World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
78
Citations
27472
World Ranking
1189
National Ranking
165

Overview

S. Joe Qin is affiliated with Lingnan University in China and specializes in the field of Engineering, with a particular focus on Control and Systems Engineering as evidenced by the majority of their research output. Their work spans several subfields including Artificial Intelligence, Analytical Chemistry, Materials Chemistry, and Mechanical Engineering.

The scientist's recent research includes publications in a range of topics from system control to materials science. Key recent papers include:

  • "Constructing and optimizing core@shell structure CNTs@MoS2 nanocomposites as outstanding microwave absorbers" (2020, Applied Surface Science)
  • "Optimization, selective and efficient production of CNTs/CoxFe3−xO4 core/shell nanocomposites as outstanding microwave absorbers" (2020, Journal of Materials Chemistry C)
  • "Bridging systems theory and data science: A unifying review of dynamic latent variable analytics and process monitoring" (2020, Annual Reviews in Control)
  • "Efficient Dynamic Latent Variable Analysis for High-Dimensional Time Series Data" (2020, IEEE Transactions on Industrial Informatics)
  • "Dynamic latent variable regression for inferential sensor modeling and monitoring" (2020, Computers & Chemical Engineering)

The research of S. Joe Qin addresses several main topics, such as:

  • Fault Detection and Control Systems
  • Spectroscopy and Chemometric Analyses
  • Control Systems and Identification
  • Advanced Control Systems Optimization
  • Neural Networks and Applications
  • Mineral Processing and Grinding
  • Topic Modeling

Frequent coauthors contributing to their work include Yining Dong, Haoran Xie, Fu Lee Wang, Xiaohui Tao, and Lishuai Li.

Several publication venues have been typical outlets for their work, indicating a diverse interdisciplinary approach. These venues include:

  • arXiv (Cornell University)
  • IFAC-PapersOnLine
  • SSRN Electronic Journal
  • IEEE Transactions on Industrial Informatics
  • Computers & Chemical Engineering

The collective output demonstrates a blend of theoretical and applied research, particularly emphasizing engineering systems, data analytics for process monitoring, and materials chemistry. Their work contributes frequently to advancing knowledge in control systems, dynamic modeling, and nanocomposite materials research.

Best Publications

  • A survey of industrial model predictive control technology

    S.Joe Qin;Thomas A. Badgwell

  • Statistical process monitoring: basics and beyond

    S. Joe Qin

  • Survey on data-driven industrial process monitoring and diagnosis

    S. Joe Qin

  • Recursive PCA for adaptive process monitoring

    Weihua Li;H.Henry Yue;Sergio Valle-Cervantes;S.Joe Qin

  • Recursive PLS algorithms for adaptive data modeling

    S. Joe Qin

  • An overview of subspace identification

    S. Joe Qin

  • Identification of faulty sensors using principal component analysis

    Ricardo Dunia;S. Joe Qin;Thomas F. Edgar;Thomas J. McAvoy

  • Selection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods†

    Sergio Valle;and Weihua Li;S. Joe Qin

  • Nonlinear PLS Modeling Using Neural Networks

    S.J. Qin;T.J. McAvoy

  • An Overview of Nonlinear Model Predictive Control Applications

    S. Joe Qin;Thomas A. Badgwell

  • Reconstruction-based contribution for process monitoring

    Carlos F. Alcala;S. Joe Qin

  • Reconstruction-Based Fault Identification Using a Combined Index

    H. Henry Yue;S. Joe Qin

  • Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models

    Jie Yu;S. Joe Qin

  • Subspace approach to multidimensional fault identification and reconstruction

    Ricardo Dunia;S. Joe Qin

  • Total projection to latent structures for process monitoring

    Donghua Zhou;Gang Li;S. Joe Qin

  • Control performance monitoring — a review and assessment

    S. Joe Qin

  • Fault detection and diagnosis based on modified independent component analysis

    Jong-Min Lee;S. Joe Qin;In-Beum Lee

  • A novel dynamic PCA algorithm for dynamic data modeling and process monitoring

    Yining Dong;Yining Dong;S. Joe Qin;S. Joe Qin

  • Process data analytics in the era of big data

    S. Joe Qin

  • Brief paper: Geometric properties of partial least squares for process monitoring

    Gang Li;S. Joe Qin;Donghua Zhou

  • On unifying multiblock analysis with application to decentralized process monitoring

    S. Joe Qin;Sergio Valle;Michael J. Piovoso

  • Multivariate process monitoring and fault diagnosis by multi-scale PCA

    Manish Misra;H.Henry Yue;S.Joe Qin;Cheng Ling

  • Recursive PCA for Adaptive Process Monitoring

    S. Joe Qin;Weihua Li;H. Henry Yue

Frequent Co-Authors

Tianyou Chai
Tianyou Chai Northeastern University
Donghua Zhou
Donghua Zhou Shandong University of Science and Technology
Lennart Ljung
Lennart Ljung Linköping University
Terrence L. Blevins
Terrence L. Blevins Emerson (Sweden)
Ruilong Deng
Ruilong Deng Zhejiang University
In-Beum Lee
In-Beum Lee Pohang University of Science and Technology
Thomas J. McAvoy
Thomas J. McAvoy University of Maryland, College Park
Muhammad Sahimi
Muhammad Sahimi University of Southern California
Thomas F. Edgar
Thomas F. Edgar The University of Texas at Austin
Bjarne A. Foss
Bjarne A. Foss Norwegian University of Science and Technology

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