Application of Real Valued Neuro Genetic Algorithm in Detection of Components Present in Manhole Gas Mixture

  • Varun Kumar Ojha
  • Paramarta Dutta
  • Hiranmay Saha
  • Sugato Ghosh
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)

Abstract

The article deals with the implementation of an Intelligent System for detection of components present in manhole gas mixture. The detection of manhole gas is important because the manhole gas mixture contain many poisonous gases namely Hydrogen Sulfide (H 2 S), Ammonia (NH 3), Methane (CH 4), Carbon Dioxide (CO 2), Nitrogen Oxide (NO x ), and Carbon Monoxide (CO). A short exposure to any of these components with human beings endangers their lives. A gas sensor array is used for recognition of multiple gases simultaneously. At an instance the manhole gas mixture may contain many hazardous gas components. So it is wise to use specific gas sensor for each gas component in the gas sensor array. Use of multiple gas sensors and presence of multiple gases together result a cross-sensitivity. We implement a real valued neuro genetic algorithm to unravel the multiple gas detection issue.

Keywords

Cross-Sensitivity Gas Sensor Array Real Value Neuro Genetic Algorithm 

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References

  1. 1.
    Barsky, J.B.: Simultaneous Multi-Instrumental Monitoring of Vapors in Sewer Headspaces by Several Direct-Reading Instruments. Environmental Research 39(2), 307–320 (1986)CrossRefGoogle Scholar
  2. 2.
    Hutter, G.M.: Reference Data Sheet on Gas(es), Meridian Engineering & Technology (November 1993), http://www.meridianeng.com/sewergas.html
  3. 3.
    Haykin, S.: Neural Network a Comprehensive Foundation, 2nd edn. Pearson Prentice Hall (2005)Google Scholar
  4. 4.
    Goldberg, D.E.: Genetic Algorithms in search, Optimization & Machine learning, 1st edn. Pearson Education (2006) ISBN 81-7758-829-XGoogle Scholar
  5. 5.
    Mitchell, M.: An Introduction to Genetic Algorithms. First MIT Press paperback edition (1998) ISBN 0262631857Google Scholar
  6. 6.
    Sindhu, S.S.S., Geetha, S., Sivanath, S.S.: A Neuro-genetic ensemble Short Term Forecasting Framework for Anomaly Intrusion Prediction. IEEE (2006) 1-4244-0716-8/06/$20.00Google Scholar
  7. 7.
    Kwon, Y.-K., Moon, B.-R.: A Hybrid Neurogenetic Approach for Stock Forecasting. IEEE Transactions on Neural Network 18(3) (May 20)Google Scholar
  8. 8.
    Srivastava, A.K., Srivastava, S.K., Shukla, K.K. In: Search of A Good Neuro-Genetic Computational Paradigm. IEEE (2000) 0-7803-5812-0/00/$10.009Google Scholar
  9. 9.
    Srivastava, A.K., Srivastava, S.K., Shukla, K. K.: On The Design Issue of Intelligent Electronic Nose System. IEEE (2000) 0-7803-581 2-0/00/$10.00Google Scholar
  10. 10.
    Barrios, D., Carrascal, A., Manrique, D., Rios, J.: Cooperative binary-real coded genetic algorithms for generating and adapting artificial neural networks. Springer-Verlag London Limited (2003)Google Scholar
  11. 11.
    Ojha, V.K., Dutta, P., Saha, H.: Detection of proportion of different gas components present in manhole gas mixture using backpropagation neural network. In: International Conference on Information & Network Technology (in press, 2012)Google Scholar
  12. 12.
    Li, H.-Q., Li, L.: A novel hybrid real-valued genetic algorithm for optimization problems. In: International Conference on Computational Intelligence & Security (2007)Google Scholar
  13. 13.
    Stallings, W.: Computer Organization and Architecture, pp. 222-234. Macmillan Publishing Company, ISBN 0-02-415480-6Google Scholar
  14. 14.
    IEEE Computer Society, IEEE Standard for Binary Floating-Point Arithmetic, IEEE Std. 754-1985Google Scholar
  15. 15.
    Sivanadam, S.N., Deepa, S.N.: Principles of Soft Computing, 1st edn. Wiley India (p) Ltd. (2007) ISBN 10:81-265-1075-7Google Scholar
  16. 16.
    Ojha, V.K., Dutta, P., Saha, H., Ghosh, S.: Linear regression based statistical approach for detecting proportion of component gases in manhole gas mixture. In: International Symposium on Physics and Technology of Sensors (in press, 2012)Google Scholar
  17. 17.
    Wongchoosuk, C., Wisitsoraat, A., Tuantranont, A., Kerdcharoen, T., Wisitsoraatb, A.: Portable electronic nose based on carbon nanotube-SnO 2 gas sensors and its application for detection of methanol contamination in whiskeys. Sensors and Actuators B: Chemical,SNB-12243Google Scholar
  18. 18.
    Tsirigotis, G., Berry, L.: Neural Network Based Recognition, of CO and NH 3 Reducing Gases, Using a Metallic Oxide Gas Sensor Array. In: Scientific Proceedings of RTU. Series 7. Telecommunications and Electronics, vol. 3 (2003)Google Scholar
  19. 19.
    Ojha, V.K., Dutta, P., Saha, H., Ghosh, S.: A Neuro-Swarm Technique for the Detection of Proportion of Components in Manhole Gas Mixture. In: International Conference on Modeling, Optimization and Computing (in press, 2012)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Varun Kumar Ojha
    • 1
  • Paramarta Dutta
    • 1
  • Hiranmay Saha
    • 2
  • Sugato Ghosh
    • 2
  1. 1.Department of Computer & System SciencesVisva BharatiSantiniketanIndia
  2. 2.Centre of Excellence for Green Energy & Sensors SystemBengal Engineering & Science UniversityHowrahIndia

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