Optimizing a Sensor to Detect Ammonium Nitrate Based IEDS in Vehicles Using Artificial Neural Networks

Bourdillon Omijeh; Akani Okemeka Machiavelli

doi:doi:10.11648/j.ajnna.20190501.11

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Optimizing a Sensor to Detect Ammonium Nitrate Based IEDS in Vehicles Using Artificial Neural Networks

Bourdillon Omijeh, Akani Okemeka Machiavelli

Published in American Journal of Neural Networks and Applications (Volume 5, Issue 1)

Received: 12 April 2019 Accepted: 21 May 2019 Published: 10 June 2019

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Abstract

Ammonium nitrate based explosives are a choice weapon for many terrorist groups due to its ease in manufacturing and high velocity of detonation. These explosives undergo thermal decomposition to release ammonia gas in traces of about 5- 25 Parts per Million (PPM) below the olfactory threshold. Ammonia is a reducing gas. MQ137 sensors are low cost commercially available metal oxide semiconductor ammonia gas sensors with a problem of selectivity (reacting with other reducing gasses like carbon monoxide etc) and sensitivity. We present the optimization of MQ137 metal oxide semiconductor electrochemical sensor using MATLAB, to improve its selectivity and sensitivity for accurately recognizing the characteristics of ammonia gas within specified PPM range as a sign of ammonium nitrate based explosives in vehicles. In this study, MQ137 sensor was connected with an ARDUINO microcontroller to a digital computer (2.40 GHz processor) and pre-heated for 12 hours before being exposed to ammonia gas in a controlled environment at room temperature to extract features (sensitivity constant and concentration in PPM) of ammonia gas with MQ137 sensor. 150 data samples of each feature were extracted and trained in a multilayer pattern recognition neural network with one hidden layer and 50 data samples containing features of other reducing gasses from the data sheet were used for testing. Test performance of multilayer artificial neural network has an accuracy of 100% with no misclassifications.

Published in	American Journal of Neural Networks and Applications (Volume 5, Issue 1)
DOI	10.11648/j.ajnna.20190501.11
Page(s)	1-6
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

IED Detection, MQ137 Sensor, Neural Networks, Ammonia Gas, Optimization, PPM

References

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[2]	SUSANA M. SILVA, JORGE D. GAMARRA, CÉSAR A. HERNÁNDEZ, JOHANN F. OSMA, "DESIGN AND FABRICATION OF A SENSOR FOR EXPLOSIVES AS A FIRST STEP TO AN IED DETECTION DEVICE.," IEEE JOURNAL, 2014.
[3]	R. D. DELGADO, "TIN OXIDE GAS SENSORS: AN ELECTROCHEMICAL APPROACH," UNIVERSITY OF BARCELONA PRESS, BARCELONA, SEPTEMBER 2002.
[4]	L. D. A. A. N. MINNAJA, "SENSITIVITY AND SELECTIVITY OF A THIN-FILM TIN OXIDE GAS SENSOR," SENSORS AND ACTUATORS B, PP. 197-204, 1991.
[5]	ATA JAHANGIR MOSHAYEDI ET AL, "MATHEMATICAL MODELLING FOR SNO2 GAS SENSOR BASED ON SECOND-ORDER RESPONSE," IN IEEE SYMPOSIUM ON INDUSTRIAL ELECTRONICS & APPLICATIONS, KUCHING, MALAYSIA, 2013.
[6]	L. ZHENGZHOU WINSEN ELECTRONICS TECHNOLOGY CO.. [ONLINE]. AVAILABLE: WWW.WINSEN-SENSOR.COM.
[7]	MANN D. P., PRATT K. F. E., PARASKEVA T., PARKIN I. P., METAL OXIDE SEMICONDUCTOR GAS SENSORS UTILISING MODIFIED ZEOLITE CATALYSTS TO IMPROVE SELECTIVITY, IEEE PROCEEDING, PP 184-187, 2014.
[8]	R. S. FALCONER, R LEC, J. F. VETELINO AND Z. XU, OPTIMIZATION OF A SAW METAL OXIDE SEMICONDUCTOR GAS SENSOR, ULTRASONICS SYMPOSIUM, PP 585-590, 1989.
[9]	J. FRANK, M. FLEISCHER, H. MEIXNER, A. FELTZ, ENHANCEMENT OF SENSITIVITY AND CONDUCTIVITY OF SEMICONDUCTING GA2O3 GAS SENSORS BY DOPING WITH SNO2.
[10]	ERIC N. DATTOLI AND KURT D. BENKSTEIN, IMPROVING THE SELECTIVITY OF A METAL OXIDE NANOWIRE GAS SENSOR USING A MICROHOTPLATE/FET PLATFORM, INTERNATIONAL SUSTAINABLE DEVELOPMENT RESEARCH SOCIETY (ISDRS), 2011.
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[13]	J. HOFFMAN, MASTERING ARDUINO; A PROJECT BASED APPROACH TO ELECTRONICS, CIRCUITS, AND PROGRAMMING, BIRMINGHAM, UK: PACKT PUBLISHING LIMITED, 2018.
[14]	S. HAYKIN, NEURAL NETWORKS AND LEARNING MACHINE, THIRD EDITION ED., ONTARIO, CANADA: PEARSON EDUCATION INCOPORATED, 2009.
[15]	KH TOHIDUL ISLAM, GHULAM MUJTABA, DR. RAM GOPALRAJ, HENRY FRIDAY NWEKE, HANDWRITTEN DIGITS RECOGNITION WITH ARTIFICIAL NEURAL NETWORK, INTERNATIONAL CONFERENCE ON ENGINEERING TECHNOLOGY AND TECHNOPRENEURSHIP (ICE2T), 18-20 SEPTEMBER, KUALA LUMPUR, MALAYSIA, 2017.

