His main research concerns Principal component analysis, Algorithm, Fault detection and isolation, Artificial intelligence and Pattern recognition. His Principal component analysis study combines topics in areas such as Reconstruction error and Missing data. His research in Algorithm intersects with topics in Chemometrics, Partial least squares regression, Monte Carlo method and Signal processing.
His work deals with themes such as Subspace topology, Residual, Identifiability and Data mining, which intersect with Fault detection and isolation. His Artificial intelligence research incorporates elements of Process control, Statistical process control and Identification. His study in Pattern recognition is interdisciplinary in nature, drawing from both Errors-in-variables models and Electronic engineering.
S. Joe Qin spends much of his time researching Control theory, Algorithm, Principal component analysis, Fault detection and isolation and Artificial intelligence. His research integrates issues of Control engineering, Subspace topology and Model predictive control in his study of Control theory. His study in the fields of Data compression under the domain of Algorithm overlaps with other disciplines such as Dynamic data.
S. Joe Qin interconnects Multivariate statistics and Cluster analysis in the investigation of issues within Principal component analysis. The Fault detection and isolation study combines topics in areas such as Data mining, Statistical process control, Identifiability, Residual and Process. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition.
His primary areas of study are Data mining, Latent variable, Canonical correlation, Algorithm and Principal component analysis. The various areas that he examines in his Data mining study include Latent variable model, Covariance, Reliability engineering and Fault detection and isolation. His Latent variable study is concerned with the larger field of Artificial intelligence.
He combines subjects such as Control engineering and Nonlinear system with his study of Algorithm. His Control engineering research is multidisciplinary, incorporating elements of Abstract process and Model predictive control. His biological study deals with issues like Singular value decomposition, which deal with fields such as Observability and Orthographic projection.
S. Joe Qin focuses on Data mining, Artificial intelligence, Machine learning, Algorithm and Principal component analysis. The study incorporates disciplines such as Latent variable, Root cause and Fault detection and isolation in addition to Data mining. His Fault detection and isolation research is multidisciplinary, relying on both Data-driven, Dynamic time warping and Reliability engineering.
His Artificial intelligence research includes themes of Big data, Process and Pattern recognition. S. Joe Qin combines topics linked to Process modeling with his work on Algorithm. In his study, Covariance, Collinearity and Regularization is strongly linked to Canonical correlation, which falls under the umbrella field of Principal component analysis.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
A survey of industrial model predictive control technology
S.Joe Qin;Thomas A. Badgwell.
Control Engineering Practice (2003)
Statistical process monitoring: basics and beyond
S. Joe Qin.
Journal of Chemometrics (2003)
Survey on data-driven industrial process monitoring and diagnosis
S. Joe Qin.
Annual Reviews in Control (2012)
Recursive PCA for adaptive process monitoring
Weihua Li;H.Henry Yue;Sergio Valle-Cervantes;S.Joe Qin.
Journal of Process Control (2000)
Recursive PLS algorithms for adaptive data modeling
S. Joe Qin.
Computers & Chemical Engineering (1998)
Identification of faulty sensors using principal component analysis
Ricardo Dunia;S. Joe Qin;Thomas F. Edgar;Thomas J. McAvoy.
Aiche Journal (1996)
An overview of subspace identification
S. Joe Qin.
Computers & Chemical Engineering (2006)
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.
Industrial & Engineering Chemistry Research (1999)
An Overview of Nonlinear Model Predictive Control Applications
S. Joe Qin;Thomas A. Badgwell.
Reconstruction-Based Fault Identification Using a Combined Index
H. Henry Yue;S. Joe Qin.
Industrial & Engineering Chemistry Research (2001)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: