88 related articles for article (PubMed ID: 28193441)
1. Double-layer ensemble monitoring of non-gaussian processes using modified independent component analysis.
Tong C; Lan T; Shi X
ISA Trans; 2017 May; 68():181-188. PubMed ID: 28193441
[TBL] [Abstract][Full Text] [Related]
2. Statistical monitoring for non-Gaussian processes based on MICA-KDR method.
Lan T; Tong C; Yu H; Shi X
ISA Trans; 2019 Nov; 94():164-173. PubMed ID: 31078289
[TBL] [Abstract][Full Text] [Related]
3. Fault Detection of Non-Gaussian and Nonlinear Processes Based on Independent Slow Feature Analysis.
Li C; Zhou Z; Wen C; Li Z
ACS Omega; 2022 Mar; 7(8):6978-6990. PubMed ID: 35252689
[TBL] [Abstract][Full Text] [Related]
4. Application of multiway ICA for on-line process monitoring of a sequencing batch reactor.
Yoo CK; Lee DS; Vanrolleghem PA
Water Res; 2004 Apr; 38(7):1715-32. PubMed ID: 15026226
[TBL] [Abstract][Full Text] [Related]
5. Monitoring Nonlinear and Non-Gaussian Processes Using Gaussian Mixture Model-Based Weighted Kernel Independent Component Analysis.
Cai L; Tian X; Chen S
IEEE Trans Neural Netw Learn Syst; 2017 Jan; 28(1):122-135. PubMed ID: 26685274
[TBL] [Abstract][Full Text] [Related]
6. Nonlinear Dynamic Process Monitoring Based on Ensemble Kernel Canonical Variate Analysis and Bayesian Inference.
Wang X; Wu P
ACS Omega; 2022 Jun; 7(22):18904-18921. PubMed ID: 35694473
[TBL] [Abstract][Full Text] [Related]
7. A Unifying Objective Function of Independent Component Analysis for Ordering Sources by Non-Gaussianity.
Matsuda Y; Yamaguchi K
IEEE Trans Neural Netw Learn Syst; 2018 Nov; 29(11):5630-5642. PubMed ID: 29993873
[TBL] [Abstract][Full Text] [Related]
8. Automatic Subspace Learning via Principal Coefficients Embedding.
Peng X; Lu J; Yi Z; Yan R
IEEE Trans Cybern; 2017 Nov; 47(11):3583-3596. PubMed ID: 27305691
[TBL] [Abstract][Full Text] [Related]
9. Process monitoring using kernel density estimation and Bayesian networking with an industrial case study.
Gonzalez R; Huang B; Lau E
ISA Trans; 2015 Sep; 58():330-47. PubMed ID: 25930233
[TBL] [Abstract][Full Text] [Related]
10. Microarray data classification based on ensemble independent component selection.
Liu KH; Li B; Wu QQ; Zhang J; Du JX; Liu GY
Comput Biol Med; 2009 Nov; 39(11):953-60. PubMed ID: 19716554
[TBL] [Abstract][Full Text] [Related]
11. Fault Detection and Isolation of Non-Gaussian and Nonlinear Processes Based on Statistics Pattern Analysis and the
Zhou Z; Wang J; Yang C; Wen C; Li Z
ACS Omega; 2022 Jun; 7(22):18623-18637. PubMed ID: 35694521
[TBL] [Abstract][Full Text] [Related]
12. Statistical process monitoring based on orthogonal multi-manifold projections and a novel variable contribution analysis.
Tong C; Shi X; Lan T
ISA Trans; 2016 Nov; 65():407-417. PubMed ID: 27435000
[TBL] [Abstract][Full Text] [Related]
13. Fault detection and diagnosis for non-Gaussian stochastic distribution systems with time delays via RBF neural networks.
Yi Q; Zhan-ming L; Er-chao L
ISA Trans; 2012 Nov; 51(6):786-91. PubMed ID: 22902083
[TBL] [Abstract][Full Text] [Related]
14. Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals.
Barbati G; Porcaro C; Zappasodi F; Rossini PM; Tecchio F
Clin Neurophysiol; 2004 May; 115(5):1220-32. PubMed ID: 15066548
[TBL] [Abstract][Full Text] [Related]
15. Best harmony, unified RPCL and automated model selection for unsupervised and supervised learning on Gaussian mixtures, three-layer nets and ME-RBF-SVM models.
Xu L
Int J Neural Syst; 2001 Feb; 11(1):43-69. PubMed ID: 11310554
[TBL] [Abstract][Full Text] [Related]
16. Independent component ensemble of EEG for brain-computer interface.
Chuang CH; Ko LW; Lin YP; Jung TP; Lin CT
IEEE Trans Neural Syst Rehabil Eng; 2014 Mar; 22(2):230-8. PubMed ID: 24608683
[TBL] [Abstract][Full Text] [Related]
17. New monitoring technique with an ICA algorithm in the wastewater treatment process.
Lee JM; Yoo CK; Lee IB
Water Sci Technol; 2003; 47(12):49-56. PubMed ID: 12926669
[TBL] [Abstract][Full Text] [Related]
18. GP-Select: Accelerating EM Using Adaptive Subspace Preselection.
Shelton JA; Gasthaus J; Dai Z; Lücke J; Gretton A
Neural Comput; 2017 Aug; 29(8):2177-2202. PubMed ID: 28562214
[TBL] [Abstract][Full Text] [Related]
19. Modified kernel principal component analysis using double-weighted local outlier factor and its application to nonlinear process monitoring.
Deng X; Wang L
ISA Trans; 2018 Jan; 72():218-228. PubMed ID: 29017769
[TBL] [Abstract][Full Text] [Related]
20. Fault diagnosis of a benchmark fermentation process: a comparative study of feature extraction and classification techniques.
Monroy I; Villez K; Graells M; Venkatasubramanian V
Bioprocess Biosyst Eng; 2012 Jun; 35(5):689-704. PubMed ID: 22076402
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
