| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A geometric method for batch data visualization, process monitoring and fault detection
|
Wang, Ray
|
|
|
67 |
C |
p. 197-205
|
artikel
|
| 2 |
An improved variable selection method for support vector regression in NIR spectral modeling
|
Xu, Shu
|
|
|
67 |
C |
p. 83-93
|
artikel
|
| 3 |
A novel dynamic PCA algorithm for dynamic data modeling and process monitoring
|
Dong, Yining
|
|
|
67 |
C |
p. 1-11
|
artikel
|
| 4 |
A scalable design of experiments framework for optimal sensor placement
|
Yu, Jing
|
|
|
67 |
C |
p. 44-55
|
artikel
|
| 5 |
Big Data Approximating Control (BDAC)—A new model-free estimation and control paradigm based on pattern matching and approximation
|
Stanley, G.M.
|
|
|
67 |
C |
p. 141-159
|
artikel
|
| 6 |
Comparative study on monitoring schemes for non-Gaussian distributed processes
|
Li, Gang
|
|
|
67 |
C |
p. 69-82
|
artikel
|
| 7 |
Data-driven adaptive multiple model system utilizing growing self-organizing maps
|
Lu, Bo
|
|
|
67 |
C |
p. 56-68
|
artikel
|
| 8 |
Data mining and clustering in chemical process databases for monitoring and knowledge discovery
|
Thomas, Michael C.
|
|
|
67 |
C |
p. 160-175
|
artikel
|
| 9 |
Dynamic concurrent kernel CCA for strip-thickness relevant fault diagnosis of continuous annealing processes
|
Liu, Qiang
|
|
|
67 |
C |
p. 12-22
|
artikel
|
| 10 |
Editorial Board
|
|
|
|
67 |
C |
p. ii
|
artikel
|
| 11 |
Multilevel MVU models with localized construction for monitoring processes with large scale data
|
Wei, Chihang
|
|
|
67 |
C |
p. 176-196
|
artikel
|
| 12 |
Novel sparse LSSVR models in primal weight space for robust system identification with outliers
|
A. Santos, José Daniel
|
|
|
67 |
C |
p. 129-140
|
artikel
|
| 13 |
Real-time fault detection and diagnosis using sparse principal component analysis
|
Gajjar, Shriram
|
|
|
67 |
C |
p. 112-128
|
artikel
|
| 14 |
Robust probabilistic principal component analysis based process modeling: Dealing with simultaneous contamination of both input and output data
|
Sadeghian, A.
|
|
|
67 |
C |
p. 94-111
|
artikel
|
| 15 |
Semi-supervised online soft sensor maintenance experiences in the chemical industry
|
Lu, Bo
|
|
|
67 |
C |
p. 23-34
|
artikel
|
| 16 |
Special issue on big data: Data science for process control and operations
|
Joe Qin, S.
|
|
|
67 |
C |
p. iii
|
artikel
|
| 17 |
Statistical process monitoring as a big data analytics tool for smart manufacturing
|
He, Q. Peter
|
|
|
67 |
C |
p. 35-43
|
artikel
|
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