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Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models

Chemical engineering research & design, 2015-03, Vol.95, p.113-132

2015

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Virtual Sensing of Key Variables in the Hydrogen Production Process: A Comparative Study of Data-Driven Models

Sensors (Basel, Switzerland), 2024-05, Vol.24 (10), p.3143

2024

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Student's- t Mixture Regression-Based Robust Soft Sensor Development for Multimode Industrial Processes

Sensors (Basel, Switzerland), 2018-11, Vol.18 (11), p.3968

2018

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Adaptive Soft Sensor Development for Multi-Output Industrial Processes Based on Selective Ensemble Learning

IEEE access, 2018, Vol.6, p.55628-55642

2018

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An Efficient Modeling and Optimization Approach for Pressure Testing During Reentry Operation of Deepwater Drilling Riser System

IEEE access, 2020, Vol.8, p.74759-74770

2020

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Physics-informed Student’s t mixture regression model applied to predict mixed oil length

Journal of Pipeline Science and Engineering, 2023-03, Vol.3 (1), p.100105, Article 100105

2023

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Semi-supervised selective ensemble learning based on distance to model for nonlinear soft sensor development

Neurocomputing (Amsterdam), 2017-01, Vol.222, p.91-104

2017

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Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor

Chinese journal of chemical engineering, 2015-12, Vol.23 (12), p.1925-1934

2015

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Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

Chinese journal of chemical engineering, 2014-07, Vol.22 (7), p.828-836

2014

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Robust Supervised Probabilistic Factor Analysis and Its Application to Industrial Soft Sensor Modeling

IEEE access, 2019, Vol.7, p.184038-184052

2019

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Hierarchical Quality Monitoring for Large-Scale Industrial Plants With Big Process Data

IEEE transaction on neural networks and learning systems, 2021-08, Vol.32 (8), p.3330-3341

2021

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Soft-Sensor Development for Processes With Multiple Operating Modes Based on Semisupervised Gaussian Mixture Regression

IEEE transactions on control systems technology, 2019-09, Vol.27 (5), p.2169-2181

2019

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Quality variable prediction for chemical processes based on semisupervised Dirichlet process mixture of Gaussians

Chemical engineering science, 2019-01, Vol.193, p.394-410

2019

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Bayesian Just-in-Time Learning and Its Application to Industrial Soft Sensing

IEEE transactions on industrial informatics, 2020-04, Vol.16 (4), p.2787-2798

2020

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Semisupervised Bayesian Gaussian Mixture Models for Non-Gaussian Soft Sensor

IEEE transactions on cybernetics, 2021-07, Vol.51 (7), p.3455-3468

2021

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Parallel Computing and SGD-Based DPMM For Soft Sensor Development With Large-Scale Semisupervised Data

IEEE transactions on industrial electronics (1982), 2019-08, Vol.66 (8), p.6362-6373

2019

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Semisupervised Robust Modeling of Multimode Industrial Processes for Quality Variable Prediction Based on Student's t Mixture Model

IEEE transactions on industrial informatics, 2020-05, Vol.16 (5), p.2965-2976

2020

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Semi-supervised mixture of latent factor analysis models with application to online key variable estimation

Control engineering practice, 2019-03, Vol.84, p.32-47

2019

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A mixture of shallow neural networks for virtual sensing: Could perform better than deep neural networks

Expert systems with applications, 2024-12, Vol.256, p.124870, Article 124870

2024

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Nonlinear industrial soft sensor development based on semi-supervised probabilistic mixture of extreme learning machines

Control engineering practice, 2019-10, Vol.91, p.104098, Article 104098

2019

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Autoencoder-based nonlinear Bayesian locally weighted regression for soft sensor development

ISA transactions, 2020-08, Vol.103, p.143-155

2020

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A unified just-in-time learning paradigm and its application to adaptive soft sensing for nonlinear and time-varying chemical process

Chemical engineering science, 2022-08, Vol.258, p.117753, Article 117753

2022

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Enhancing the reliability and accuracy of data-driven dynamic soft sensor based on selective dynamic partial least squares models

Control engineering practice, 2022-10, Vol.127, p.105292, Article 105292

2022

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Semi-supervised variational Bayesian Student’s t mixture regression and robust inferential sensor application

Control engineering practice, 2019-11, Vol.92, p.104155, Article 104155

2019

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Robust inferential sensor development based on variational Bayesian Student’s-t mixture regression

Neurocomputing (Amsterdam), 2019-12, Vol.369, p.11-28

2019

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Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models

Virtual Sensing of Key Variables in the Hydrogen Production Process: A Comparative Study of Data-Driven Models

Student's- t Mixture Regression-Based Robust Soft Sensor Development for Multimode Industrial Processes

Adaptive Soft Sensor Development for Multi-Output Industrial Processes Based on Selective Ensemble Learning

An Efficient Modeling and Optimization Approach for Pressure Testing During Reentry Operation of Deepwater Drilling Riser System

Physics-informed Student’s t mixture regression model applied to predict mixed oil length

Semi-supervised selective ensemble learning based on distance to model for nonlinear soft sensor development

Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor

Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

Robust Supervised Probabilistic Factor Analysis and Its Application to Industrial Soft Sensor Modeling

Hierarchical Quality Monitoring for Large-Scale Industrial Plants With Big Process Data

Soft-Sensor Development for Processes With Multiple Operating Modes Based on Semisupervised Gaussian Mixture Regression

Quality variable prediction for chemical processes based on semisupervised Dirichlet process mixture of Gaussians

Bayesian Just-in-Time Learning and Its Application to Industrial Soft Sensing

Semisupervised Bayesian Gaussian Mixture Models for Non-Gaussian Soft Sensor

Parallel Computing and SGD-Based DPMM For Soft Sensor Development With Large-Scale Semisupervised Data

Semisupervised Robust Modeling of Multimode Industrial Processes for Quality Variable Prediction Based on Student's t Mixture Model

Semi-supervised mixture of latent factor analysis models with application to online key variable estimation

A mixture of shallow neural networks for virtual sensing: Could perform better than deep neural networks

Nonlinear industrial soft sensor development based on semi-supervised probabilistic mixture of extreme learning machines

Autoencoder-based nonlinear Bayesian locally weighted regression for soft sensor development

A unified just-in-time learning paradigm and its application to adaptive soft sensing for nonlinear and time-varying chemical process

Enhancing the reliability and accuracy of data-driven dynamic soft sensor based on selective dynamic partial least squares models

Semi-supervised variational Bayesian Student’s t mixture regression and robust inferential sensor application

Robust inferential sensor development based on variational Bayesian Student’s-t mixture regression

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