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Laser Fluence Recognition Using Computationally Intelligent Pulsed Photoacoustics Within the Trace Gases Analysis

  • M. LukićEmail author
  • Ž. Ćojbašić
  • M. D. Rabasović
  • D. D. Markushev
  • D. M. Todorović
ICPPP 18
  • 111 Downloads
Part of the following topical collections:
  1. ICPPP-18: Selected Papers of the 18th International Conference on Photoacoustic and Photothermal Phenomena

Abstract

In this paper, the possibilities of computational intelligence applications for trace gas monitoring are discussed. For this, pulsed infrared photoacoustics is used to investigate \(\hbox {SF}_{6}\)–Ar mixtures in a multiphoton regime, assisted by artificial neural networks. Feedforward multilayer perceptron networks are applied in order to recognize both the spatial characteristics of the laser beam and the values of laser fluence \(\Phi \) from the given photoacoustic signal and prevent changes. Neural networks are trained in an offline batch training regime to simultaneously estimate four parameters from theoretical or experimental photoacoustic signals: the laser beam spatial profile R(r), vibrational-to-translational relaxation time \(\tau _{V-T} \), distance from the laser beam to the absorption molecules in the photoacoustic cell r* and laser fluence \(\Phi \). The results presented in this paper show that neural networks can estimate an unknown laser beam spatial profile and the parameters of photoacoustic signals in real time and with high precision. Real-time operation, high accuracy and the possibility of application for higher intensities of radiation for a wide range of laser fluencies are factors that classify the computational intelligence approach as efficient and powerful for the in situ measurement of atmospheric pollutants.

Keywords

Artificial neural networks Laser beam profile Laser fluence Multiphoton processes Photoacoustic spectroscopy 

Notes

Acknowledgements

This work has been supported by the Ministry of Education, Science and Technological Development of Republic of Serbia under the Grants ON171016 and TR35016.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • M. Lukić
    • 1
    Email author
  • Ž. Ćojbašić
    • 2
  • M. D. Rabasović
    • 3
  • D. D. Markushev
    • 3
  • D. M. Todorović
    • 4
  1. 1.Faculty of Occupational SafetyUniversity of NišNišSerbia
  2. 2.Mechanical Engineering FacultyUniversity of NišNišSerbia
  3. 3.Institute of PhysicsUniversity of BelgradeBelgrade-ZemunSerbia
  4. 4.Institute for Multidisciplinary ResearchUniversity of BelgradeBelgradeSerbia

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