Overcoming Long Recovery Time of Metal-Oxide Gas Sensor With Certainty Factor Sensing Algorithm

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

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VOLUME 7 , ISSUE 5 (December 2014) > List of articles

Special issue ICST 2014

Overcoming Long Recovery Time of Metal-Oxide Gas Sensor With Certainty Factor Sensing Algorithm

Kok Seng Eu / Kian Meng Yap

Keywords : Gas detection, Certainty Factor Sensing, Odour plume tracking, MOX Gas Sensor

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 7, Issue 5, Pages 1-6, DOI: https://doi.org/10.21307/ijssis-2019-077

License : (CC BY-NC-ND 4.0)

Published Online: 15-February-2020

ARTICLE

ABSTRACT

Gas leaking in gas production industry is a serious issue which could cause explosion or pose a high risk to human life. The searching of leaking gas can be performed by robots. It is better than using human beings because searching of leaking gas is a high risk task. Most of the gas sensors used in industries is semiconductor metal-oxide (MOX) type due to its low cost, ease of use, high sensitivity and fast response time in gas sensing, and ability to detect large number of gases. However, there is a fatal limitation i. e. long recovery time after the exposure of the target gas. It definitely causes robots to fail in gas/odour plume searching tasks due to delay of responses during the absent of gas plume. This paper proposes a sensing algorithm based on evidential theory which is using certainty factors and evidential reasoning to overcome the long recovery problem. Based on the conducted experiments, the proposed algorithm has improved the accuracy and reliability while maintaining its performance in recovery time. It performs better than other algorithms such as simple threshold methods, transient response algorithm and system modelling approach.

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REFERENCES

[1] J. Gutiérrez and M. C. Horrillo, “Advances in artificial olfaction: Sensors and applications,” Talanta, vol. 124, pp. 95–105, Jun. 2014.

[2] J.-H. L. J.K. Choi, I.S.Hwang, S.J.Kim, J.S.Park, S.SPark, U.Jeong, Y.C.Kang, “Chem.150,” Sens. ActuatorsB, pp. 190–199, 2010.

[3] S. Marco, A. Gutiérrez-Gálvez, and A. Lansner, “A biomimetic approach to machine olfaction, featuring a very large-scale chemical sensor array and embedded neuro-bio-inspired computation,” Microsyst. Technol., vol. 20, no. 4–5, pp. 729–742, Dec. 2013.

[4] L. Zhang and F. Tian, “Performance Study of Multilayer Perceptrons in a Low-Cost Electronic Nose,” IEEE Trans. Instrum. Meas., pp. 1–1, 2014.

[5] F. K. Che Harun, J. a Covington, and J. W. Gardner, “Mimicking the biological olfactory system: a Portable electronic Mucosa.,” IET Nanobiotechnol., vol. 6, no. 2, pp. 45–51, Jun. 2012.

[6] J. Gonzalez-Jimenez, J. G. Monroy, and J. L. Blanco, “The MultiChamber Electronic Nose--an improved olfaction sensor for mobile robotics.,” Sensors (Basel)., vol. 11, no. 6, pp. 6145–64, Jan. 2011.

[7] A. J. Lilienthal, A. Loutfi, and T. Duckett, “Airborne Chemical Sensing with Mobile Robots,” Sensors, vol. 6, no. 11, pp. 1616– 1678, Nov. 2006.

[8] G. Lu, A. Zhang, J. Zhou, S. Cui, and L. Zhao, “Experiment Research of Robot Biological-Inspired Active Olfaction Strategy Based on Wandering Albatross Behavior,” pp. 214–220, 2012.

[9] K. J. Albert and N. S. Lewis, “Cross Reactive Chemical Sensor Arrays,” Chem. Rev., no. 100, pp. 2595– 2626, 2000.

[10] T. Review and U. Weimar, “Understanding the fundamental principles of metal oxide based gas sensors,” J. Phys. Condens. Matter, vol. 15, pp. 813–839, 2003.

[11] L. L. López, “Moth-Like Chemo-Source Localisation and Classification on an Indoor Autonomous Robot,” in On Biomimetics, 2011, pp. 453–466.

[12] K. S. Eu, K. M. Yap, and T. H. Tee, “Olfactory sensory system for odour plume sensing process by using quadrotor based flying sniffer robot,” in International Conference on Robotics, Biomimetics, Intelligent Computational Systems, 2013, pp. 188–193.

[13] H. Ishida, G. Nakayama, T. Nakamoto, and T. Moriizumi, “Controlling a gas/odor plume-tracking robot based on transient responses of gas sensors,” IEEE Sens. J., vol. 5, no. 3, pp. 537–545, Jun. 2005.

[14] J. G. Monroy, J. González-Jiménez, and J. L. Blanco, “Overcoming the slow recovery of MOX gas sensors through a system modeling approach.,” Sensors (Basel)., vol. 12, no. 10, pp. 13664–80, Jan. 2012.

[15] M. Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, 3rd Etd. Pearson Education Limited, 2011, pp. 74– 83.

[16] K. S. Eu, S. L. Yong, M. W. Yip, Y. K. Lee, Y. H. Ko, and K. M. Yap, “Fingers Bending Motion Controlled Electrical Wheelchair by using Flexible Bending Sensors with Kalman filter Algorithm,” in 3rd International Conference on Convergence and its Application, 2014.

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