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, and S.X. Yang, A support vector machinediscriminator for tobacco growing areas based on near-infraredspectrum, Proc. 2012 IEEE International Conf. on Automationand Logistics (ICAL), Zhengzhou, China, 2012, 24–29.
  2. [2] D. Wang, F.C. Tian, S.X. Yang, and Z.Q. Zhu, Intelligentestimate of chemical compositions based on NIR spectra anal-ysis, Proc. 2017 IEEE International Conf. on Information andAutomation (IEEE ICIA 2017), Macau, China, 2017, 472–477.
  3. [3] G.Q. Qi, Z.Q. Zhu, K. Erqinhu, Y.N. Chen, Y. Chai, andJ. Sun, Fault-diagnosis for reciprocating compressors using bigdata and machine learning, Simulation Modelling Practice andTheory, 80, 2018, 104–127.
  4. [4] Z.Q. Zhu, H.P. Yin, Y. Chai, Y.X. Li, and G.Q. Qi, A novelmulti-modality image fusion method based on image decom-position and sparse representation, Information Sciences, 432,2018, 516–529.
  5. [5] H. Chen and 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.
  6. [6] A.M. Tehrani, M.S. Kamel, and A.M. Khamis, Fuzzy reinforcement learning for embedded soccer agents in a multi-agentcontext, International Journal of Robotics & Automation,21(2), 2006, 110–119.
  7. [7] C.Q. Huang, X.F. Peng, X.G. Wang, and S.J. Shi, New robust-adaptive algorithm for tracking control of robot manipulators,International Journal of Robotics & Automation, 23(2), 2008,67–78.
  8. [8] H.F. Li, X.S. Li, Z.T. Yu, and C.L. Mao, Multifocus image fu-sion by combining with mixed-order structure tensors and mul-tiscale neighborhood, Information Sciences, 349, 2016, 25–49.
  9. [9] Z.Q. Zhu, Y. Chai, H.P. Yin, Y.X. Li, and Z.D. Liu, A noveldictionary learning approach for multi-modality medical imagefusion, Neurocomputing, 214, 2016, 471–482.
  10. [10] D. Vassis, B.A. Kampouraki, P. Belsis, V. Zafeiris, N. Vas-silas, E. Galiotou, et al., Using neural networks and svmsfor automatic medical diagnosis: A comprehensive review,Proc. International Conf. on Integrated Information, Athens,Greece, 2015, 32–36.
  11. [11] Z.H. He, W.L. Lian, M.J. Wu, Y. Chen, L.Y. Tang, and J.Luo, Determination of tobacco constituents with acousto-optictunable filter-near infrared spectroscopy, Journal of NearInfrared Spectroscopy, 14(1), 2006, 45–50.
  12. [12] L. Li, S. Xu, X. An, and L.D. Zhang, A novel approach to NIRspectral quantitative analysis: Semi-supervised least-squaressupport vector regression machine, Spectroscopy and SpectralAnalysis, 31(10), 2011, 2702–2705.
  13. [13] Y. Zhang, Q. Cong, Y.F. Xie, J.X. Yang, and B. Zhao, Quan-titative analysis of routine chemical constituents in tobaccoby near-infrared spectroscopy and support vector machine,Spectrochimica Acta Part A—Molecular and BiomolecularSpectroscopy, 71(4), 2008, 1408–1413.
  14. [14] W. Zhao, T.H. Beach, and Y. Rezgui, Efficient least angleregression for identification of linear-in-the-parameters models,Proceedings of the Royal Society A—Mathematical Physicaland Engineering Sciences, 473(2198), 2017, 0775.
  15. [15] I. Chatzisavvas and F. Dohnal, Unbalance identification usingthe least angle regression technique, Mechanical Systems andSignal Processing, 50–51, 2015, 706–717.
  16. [16] R.R. Karn, I.M. Elfadel, Multicore power proxies using least-angle regression, Proc. 2015 IEEE International Symposiumon Circuits and Systems (ISCAS), Lisbon, Portugal, 2015,2872–2875.

Important Links:

Go Back