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A Soft Calibration Technique for Thermistor Using Support Vector Machine

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Proceedings of the Third International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 258))

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Abstract

This paper aims at designing an calibration technique for temperature measurement using support vector machine. The objectives of the present work are: (i) to extend the linearity range of measurement to 100 % of input range, and (ii) to make measurement technique adaptive to variations in physical parameters of thermistor like reference resistance and temperature coefficient. Support vector machine (SVM) is trained to achieve the proposed objectives. The proposed measurement technique is tested considering variations in physical parameters of thermistor like reference resistance \( \left( {R_{o} } \right) \) and temperature coefficient. Results show that the proposed intelligent technique has fulfilled the set objectives.

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References

  1. Attari, M.: Methods For Linearization of Non-Linear Sensors. In: Proceedings of CMMNI-4, Fourth Maghrebin Conference on Numerical Methods of Engineering, Algiers (Algeria), vol. 1, pp. 344–350 (1993)

    Google Scholar 

  2. Freeman, J.A.: Neural Networks—Algorithms, Applications, and Programming Techniques. Addison-Wesley, Massachusetts (1992)

    Google Scholar 

  3. Aggoune, M.E., Boudjema, F., Bensenouci, A., Hellal, A., Vadari, S.V., El Mesai, M.R.: Design of an adaptive-structure voltage regulator using artificial neural networks. In: Proceedings of the 2nd IEEE Conference on Control Applications, Vancouver, Canada, Sept 1993, pp. 337–343 (1993)

    Google Scholar 

  4. Pau, L.F., Johansen, F.S.: Neural Network Signal Understanding for Instrumentation. IEEE Trans. Instrum. Meas. 39(4), 558–564 (1990)

    Article  Google Scholar 

  5. Wang, L.M., Ma, L.Y., Huang, Y.: A general neural network-based approach to modeling sensors in PSPICE simulation. In: Proceedings of International Conference on Natural Computation, Hainon, China (2007)

    Google Scholar 

  6. Renneberg, C., Lehman, T.: Analog circuits for thermistor linearization with Chebyshev-optimal linearity error. In: Proceedings of European Conference on Circuit Theory and Design, Seville (2007)

    Google Scholar 

  7. Wang, L.M., Deng, Y.F., Zhao, X.L., Liu, B.L.: A neural network-based approach for creating a ntc thermistor model library for PSPICE. In: Computational Intelligence and Security Workshop, Hong Kong, Oct 2008 (2008)

    Google Scholar 

  8. Nenova, Z.P., Nenov, T.G.: Linearization circuit of the thermistor connection. IEEE Trans. Instrum. Meas. 58(2), 441–449 (2009)

    Google Scholar 

  9. Khan, S.A., Shahani, D.T., Agarwala, A.K.: Sensor Calibration and Compensation using Artificial Neural Network. ISA Trans. 48, 143–144 (2009)

    Article  Google Scholar 

  10. Tsai, C.-F., Li, L.-T., Li, C.-H., Young, M.-S.: Implementation of thermistor linearization using LabVIEW. In: Proceedings of International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Kyoto, Japan (2009)

    Google Scholar 

  11. Balraj, A., Patvardhan, A., Renuka Devi, V., Aiswarya, R., Prasen, V.: Embedded temperature monitoring and control unit. In: Proceedings of International Conference on Recent Trends in Information, Telecommunication and Computing, kochi, India (2010)

    Google Scholar 

  12. Dey, D., Munshi, S.: Simulation studies on a new intelligent scheme for temperature measurement using thermistor in 555 timer circuit. Int. J. Smart Sens. Intell. Syst. 3(2), 217–229 (2010)

    Google Scholar 

  13. Madhu Mohan, N., Jagadeesh Kumar, V., Sankaran, P.: Linearizing dual-slope digital converter suitable for a thermistor. IEEE Trans. Instrum. Meas. 60(5), 1515–1521 (2011)

