AUTOMATIC PREDICTION OF LEAVE CHEMICAL COMPOSITIONS BASED ON NIR SPECTROSCOPY WITH MACHINE LEARNING

Di Wang, Fengchun Tian, Zhiqin Zhu, and Wenjie Pan

References

  1. [1] L. Xie, W.J. Pan, S.X. Yang, A support vector machine discriminator for tobacco growing areas based on near-infrared spectrum, Proc. 2012 Ieee International Conference on Automation and Logistics (Ical), Zhengzhou, China, 2012, 24-29.
  2. [2] D. Wang, F.C. Tian, S.X. Yang, Z.Q. Zhu, Intelligent estimate of chemical compositions based on NIR spectra analysis, Proc. 2017 Ieee International Conference on Information and Automation (Ieee Icia 2017), Macau, China, 2017, 472-477.
  3. [3] ZhiqinZhu, et al., A novel dictionary learning approach for multi-modality medical image fusion, Neurocomputing, 214, 2016, 471-482.
  4. [4] G.Q. Qi, et al., Fault-diagnosis for reciprocating compressors using big data and machine learning, Simulation Modelling Practice and Theory, 80, 2018, 104-127.
  5. [5] Z.Q. Zhu, et al., A novel multi-modality image fusion method based on image decomposition and sparse representation, Information Sciences, 432, 2018, 516-529.
  6. [6] H. Chen, L. Xie, A novel artificial potential field-based reinforcement learning for mobile robotics in ambient intelligence, International Journal of Robotics & Automation, 24(3), 2009, 1.
  7. [7] A.M. Tehrani, M.S. Kamel, A.M. Khamis, Fuzzy reinforcement learning for embedded soccer agents in a multi-agent context, International Journal of Robotics & Automation, 21(2), 2006, 110-119.
  8. [8] C.Q. Huang, X.F. Peng, X.G. Wang, S.J. Shi, New robust-adaptive algorithm for tracking control of robot manipulators, International Journal of Robotics & Automation, 23(2), 2008, 67-78.
  9. [9] H.F. Li, X.S. Li, Z.T. Yu, C.L. Mao, Multifocus image fusion by combining with mixed-order structure tensors and multiscale neighborhood, Information Sciences, 349, 2016, 25-49.
  10. [10] Z.Q. Zhu, et al., A novel dictionary learning approach for multi-modality medical image fusion, Neurocomputing, 214, 2016, 471-482.
  11. [11] D. Vassis, et al., Using neural networks and svms for automatic medical diagnosis: A comprehensive review, Proc. International Conference on Integrated Information, Athens, Greece, 2015, 32-36.
  12. [12] Z.H. He, et al., Determination of tobacco constituents with acousto-optic tunable filter-near infrared spectroscopy, Journal of Near Infrared Spectroscopy, 14(1), 2006, 45-50.
  13. [13] L. Li, S. Xu, X. An, L.D. Zhang, A novel approach to nir spectral quantitative analysis : Semi-supervised least-squares support vector regression machine, Spectroscopy and Spectral Analysis, 31(10), 2011, 2702-2705.
  14. [14] Y. Zhang, et al., Quantitative analysis of routine chemical constituents in tobacco by near-infrared spectroscopy and support vector machine, Spectrochimica Acta Part a-Molecular and Biomolecular Spectroscopy, 71(4), 2008, 1408-1413.
  15. [15] W. Zhao, T.H. Beach, Y. Rezgui, Efficient least angle regression for identification of linear-in-the-parameters models, Proceedings of the Royal Society a-Mathematical Physical and Engineering Sciences, 473(2198), 2017.
  16. [16] I. Chatzisavvas, F. Dohnal, Unbalance identification using the least angle regression technique, Mechanical Systems and Signal Processing, 50-51, 2015, 706-717.
  17. [17] R.R. Karn, I.M. Elfadel, Multicore power proxies using least-angle regression, Proc. 2015 Ieee International Symposium on Circuits and Systems (Iscas), Lisbon, Portugal, 2015, 2872-2875.

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