EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 355 (2026) EPJ Web Conf., 355 (2026) 01009 References
Open Access
Issue
EPJ Web Conf.
Volume 355, 2026
4th International Conference on Sustainable Technologies and Advances in Automation, Aerospace and Robotics (STAAAR 2025)
Article Number 01009
Number of page(s) 10
Section Robotics, Exoskeletons and AI Modeling
DOI https://doi.org/10.1051/epjconf/202635501009
Published online 03 March 2026
  1. Z. Jalilibal, M.H.A. Karavigh, A. Amiri, M.B.C. Khoo, Run rules schemes for statistical process monitoring: A literature review, Qual. Technol. Quant. Manag. 20, 21–52 (2023) [Google Scholar]
  2. A. Melo, M.M. Câmara, J.C. Pinto, Data-driven process monitoring and fault diagnosis: A comprehensive survey, Processes 12, 251 (2024) [Google Scholar]
  3. R. Asadi, A. Queguineur, O. Wiikinkoski, H. Mokhtarian, T. Aihkisalo, A. Revuelta, I.F. Ituarte, Process monitoring by deep neural networks in directed energy deposition, Rob. Comput. Integr. Manuf. 87, 102710 (2024) [Google Scholar]
  4. D. Zago, Statistical process monitoring: A general framework for statistical process monitoring in Julia, J. Stat. Softw. 113, 1–45 (2025) [Google Scholar]
  5. R.L. Mason, J.C. Young, Multivariate statistical process control, in: International Encyclopedia of Statistical Science (Springer, Berlin, 2025), pp. 1645–1647. [Google Scholar]
  6. T.J. Fontalvo-Herrera, R. Herrera, J. Zambrano, Three-phase method to assess the logistics service using Six Sigma metrics and Hotelling’s T² chart, Int. J. Product. Qual. Manag. 35, 17–39 (2022) [Google Scholar]
  7. E. Sari, R. Widyasari, Application of Hotelling T² control diagram for drinking water quality, Mathline J. Mat. 9, 273–284 (2024) [Google Scholar]
  8. K. Rashid, A comparison between the MEWMA and Mahalanobis distance control chart, Kurdistan J. Appl. Res. 6, 94–104 (2021) [Google Scholar]
  9. D.H. Kadir, R.O. Yahya, A.M. Khudhur, T.H. Ali, Data de-noise for multivariate T² and S-charts using multivariate wavelets, Zanco J. Human Sci. 28, 343–360 (2024) [Google Scholar]
  10. S. Rakuff, R. Mikail, F. Sutton, M. Rutkevicius, Multiple principal component statistics for bearing condition monitoring, in: Proc. IEEE SDEMPED (2025), pp. 1–6. [Google Scholar]
  11. M. Greenacre, P.J.F. Groenen, T. Hastie, A. Iodice d’Enza, A. Markos, E. Tuzhilina, Principal component analysis, Nat. Rev. Methods Primers 2, 100 (2022) [CrossRef] [Google Scholar]
  12. B.M.S. Hasan, A.M. Abdulazeez, A review of principal component analysis algorithm, J. Soft Comput. Data Min. 2, 20–30 (2021). [Google Scholar]
  13. X. Chang et al., Prediction of coal body structure using PCA and logging curves, Nat. Resour. Res. 34, 1023–1044 (2025) [Google Scholar]
  14. I.A. Sadiq, Performance of partial least squares regression under multicollinearity, in: RSS Nigeria Annual Conf. Proc. (2025), pp. 318–328. [Google Scholar]
  15. N. Er-rahmany et al., QSAR model using PLS/RF for corrosion inhibitors, Next Res. 2, 100148 (2025) [Google Scholar]
  16. Y. Han et al., Fault monitoring using adaptive kernel PCA integrating grey relational analysis, Process Saf. Environ. Prot. 157, 397–410 (2022) [Google Scholar]
  17. B.K. Gwashavanhu, A.J. Oberholster, S.P. Heyns, PCA vs. KPCA for turbine blade analysis, Eng. Struct. 318, 118712 (2024) [Google Scholar]
  18. Z. Wang, T. van der Laan, M. Usman, Self-adaptive quantum kernel PCA, Adv. Sci. 12, 2570102 (2025) [Google Scholar]
  19. M.T.H. Kaib et al., Data size reduction for nonlinear process monitoring using KPCA, Expert Syst. Appl. 274, 126975 (2025) [Google Scholar]
  20. B.K. Mishra, A. Das, A non-linear SP monitoring strategy: A case study, Mater. Today Proc. 41, 1107–1109 (2021) [Google Scholar]
  21. K.R. Storrs et al., Diverse deep neural networks predict human IT cortex, J. Cogn. Neurosci. 33, 2044–2064 (2021) [Google Scholar]
  22. C. Shi et al., ANN-based shelf life prediction: A review, Crit. Rev. Food Sci. Nutr. 64, 12009–12024 (2024) [Google Scholar]
  23. S. Bhatnagar et al., Chemical reaction neural networks for calorimetry data, J. Power Sources 628, 235834 (2025) [Google Scholar]
  24. M. Qureshi et al., Forecasting cardiovascular mortality using ANN, BMC Public Health 25, 34 (2025) [Google Scholar]
  25. A. Tharwat, Independent component analysis: An introduction, Appl. Comput. Inform. 17, 222–249 (2021) [Google Scholar]
  26. A. Hyvärinen, I. Khemakhem, H. Morioka, Nonlinear ICA for disentanglement, Patterns 4, (2023) [Google Scholar]
  27. E. Cerrillo-Cuenca, M. Sepúlveda, Z. Guerrero-Bueno, ICA for rock painting analysis, J. Archaeol. Sci. 125, 105269 (2021) [Google Scholar]
  28. G.T. Alencar, R.C. dos Santos, A. Neves, Fault recognition in transmission systems using ICA and CNN, Electr. Power Syst. Res. 229, 110105 (2024) [Google Scholar]
  29. F. Ricci, L. Bardone, S. Goldt, Feature learning from non-Gaussian inputs via ICA, arXiv preprint, arXiv:2503.23896 (2025) [Google Scholar]
  30. B.K. Mishra, A. Das, A stage-wise non-Gaussian SPC strategy, Eng. Res. Express 5, 015069 (2023) [Google Scholar]
  31. K. Li et al., Deep SVDD for hyperspectral anomaly detection, IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2022) [CrossRef] [Google Scholar]
  32. Q. Wu, W. Lu, X. Yan, Process monitoring of nonlinear uncertain systems using stacked autoencoder–SVDD, Appl. Soft Comput. 129, 109570 (2022) [Google Scholar]
  33. A. Beck, O. Noam. A note on the support vector data description problem. Oper Res Lett. ,107357.(2025) [Google Scholar]
  34. S.P. Fatemi et al., Distributed data storage using decision trees and SVM for IoT, Sustain. Comput. Inform. Syst. 46, 101134 (2025) [Google Scholar]
  35. M. Chen, S.J. Ludtke, Deep learning-based Gaussian mixture model for cryo-EM, Nat. Methods 18, 930–936 (2021) [Google Scholar]
  36. K. Rezaee et al., Deep learning-based real-time crowd anomaly detection, Pers. Ubiquit. Comput. 28, 135–151 (2024) [Google Scholar]
  37. A. Entezami et al., Early warning of structural damage using manifold learning, Mech. Syst. Signal Process. 224, 111984 (2025) [Google Scholar]
  38. Y. Li et al., Unsupervised embedding for process monitoring in repetitive systems, IISE Trans. 57, 724–739 (2025) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.