    Google Scholar 

  14. Hambali, N., Saat, S., Ramli, M.S., Hazmi, M.: Automatic detection computer-based (ADCob) system for temperature measurement calibration of RTD. In: Proceedings of International Conference on Electrical, Control and Computer Engineering, Pahang, Malaysia (2011)

    Google Scholar 

  15. Weng, Z., Zhou, R.: Design and simulation of temperature measurement cirucit based on LabVIEW and multisim. In: Proceedings of Second International Conference on Mechanic Automation and Control Engineering, Mongolia, China (2011)

    Google Scholar 

  16. DAleo, F.P., Prasser, H.-M.: Design, calibration and testing of a thin film temperature gauge array for temperature and heat flux measurements in fluid mixing experiments. J. Flow Meas. Instrum. 24, 29–35 (2012)

    Google Scholar 

  17. Sonowal, D., Bhuyan, M.: FPGA implementation of neural network for linearization of thermistor characteristics. In: Proceedings of International Conference on Devices, Circuits and System, Coimbatore, India (2012)

    Google Scholar 

  18. Mrugala, D., Ziegler, F., Kostelnik, J., Lang, W.: Temperature sensor measurement system for firefighter gloves. In: Proceedings of Eurosensors, Poland (2012)

    Google Scholar 

  19. Steinhart, J.S, Hart S.R.: Calibration curves for thermistors. Deep Sea Res. 15, 497 (1968)

    Google Scholar 

  20. Neubert, H.K.P.: Instrument Transducers—an Introduction to their Performance and Design. Clarendon Press, Oxford (1975)

    Google Scholar 

  21. Bosson, G., Guttman, F., Simmons, L.M.: Relationship between temperature and resistance of a thermistor. J. Appl. Phys. 21(12), 1267–1268 (1950)

    Article  Google Scholar 

  22. Deoblin, E.O.: Measurement Systems Application and Design, 3rd edn. Mc Graw Hill, New York (2005)

    Google Scholar 

  23. Pallas-Areny, R., Webster, J.G.: Sensors and signal conditioning, 2nd edn. A Wiley-Interscience Publication, New York (2001)

    Google Scholar 

  24. Chang, C.-C., Lin, C.–J.: LIBSVM. A library for support vector machines (2001). Available via http://www.csie.ntu.edu.tw/cjlin/libsvm

  25. Xuegong, Z.: Statical learning theory and support vector machines. Acta Automatica Sinica. 26(1), 32–42 (2000)

    MathSciNet  Google Scholar 

  26. Suykens, J.A.K., Vandewalle, J.: Least Squares Support Vector Machine Classifiers. Kluwer Academic Publisher, Netherlands (1999)

    Google Scholar 

  27. Schokopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, Cambridge (2002)

    Google Scholar 

  28. Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific Publishers Co, Singapore (2002)

    Book  MATH  Google Scholar 

  29. Gestel, T.V., Suykens, J.A.K., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., De Moor, B., Vandewalle, J.: Benchmarking least squares support vector machine classifiers. J. Mach. Learn. 54(1), 5–32 (2004)

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal, India

    K. V. Santhosh

  2. Department of Electrical Engineering, National Institute of Technology Silchar, Silchar, India

    B. K. Roy

Authors
  1. K. V. Santhosh
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  2. B. K. Roy
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Correspondence to K. V. Santhosh .

Editor information

Editors and Affiliations

  1. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

  2. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  3. Department of Mathematics and Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

  4. Department of Applied Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

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Santhosh, K.V., Roy, B.K. (2014). A Soft Calibration Technique for Thermistor Using Support Vector Machine. In: Pant, M., Deep, K., Nagar, A., Bansal, J. (eds) Proceedings of the Third International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 258. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1771-8_26

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  • DOI: https://doi.org/10.1007/978-81-322-1771-8_26

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1770-1

  • Online ISBN: 978-81-322-1771-8

  • eBook Packages: EngineeringEngineering (R0)

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