capture process using extreme learning machine. MMAR 2016: 1252-1257 - 2015
- [j27]Jarinah Mohd Ali, Mohamed Azlan Hussain
Artificial Intelligence techniques applied as estimator in chemical process systems - A literature survey. Expert Syst. Appl. 42(14): 5915-5931 (2015) - [c29]Fahad Al Kalbani, Jie Zhang:
Inferential Active Disturbance Rejection Control of a Distillation Column using Dynamic Principal Component Regression Models. ICINCO (1) 2015: 358-364 - [c28]Sulaiman Ayobami Lawal, Jie Zhang
Actuator fault monitoring and fault tolerant control in distillation columns. ICAC 2015: 1-6 - [c27]Seyong Jung, Jie Zhang
Iterative learning control of polyhydroxybutyrate production in a sequencing batch reactor. MMAR 2015: 459-464 - 2013
- [j26]Kazim-Junior Rod Mohammed, Jie Zhang
Reliable optimisation control of a reactive polymer composite moulding process using ant colony optimisation and bootstrap aggregated neural networks. Neural Comput. Appl. 23(7-8): 1891-1898 (2013) - [c26]Damson Leonard Kaunga, Jie Zhang
Reliable modeling of chemical duarability of high level waste glass using bootstrap aggregated neural networks. ICNC 2013: 178-183 - 2012
- [j25]Shallon Stubbs, Jie Zhang
Fault detection in dynamic processes using a simplified monitoring-specific CVA state space modelling approach. Comput. Chem. Eng. 41: 77-87 (2012) - [p1]Jie Zhang
Nonlinear Process Modelling and Control Using Neurofuzzy Networks. Handbook of Natural Computing 2012: 401-434 - 2011
- [c25]Jie Zhang, Nikos G. Pantelelis:
Reliable Modelling and Optimisation Control of Reactive Polymer Composite Moulding Processes using Bootstrap Aggregated Neural Network Models. IJCCI (NCTA) 2011: 236-241 - [c24]Katy Ferguson, Jie Zhang, Carl Steele, Colin Clarke, Julian Morris:
Modelling Vitrified Glass Viscosity in a Nuclear Fuel Reprocessing Plant using Neural Networks. IJCCI (NCTA) 2011: 322-325 - 2010
- [j24]Tao Chen
On-line multivariate statistical monitoring of batch processes using Gaussian mixture model. Comput. Chem. Eng. 34(4): 500-507 (2010) - [j23]Zhihua Xiong, Yixin Xu, Jin Dong, Jie Zhang
Neural network based iterative learning control for product qualities in batch processes. Int. J. Model. Identif. Control. 11(1/2): 107-114 (2010)
2000 – 2009
- 2009
- [j22]Fernando Herrera, Jie Zhang
Optimal control of batch processes using particle swam optimisation with stacked neural network models. Comput. Chem. Eng. 33(10): 1593-1601 (2009) - [j21]Zhihua Xiong, Jin Dong, Jie Zhang
Optimal iterative learning control for end-point product qualities in semi-batch process based on neural network model. Sci. China Ser. F Inf. Sci. 52(7): 1136-1144 (2009) - [j20]Rudy Agustriyanto
Control structure selection for the ALSTOM gasifier benchmark process using GRDG analysis. Int. J. Model. Identif. Control. 6(2): 126-135 (2009) - [j19]Zainal Ahmad
Selective combination of multiple neural networks for improving model prediction in nonlinear systems modelling through forward selection and backward elimination. Neurocomputing 72(4-6): 1198-1204 (2009) - 2008
- [j18]Zhihua Xiong, Yixin Xu, Jie Zhang
Batch-to-batch control of fed-batch processes using control-affine feedforward neural network. Neural Comput. Appl. 17(4): 425-432 (2008) - [c23]Hui Geng, Zhihua Xiong, Yongmao Xu, Jie Zhang
Iterative learning control with reference batch for linear time-variant systems. ICARCV 2008: 739-744 - [c22]Jie Zhang
Batch to batch iterative learning control of a fed-batch fermentation process using linearised models. ICARCV 2008: 745-750 - 2007
- [c21]Fernando S. Antelo, Jie Zhang
Improved Bleach Plant Control Using Internal Model Control with Smith Predictor. CCA 2007: 862-867 - [c20]Rudy Agustriyanto
Obtaining the Worst Case RGA and RDGA for Uncertain Systems via Optimization. ACC 2007: 5360-5365 - 2006
- [j17]Jie Zhang
Improved on-line process fault diagnosis through information fusion in multiple neural networks. Comput. Chem. Eng. 30(3): 558-571 (2006) - [j16]Jie Zhang:
Modelling and multi-objective optimal control of batch processes using recurrent neuro-fuzzy networks. Int. J. Autom. Comput. 3(1): 1-7 (2006) - [c19]Zainal Ahmad
A Nonlinear Model Predictive Control Strategy Using Multiple Neural Network Models. ISNN (2) 2006: 943-948 - [c18]Qiang Zhou, Zhihua Xiong, Jie Zhang
Hierarchical Neural Network Based Product Quality Prediction of Industrial Ethylene Pyrolysis Process. ISNN (2) 2006: 1132-1137 - 2005
- [j15]Zhihua Xiong, Jie Zhang
Optimal Control of Fed-Batch Processes Based on Multiple Neural Networks. Appl. Intell. 22(2): 149-161 (2005) - [j14]Zainal Ahmad
Combination of multiple neural networks using data fusion techniques for enhanced nonlinear process modelling. Comput. Chem. Eng. 30(2): 295-308 (2005) - [j13]Zainal Ahmad
Bayesian selective combination of multiple neural networks for improving long-range predictions in nonlinear process modelling. Neural Comput. Appl. 14(1): 78-87 (2005) - [j12]Jie Zhang
Modeling and Optimal Control of Batch Processes Using Recurrent Neuro-Fuzzy Networks. IEEE Trans. Fuzzy Syst. 13(4): 417-427 (2005) - [c17]Zhihua Xiong, Jie Zhang
An integrated batch-to-batch iterative learning control and within batch control strategy for batch processes. ACC 2005: 1935-1940 - [c16]Yi Liu, XianHui Yang, Zhihua Xiong, Jie Zhang:
Batch-to-Batch Optimal Control Based on Support Vector Regression Model. ISNN (3) 2005: 125-130 - [c15]Zhihua Xiong, Jie Zhang, Xiong Wang, Yongmao Xu:
Neural Network Based On-Line Shrinking Horizon Re-optimization of Fed-Batch Processes. ISNN (3) 2005: 839-844 - 2004
- [j11]Zhihua Xiong, Jie Zhang
Modelling and optimal control of fed-batch processes using a novel control affine feedforward neural network. Neurocomputing 61: 317-337 (2004) - [c14]Qiang Zhou, Jie Zhang
Predicting the product yield profile and cracking degrees in an industrial ethylene pyrolysis furnace. ICARCV 2004: 2129-2133 - [c13]Zhihua Xiong, Jie Zhang, Xiong Wang, Yongmao Xu:
Run-to-Run Iterative Optimization Control of Batch Processes. ISNN (2) 2004: 97-103 - [c12]Shi Jian Zhao, Yongmao Xu, Jie Zhang:
A Novel Nonlinear Projection to Latent Structures Algorithm. ISNN (2) 2004: 773-778 - [c11]Yi Liu, XianHui Yang, Jie Zhang:
A Neural Network Modeling Method for Batch Process. ISNN (2) 2004: 874-879 - [c10]Zhihua Xiong, Jie Zhang:
Recurrent neural network model based batch-to-batch iterative optimising control. Neural Networks and Computational Intelligence 2004: 1-6 - 2003
- [c9]Zhihua Xiong, Jie Zhang:
Batch-to-batch model-based iterative optimisation control for a batch polymerisation reactor. ACC 2003: 1962-1967 - 2002
- [j10]Yuan Tian, Jie Zhang
Optimal control of a fed-batch bioreactor based upon an augmented recurrent neural network model. Neurocomputing 48(1-4): 919-936 (2002) - [c8]Xinggao Liu, Yongmao Xu, Jie Zhang
Optimal energy cost in ideal internal thermally coupled distillation columns. ACC 2002: 1502-1507 - [c7]Jianli Wei, Shunjie Fan, Yongmao Xu, Jie Zhang
Dynamic modelling of an industrial polypropylene reactor and its application in melt index prediction during grade transitions. ACC 2002: 2725-2730 - [c6]Zhihua Xiong, Jie Zhang
Modeling and optimal control of fed-batch processes using control affine feedforward neural networks. ACC 2002: 5025-5030 - 2001
- [c5]Yuan Tian, Jie Zhang
On-line re-optimisation control of a batch polymerisation reactor based on a hybrid recurrent neural network model. ACC 2001: 350-355 - [c4]Jie Zhang
Inferential feedforward control of a distillation column. ACC 2001: 2555-2560 - [c3]Jie Zhang, Julian Morris:
Nonlinear model predictive control based on multiple local linear models. ACC 2001: 3503-3508 - [c2]Ron J. Patton, C. J. Lopez-Toribio, Silvio Simani, Julian Morris, Elaine B. Martin, Jie Zhang:
Actuator fault diagnosis in a continuous stirred tank reactor using identification techniques. ECC 2001: 2729-2734 - 2000
- [j9]Jie Zhang
Long Range Predictive Control of Nonlinear Processes Based on Recurrent Neuro-Fuzzy Network Models. Neural Comput. Appl. 9(1): 50-59 (2000)
1990 – 1999
- 1999
- [j8]Jie Zhang
Developing robust non-linear models through bootstrap aggregated neural networks. Neurocomputing 25(1-3): 93-113 (1999) - [j7]Jie Zhang
Inferential estimation of polymer quality using bootstrap aggregated neural networks. Neural Networks 12(6): 927-938 (1999) - [j6]Jie Zhang
Recurrent neuro-fuzzy networks for nonlinear process modeling. IEEE Trans. Neural Networks 10(2): 313-326 (1999) - 1998
- [j5]Jie Zhang
A Sequential Learning Approach for Single Hidden Layer Neural Networks. Neural Networks 11(1): 65-80 (1998) - 1996
- [j4]Jie Zhang
Process modelling and fault diagnosis using fuzzy neural networks. Fuzzy Sets Syst. 79(1): 127-140 (1996) - 1994
- [c1]Jie Zhang, A. Julian Morris, Gary A. Montague:
Fault Diagnosis of a CSTR using fuzzy Neural Networks. AIRTC 1994: 153-158 - 1992
- [j3]Jie Zhang
Use of genetic algorithms in training diagnostic rules for process fault diagnosis. Knowl. Based Syst. 5(4): 277-288 (1992) - 1990
- [j2]Jie Zhang
Fault diagnosis of a mixing process using deep qualitative knowledge representation of physical behaviour. J. Intell. Robotic Syst. 3(2): 103-115 (1990)
1980 – 1989
- 1988
- [j1]Jie Zhang
An application of expert systems techniques to the on-line control and fault diagnosis of a mixing process. J. Intell. Robotic Syst. 1(3): 209-223 (1988)
Coauthor Index
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