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Ensemble just-in-time learning framework through evolutionary multi-objective optimization for soft sensor development of nonlinear industrial processes

Chemometrics and intelligent laboratory systems, 2019-01, Vol.184, p.153-166

2019

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Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes

Computers & chemical engineering, 2014-12, Vol.71, p.77-93

2014

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Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data

Energies (Basel), 2024-09, Vol.17 (18), p.4739

2024

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Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry

Sensors (Basel, Switzerland), 2021-12, Vol.21 (24), p.8471

2021

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Soft sensor development for online quality prediction of industrial batch rubber mixing process using ensemble just-in-time Gaussian process regression models

Chemometrics and intelligent laboratory systems, 2016-07, Vol.155, p.170-182

2016

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Online-Dynamic-Clustering-Based Soft Sensor for Industrial Semi-Supervised Data Streams

Sensors (Basel, Switzerland), 2023-01, Vol.23 (3), p.1520

2023

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Soft Sensor Development Based on the Hierarchical Ensemble of Gaussian Process Regression Models for Nonlinear and Non-Gaussian Chemical Processes

Industrial & engineering chemistry research, 2016-07, Vol.55 (28), p.7704-7719

2016

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Deep Semi-Supervised Just-in-Time Learning Based Soft Sensor for Mooney Viscosity Estimation in Industrial Rubber Mixing Process

Polymers, 2022-03, Vol.14 (5), p.1018

2022

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Differential Evolution Algorithm Combined with Uncertainty Handling Techniques for Stochastic Reentrant Job Shop Scheduling Problem

Complexity (New York, N.Y.), 2022, Vol.2022 (1)

2022

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Online local learning based adaptive soft sensor and its application to an industrial fed-batch chlortetracycline fermentation process

Chemometrics and intelligent laboratory systems, 2015-04, Vol.143, p.58-78

2015

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Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

Advances in polymer technology, 2020, Vol.2020 (2020), p.1-14

2020

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基于病理图像集成深度学习的胃癌预后预测方法

电子与信息学报, 2023-07, Vol.45 (7), p.2623-2633

2023

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Online prediction for contamination of chlortetracycline fermentation based on Dezert-Smarandache theory

Chinese journal of chemical engineering, 2015-06, Vol.23 (6), p.1009-1016

2015

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A generative image fusion approach based on supervised deep convolution network driven by weighted gradient flow

Image and vision computing, 2019-06, Vol.86, p.1-16

2019

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Wind power forecasting based on ensemble deep learning with surrogate-assisted evolutionary neural architecture search and many-objective federated learning

Energy (Oxford), 2024-11, Vol.308, p.133023, Article 133023

2024

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Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models

Renewable energy, 2021-08, Vol.174, p.1-18

2021

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Adaptive Soft Sensor Development Based on Online Ensemble Gaussian Process Regression for Nonlinear Time-Varying Batch Processes

Industrial & engineering chemistry research, 2015-08, Vol.54 (30), p.7320-7345

2015

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Evolutionary optimization based pseudo labeling for semi-supervised soft sensor development of industrial processes

Chemical engineering science, 2021-06, Vol.237, p.116560, Article 116560

2021

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Soft Sensor Development for Nonlinear Industrial Processes Based on Ensemble Just-in-Time Extreme Learning Machine through Triple-Modal Perturbation and Evolutionary Multiobjective Optimization

Industrial & engineering chemistry research, 2019-09, Vol.58 (38), p.17991-18006

2019

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Adaptive forecasting of wind power based on selective ensemble of offline global and online local learning

Energy conversion and management, 2022-11, Vol.271, p.116296, Article 116296

2022

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Soft sensor modeling for small data scenarios based on data enhancement and selective ensemble

Chemical engineering science, 2023-09, Vol.279, p.118958, Article 118958

2023

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Semi-supervised soft sensor development based on dynamic dimensionality reduction-assisted large-scale pseudo label optimization and sample-weighted quality-relevant deep learning

Chemical engineering science, 2024-10, Vol.298, p.120387, Article 120387

2024

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Semi-supervised ensemble support vector regression based soft sensor for key quality variable estimation of nonlinear industrial processes with limited labeled data

Chemical engineering research & design, 2022-03, Vol.179, p.510-526

2022

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Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes

Chemical engineering science, 2015-07, Vol.131, p.282-303

2015

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Dual learning-based online ensemble regression approach for adaptive soft sensor modeling of nonlinear time-varying processes

Chemometrics and intelligent laboratory systems, 2016-02, Vol.151, p.228-244

2016

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Ensemble just-in-time learning framework through evolutionary multi-objective optimization for soft sensor development of nonlinear industrial processes

Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes

Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data

Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry

Soft sensor development for online quality prediction of industrial batch rubber mixing process using ensemble just-in-time Gaussian process regression models

Online-Dynamic-Clustering-Based Soft Sensor for Industrial Semi-Supervised Data Streams

Soft Sensor Development Based on the Hierarchical Ensemble of Gaussian Process Regression Models for Nonlinear and Non-Gaussian Chemical Processes

Deep Semi-Supervised Just-in-Time Learning Based Soft Sensor for Mooney Viscosity Estimation in Industrial Rubber Mixing Process

Differential Evolution Algorithm Combined with Uncertainty Handling Techniques for Stochastic Reentrant Job Shop Scheduling Problem

Online local learning based adaptive soft sensor and its application to an industrial fed-batch chlortetracycline fermentation process

Ensemble Just-In-Time Learning-Based Soft Sensor for Mooney Viscosity Prediction in an Industrial Rubber Mixing Process

基于病理图像集成深度学习的胃癌预后预测方法

Online prediction for contamination of chlortetracycline fermentation based on Dezert-Smarandache theory

A generative image fusion approach based on supervised deep convolution network driven by weighted gradient flow

Wind power forecasting based on ensemble deep learning with surrogate-assisted evolutionary neural architecture search and many-objective federated learning

Probabilistic wind power forecasting using selective ensemble of finite mixture Gaussian process regression models

Adaptive Soft Sensor Development Based on Online Ensemble Gaussian Process Regression for Nonlinear Time-Varying Batch Processes

Evolutionary optimization based pseudo labeling for semi-supervised soft sensor development of industrial processes

Soft Sensor Development for Nonlinear Industrial Processes Based on Ensemble Just-in-Time Extreme Learning Machine through Triple-Modal Perturbation and Evolutionary Multiobjective Optimization

Adaptive forecasting of wind power based on selective ensemble of offline global and online local learning

Soft sensor modeling for small data scenarios based on data enhancement and selective ensemble

Semi-supervised soft sensor development based on dynamic dimensionality reduction-assisted large-scale pseudo label optimization and sample-weighted quality-relevant deep learning

Semi-supervised ensemble support vector regression based soft sensor for key quality variable estimation of nonlinear industrial processes with limited labeled data

Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes

Dual learning-based online ensemble regression approach for adaptive soft sensor modeling of nonlinear time-varying processes

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