BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

162 related articles for article (PubMed ID: 26685274)

  • 1. 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]  

  • 2. A Fault Detection Method Based on CPSO-Improved KICA.
    Liu M; Li X; Lou C; Jiang J
    Entropy (Basel); 2019 Jul; 21(7):. PubMed ID: 33267382
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. Batch process fault detection and identification based on discriminant global preserving kernel slow feature analysis.
    Zhang H; Tian X; Deng X; Cao Y
    ISA Trans; 2018 Aug; 79():108-126. PubMed ID: 29776590
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Nonlinear Process Fault Diagnosis Based on Serial Principal Component Analysis.
    Deng X; Tian X; Chen S; Harris CJ
    IEEE Trans Neural Netw Learn Syst; 2018 Mar; 29(3):560-572. PubMed ID: 28026785
    [TBL] [Abstract][Full Text] [Related]  

  • 6. 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]  

  • 7. 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]  

  • 8. 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]  

  • 9. Improved Statistical Fault Detection Technique and Application to Biological Phenomena Modeled by S-Systems.
    Mansouri M; Nounou MN; Nounou HN
    IEEE Trans Nanobioscience; 2017 Sep; 16(6):504-512. PubMed ID: 28708564
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Discriminant Analysis on Riemannian Manifold of Gaussian Distributions for Face Recognition With Image Sets.
    Wang W; Wang R; Huang Z; Shan S; Chen X
    IEEE Trans Image Process; 2018 Jan.; 27(1):151-163. PubMed ID: 28866497
    [TBL] [Abstract][Full Text] [Related]  

  • 11. A nonlinear quality-related fault detection approach based on modified kernel partial least squares.
    Jiao J; Zhao N; Wang G; Yin S
    ISA Trans; 2017 Jan; 66():275-283. PubMed ID: 27817839
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Integrated Diagnostic Framework for Process and Sensor Faults in Chemical Industry.
    Zhang J; Luo W; Dai Y
    Sensors (Basel); 2021 Jan; 21(3):. PubMed ID: 33530519
    [TBL] [Abstract][Full Text] [Related]  

  • 14. KPLS-KSER based approach for quality-related monitoring of nonlinear process.
    Jiao J; Zhen W; Wang G; Wang Y
    ISA Trans; 2021 Feb; 108():144-153. PubMed ID: 32981684
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A Nonlinear Support Vector Machine-Based Feature Selection Approach for Fault Detection and Diagnosis: Application to the Tennessee Eastman Process.
    Onel M; Kieslich CA; Pistikopoulos EN
    AIChE J; 2019 Mar; 65(3):992-1005. PubMed ID: 32377021
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection.
    Maliuk AS; Prosvirin AE; Ahmad Z; Kim CH; Kim JM
    Sensors (Basel); 2021 Oct; 21(19):. PubMed ID: 34640899
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Modified canonical variate analysis based on dynamic kernel decomposition for dynamic nonlinear process quality monitoring.
    Zhang MQ; Luo XL
    ISA Trans; 2021 Feb; 108():106-120. PubMed ID: 32854955
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Global-and-local-structure-based neural network for fault detection.
    Zhao H; Lai Z; Chen Y
    Neural Netw; 2019 Oct; 118():43-53. PubMed ID: 31228723
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An Improved Mixture of Probabilistic PCA for Nonlinear Data-Driven Process Monitoring.
    Zhang J; Chen H; Chen S; Hong X
    IEEE Trans Cybern; 2019 Jan; 49(1):198-210. PubMed ID: 29990211
    [TBL] [Abstract][Full Text] [Related]  

  • 20. 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]  

    [Next]    [New Search]
    of 9.