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Bourdillon Omijeh, Akani Okemeka Machiavelli. (2019). Optimizing a Sensor to Detect Ammonium Nitrate Based IEDS in Vehicles Using Artificial Neural Networks. American Journal of Neural Networks and Applications, 5(1), 1-6. https://doi.org/10.11648/j.ajnna.20190501.11

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ACS Style

Bourdillon Omijeh; Akani Okemeka Machiavelli. Optimizing a Sensor to Detect Ammonium Nitrate Based IEDS in Vehicles Using Artificial Neural Networks. Am. J. Neural Netw. Appl. 2019, 5(1), 1-6. doi: 10.11648/j.ajnna.20190501.11

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AMA Style

Bourdillon Omijeh, Akani Okemeka Machiavelli. Optimizing a Sensor to Detect Ammonium Nitrate Based IEDS in Vehicles Using Artificial Neural Networks. Am J Neural Netw Appl. 2019;5(1):1-6. doi: 10.11648/j.ajnna.20190501.11

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@article{10.11648/j.ajnna.20190501.11,
  author = {Bourdillon Omijeh and Akani Okemeka Machiavelli},
  title = {Optimizing a Sensor to Detect Ammonium Nitrate Based IEDS in Vehicles Using Artificial Neural Networks},
  journal = {American Journal of Neural Networks and Applications},
  volume = {5},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ajnna.20190501.11},
  url = {https://doi.org/10.11648/j.ajnna.20190501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20190501.11},
  abstract = {Ammonium nitrate based explosives are a choice weapon for many terrorist groups due to its ease in manufacturing and high velocity of detonation. These explosives undergo thermal decomposition to release ammonia gas in traces of about 5- 25 Parts per Million (PPM) below the olfactory threshold. Ammonia is a reducing gas. MQ137 sensors are low cost commercially available metal oxide semiconductor ammonia gas sensors with a problem of selectivity (reacting with other reducing gasses like carbon monoxide etc) and sensitivity. We present the optimization of MQ137 metal oxide semiconductor electrochemical sensor using MATLAB, to improve its selectivity and sensitivity for accurately recognizing the characteristics of ammonia gas within specified PPM range as a sign of ammonium nitrate based explosives in vehicles. In this study, MQ137 sensor was connected with an ARDUINO microcontroller to a digital computer (2.40 GHz processor) and pre-heated for 12 hours before being exposed to ammonia gas in a controlled environment at room temperature to extract features (sensitivity constant and concentration in PPM) of ammonia gas with MQ137 sensor. 150 data samples of each feature were extracted and trained in a multilayer pattern recognition neural network with one hidden layer and 50 data samples containing features of other reducing gasses from the data sheet were used for testing. Test performance of multilayer artificial neural network has an accuracy of 100% with no misclassifications.},
 year = {2019}
}

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TY  - JOUR
T1  - Optimizing a Sensor to Detect Ammonium Nitrate Based IEDS in Vehicles Using Artificial Neural Networks
AU  - Bourdillon Omijeh
AU  - Akani Okemeka Machiavelli
Y1  - 2019/06/10
PY  - 2019
N1  - https://doi.org/10.11648/j.ajnna.20190501.11
DO  - 10.11648/j.ajnna.20190501.11
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20190501.11
AB  - Ammonium nitrate based explosives are a choice weapon for many terrorist groups due to its ease in manufacturing and high velocity of detonation. These explosives undergo thermal decomposition to release ammonia gas in traces of about 5- 25 Parts per Million (PPM) below the olfactory threshold. Ammonia is a reducing gas. MQ137 sensors are low cost commercially available metal oxide semiconductor ammonia gas sensors with a problem of selectivity (reacting with other reducing gasses like carbon monoxide etc) and sensitivity. We present the optimization of MQ137 metal oxide semiconductor electrochemical sensor using MATLAB, to improve its selectivity and sensitivity for accurately recognizing the characteristics of ammonia gas within specified PPM range as a sign of ammonium nitrate based explosives in vehicles. In this study, MQ137 sensor was connected with an ARDUINO microcontroller to a digital computer (2.40 GHz processor) and pre-heated for 12 hours before being exposed to ammonia gas in a controlled environment at room temperature to extract features (sensitivity constant and concentration in PPM) of ammonia gas with MQ137 sensor. 150 data samples of each feature were extracted and trained in a multilayer pattern recognition neural network with one hidden layer and 50 data samples containing features of other reducing gasses from the data sheet were used for testing. Test performance of multilayer artificial neural network has an accuracy of 100% with no misclassifications.
VL  - 5
IS  - 1
ER  -

