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All issues Volume 377 (2023) MATEC Web Conf., 377 (2023) 01025 References
Open Access
Issue
MATEC Web Conf.
Volume 377, 2023
Curtin Global Campus Higher Degree by Research Colloquium (CGCHDRC 2022)
Article Number 01025
Number of page(s) 11
Section Engineering and Technologies for Sustainable Development
DOI https://doi.org/10.1051/matecconf/202337701025
Published online 17 April 2023
  1. Gargalo, C. L., Udugama, I., Pontius, K., Lopez, P. C., Nielsen, R. F., Hasanzadeh, A., & Gernaey, K. V. (2020). Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes. Journal of Industrial Microbiology & Biotechnology: Official Journal of the Society for Industrial Microbiology and Biotechnology, 47(11), 947–964. [CrossRef] [Google Scholar]
  2. Liu, Y., & Xie, M. (2020). Rebooting data-driven soft-sensors in process industries: A review of kernel methods. Journal of Process Control, 89, 58–73. [CrossRef] [Google Scholar]
  3. Ngu, J. C. Y., & Yeo, C. (2022). A Comparative Study of Different Kernel Functions Applied to LW-KPLS Model for Nonlinear Processes. Biointerface Research in Applied Chemistry, 13(2). [Google Scholar]
  4. Souza, F. A., Araújo, R., & Mendes, J. (2016). Review of soft sensor methods for regression applications. Chemometrics and Intelligent Laboratory Systems, 152, 69–79. [CrossRef] [Google Scholar]
  5. Jin, H., Chen, X., Yang, J., & Wu, L. (2014). Adaptive soft sensor modeling framework based on just-in-time learning and kernel partial least squares regression for nonlinear multiphase batch processes. Computers & Chemical Engineering, 71, 77–93. [CrossRef] [Google Scholar]
  6. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine learning, 20(3), 273–297. [Google Scholar]
  7. Curreri, F., Fiumara, G., & Xibilia, M. G. (2020). Input selection methods for soft sensor design: A survey. Future Internet, 12(6), 97. [CrossRef] [Google Scholar]
  8. Suykens, J. A., De Brabanter, J., Lukas, L., & Vandewalle, J. (2002). Weighted least squares support vector machines: robustness and sparse approximation. Neurocomputing, 48(1-4), 85–105. [CrossRef] [Google Scholar]
  9. Xiaoming, L., Xinglong, T., & Changsheng, Z. (2019). PSO-LSSVR assisted GPS/INS positioning in occlusion region. Sensors, 19(23), 5256. [CrossRef] [Google Scholar]
  10. Sheng, X., & Xiong, W. (2020). Soft sensor design based on phase partition ensemble of LSSVR models for nonlinear batch processes. Mathematical Biosciences and Engineering, 17(2), 1901–1921. [CrossRef] [Google Scholar]
  11. Adnan, R. M., Liang, Z., Yuan, X., Kisi, O., Akhlaq, M., & Li, B. (2019). Comparison of LSSVR, M5RT, NF-GP, and NF-SC models for predictions of hourly wind speed and wind power based on cross-validation. Energies, 12(2), 329. [CrossRef] [Google Scholar]
  12. Kisi, O., & Parmar, K. S. (2016). Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. Journal of Hydrology, 534, 104–112. [CrossRef] [Google Scholar]
  13. Bao, L., Yuan, X., & Ge, Z. (2015). Co-training partial least squares model for semi-supervised soft sensor development. Chemometrics and Intelligent Laboratory Systems, 147, 75–85. [CrossRef] [Google Scholar]
  14. Zhu, P., Liu, X., Wang, Y., & Yang, X. (2018). Mixture semisupervised Bayesian principal component regression for soft sensor modeling. IEEE Access, 6, 40909–40919. [CrossRef] [Google Scholar]
  15. Mickel, V. M., Yeo, W. S., & Saptoro, A. (2019). Evaluating the Performance of Newly Integrated Model in Nonlinear Chemical Process Against Missing Measurements. Chemical Product and Process Modeling, 14(4). [CrossRef] [Google Scholar]
  16. Xu, S., An, X., Qiao, X., Zhu, L., & Li, L. (2013). Multi-output least-squares support vector regression machines. Pattern Recognition Letters, 34(9), 1078–1084. [CrossRef] [Google Scholar]
  17. Achirul Nanda, M., Boro Seminar, K., Nandika, D., & Maddu, A. (2018). A comparison study of kernel functions in the support vector machine and its application for termite detection. Information, 9(1), 5. [CrossRef] [Google Scholar]
  18. Thien, T. F., & Yeo, W. S. (2022). A comparative study between PCR, PLSR, and LW-PLS on the predictive performance at different data splitting ratios. Chemical Engineering Communications, 209(11), 1439–1456. [CrossRef] [Google Scholar]
  19. Tange, R. I., Rasmussen, M. A., Taira, E., & Bro, R. (2017). Benchmarking support vector regression against partial least squares regression and artificial neural network: Effect of sample size on model performance. Journal of Near Infrared Spectroscopy, 25(6), 381–383. [CrossRef] [Google Scholar]
  20. Yeo, W. S. (2019). Adaptive Soft Sensors for Non-Gaussian Chemical Process Plant Data Based on Locally Weighted Partial Least Square (Doctoral dissertation, Curtin University). [Google Scholar]
  21. Wang, W., & Lu, Y. (2018, March). Analysis of the mean absolute error (MAE) and the root mean square error (RMSE) in assessing rounding model. In IOP conference series: materials science and engineering (Vol. 324, No. 1, p. 012049). IOP Publishing. [CrossRef] [Google Scholar]
  22. Goldrick, S., Ştefan, A., Lovett, D., Montague, G., & Lennox, B. (2015). The development of an industrial-scale fed-batch fermentation simulation. Journal of biotechnology, 193, 70–82. [CrossRef] [Google Scholar]
  23. Goldrick, S., Duran-Villalobos, C. A., Jankauskas, K., Lovett, D., Farid, S. S., & Lennox, B. (2019). Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process. Computers & Chemical Engineering, 130, 106471. [CrossRef] [Google Scholar]
  24. Drewnik, M., & Pasternak-Winiarski, Z. (2017, June). SVM kernel configuration and optimization for the handwritten digit recognition. In IFIP International Conference on Computer Information Systems and Industrial Management (pp. 87–98). Springer, Cham. [Google Scholar]
  25. Tang, Q., Qiu, W., & Zhou, Y. (2019). Classification of complex power quality disturbances using optimized S-transform and kernel SVM. IEEE Transactions on Industrial Electronics, 67(11), 9715–9713. [Google Scholar]
  26. Nguyen, H., Bui, X. N., Choi, Y., Lee, C. W., & Armaghani, D. J. (2021). A novel combination of whale optimization algorithm and support vector machine with different kernel functions for prediction of blasting-induced fly-rock in quarry mines. Natural Resources Research, 30(1), 191–193. [CrossRef] [Google Scholar]
  27. Mehmani, A., Chowdhury, S., Meinrenken, C., & Messac, A. (2018). Concurrent surrogate model selection (COSMOS): optimizing model type, kernel function, and hyper-parameters. Structural and Multidisciplinary Optimization, 57(3), 1093–1193. [CrossRef] [Google Scholar]
  28. Trygg, J., & Lundstedt, T. (2007). Chapter 6—chemometrics techniques for metabonomics. The handbook of metabonomics and metabolomics, 171–199. [CrossRef] [Google Scholar]
  29. Wang, B. W., Tang, W. Z., Song, L. K., & Bai, G. C. (2020, December). PSO-LSSVR: A surrogate modeling approach for probabilistic flutter evaluation of compressor blade. In Structures (Vol. 28, pp. 1634–1645). Elsevier. [CrossRef] [Google Scholar]

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