Qingchao Jiang

According to our database1, Qingchao Jiang authored at least 15 papers between 2016 and 2020.

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Bibliography

2020
Deep relevant representation learning for soft sensing.
Inf. Sci., 2020

2019
Learning Deep Correlated Representations for Nonlinear Process Monitoring.
IEEE Trans. Industrial Informatics, 2019

Multiobjective Two-Dimensional CCA-Based Monitoring for Successive Batch Processes With Industrial Injection Molding Application.
IEEE Trans. Industrial Electronics, 2019

Neighborhood Variational Bayesian Multivariate Analysis for Distributed Process Monitoring With Missing Data.
IEEE Trans. Contr. Sys. Techn., 2019

Multivariate Statistical Monitoring of Key Operation Units of Batch Processes Based on Time-Slice CCA.
IEEE Trans. Contr. Sys. Techn., 2019

Multimode Process Monitoring Using Variational Bayesian Inference and Canonical Correlation Analysis.
IEEE Trans. Automation Science and Engineering, 2019

Quality-Driven Kernel Projection to Latent Structure Model for Nonlinear Process Monitoring.
IEEE Access, 2019

2018
Joint-Individual Monitoring of Parallel-Running Batch Processes Based on MCCA.
IEEE Access, 2018

Optimal Variable Transmission for Distributed Local Fault Detection Incorporating RA and Evolutionary Optimization.
IEEE Access, 2018

2017
Data-Driven Distributed Local Fault Detection for Large-Scale Processes Based on the GA-Regularized Canonical Correlation Analysis.
IEEE Trans. Industrial Electronics, 2017

Data-Driven Optimized Distributed Dynamic PCA for Efficient Monitoring of Large-Scale Dynamic Processes.
IEEE Access, 2017

2016
Performance-Driven Distributed PCA Process Monitoring Based on Fault-Relevant Variable Selection and Bayesian Inference.
IEEE Trans. Industrial Electronics, 2016

Bayesian Fault Diagnosis With Asynchronous Measurements and Its Application in Networked Distributed Monitoring.
IEEE Trans. Industrial Electronics, 2016

GMM and optimal principal components-based Bayesian method for multimode fault diagnosis.
Computers & Chemical Engineering, 2016

Independent component analysis model utilizing de-mixing information for improved non-Gaussian process monitoring.
Computers & Industrial Engineering, 2016


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