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Author Information

Bourdillon Omijeh

Department of Electronics and Computer Engineering, University of Port Harcourt, Port Harcourt, Nigeria
Akani Okemeka Machiavelli

Department of Electronics and Computer Engineering, University of Port Harcourt, Port Harcourt, Nigeria

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APA Style

Bourdillon Omijeh, Akani Okemeka Machiavelli. (2019). Optimizing a Sensor to Detect Ammonium Nitrate Based IEDS in Vehicles Using Artificial Neural Networks. American Journal of Neural Networks and Applications, 5(1), 1-6. https://doi.org/10.11648/j.ajnna.20190501.11

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ACS Style

Bourdillon Omijeh; Akani Okemeka Machiavelli. Optimizing a Sensor to Detect Ammonium Nitrate Based IEDS in Vehicles Using Artificial Neural Networks. Am. J. Neural Netw. Appl. 2019, 5(1), 1-6. doi: 10.11648/j.ajnna.20190501.11

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AMA Style

Bourdillon Omijeh, Akani Okemeka Machiavelli. Optimizing a Sensor to Detect Ammonium Nitrate Based IEDS in Vehicles Using Artificial Neural Networks. Am J Neural Netw Appl. 2019;5(1):1-6. doi: 10.11648/j.ajnna.20190501.11

Copy | Download

@article{10.11648/j.ajnna.20190501.11,
  author = {Bourdillon Omijeh and Akani Okemeka Machiavelli},
  title = {Optimizing a Sensor to Detect Ammonium Nitrate Based IEDS in Vehicles Using Artificial Neural Networks},
  journal = {American Journal of Neural Networks and Applications},
  volume = {5},
  number = {1},
  pages = {1-6},
  doi = {10.11648/j.ajnna.20190501.11},
  url = {https://doi.org/10.11648/j.ajnna.20190501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20190501.11},
  abstract = {Ammonium nitrate based explosives are a choice weapon for many terrorist groups due to its ease in manufacturing and high velocity of detonation. These explosives undergo thermal decomposition to release ammonia gas in traces of about 5- 25 Parts per Million (PPM) below the olfactory threshold. Ammonia is a reducing gas. MQ137 sensors are low cost commercially available metal oxide semiconductor ammonia gas sensors with a problem of selectivity (reacting with other reducing gasses like carbon monoxide etc) and sensitivity. We present the optimization of MQ137 metal oxide semiconductor electrochemical sensor using MATLAB, to improve its selectivity and sensitivity for accurately recognizing the characteristics of ammonia gas within specified PPM range as a sign of ammonium nitrate based explosives in vehicles. In this study, MQ137 sensor was connected with an ARDUINO microcontroller to a digital computer (2.40 GHz processor) and pre-heated for 12 hours before being exposed to ammonia gas in a controlled environment at room temperature to extract features (sensitivity constant and concentration in PPM) of ammonia gas with MQ137 sensor. 150 data samples of each feature were extracted and trained in a multilayer pattern recognition neural network with one hidden layer and 50 data samples containing features of other reducing gasses from the data sheet were used for testing. Test performance of multilayer artificial neural network has an accuracy of 100% with no misclassifications.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Optimizing a Sensor to Detect Ammonium Nitrate Based IEDS in Vehicles Using Artificial Neural Networks
AU  - Bourdillon Omijeh
AU  - Akani Okemeka Machiavelli
Y1  - 2019/06/10
PY  - 2019
N1  - https://doi.org/10.11648/j.ajnna.20190501.11
DO  - 10.11648/j.ajnna.20190501.11
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 1
EP  - 6
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20190501.11
AB  - Ammonium nitrate based explosives are a choice weapon for many terrorist groups due to its ease in manufacturing and high velocity of detonation. These explosives undergo thermal decomposition to release ammonia gas in traces of about 5- 25 Parts per Million (PPM) below the olfactory threshold. Ammonia is a reducing gas. MQ137 sensors are low cost commercially available metal oxide semiconductor ammonia gas sensors with a problem of selectivity (reacting with other reducing gasses like carbon monoxide etc) and sensitivity. We present the optimization of MQ137 metal oxide semiconductor electrochemical sensor using MATLAB, to improve its selectivity and sensitivity for accurately recognizing the characteristics of ammonia gas within specified PPM range as a sign of ammonium nitrate based explosives in vehicles. In this study, MQ137 sensor was connected with an ARDUINO microcontroller to a digital computer (2.40 GHz processor) and pre-heated for 12 hours before being exposed to ammonia gas in a controlled environment at room temperature to extract features (sensitivity constant and concentration in PPM) of ammonia gas with MQ137 sensor. 150 data samples of each feature were extracted and trained in a multilayer pattern recognition neural network with one hidden layer and 50 data samples containing features of other reducing gasses from the data sheet were used for testing. Test performance of multilayer artificial neural network has an accuracy of 100% with no misclassifications.
VL  - 5
IS  - 1
ER  -

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