TY - GEN
T1 - Detecting Gas Vapor Leaks through Uncalibrated Sensor Based CPS
AU - Badawi, Diaa
AU - Ozev, Sule
AU - Christen, Jennifer Blain
AU - Yang, Chengmo
AU - Orailoglu, Alex
AU - Cetin, A. Enis
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - While Volatile Organic Compounds (VOC) and ammonia have a place in our daily lives, their leakage into the environment is harmful to human health. In order to prevent and detect gaseous leaks of harmful VOCs, a cyber-physical system (CPS) comprised of ordinary people or first responders is proposed. This CPS uses small, low-cost sensors coupled to smart phones or mobile devices with the necessary computation and communication capabilities. The efficacy of such a CPS hinges on its ability to address technical challenges stemming from the fact that identically produced sensors may produce different results under the same conditions due to sensor drift, noise, or resolution errors.The proposed system makes use of time-varying signals produced by sensors to detect gas leaks. Sensors sample the gas vapor level in a continuous manner and time-varying sensor data is processed using deep neural networks. One of the neural networks (NN) is an energy efficient Additive Neural Network (AddNet) which can be implemented in host devices. The second NN is the discriminator of a GAN and the third a regular convolutional NN. AddNet produces comparable VOC gas leak detection results to regular convolutional networks while reducing area requirements by two thirds.
AB - While Volatile Organic Compounds (VOC) and ammonia have a place in our daily lives, their leakage into the environment is harmful to human health. In order to prevent and detect gaseous leaks of harmful VOCs, a cyber-physical system (CPS) comprised of ordinary people or first responders is proposed. This CPS uses small, low-cost sensors coupled to smart phones or mobile devices with the necessary computation and communication capabilities. The efficacy of such a CPS hinges on its ability to address technical challenges stemming from the fact that identically produced sensors may produce different results under the same conditions due to sensor drift, noise, or resolution errors.The proposed system makes use of time-varying signals produced by sensors to detect gas leaks. Sensors sample the gas vapor level in a continuous manner and time-varying sensor data is processed using deep neural networks. One of the neural networks (NN) is an energy efficient Additive Neural Network (AddNet) which can be implemented in host devices. The second NN is the discriminator of a GAN and the third a regular convolutional NN. AddNet produces comparable VOC gas leak detection results to regular convolutional networks while reducing area requirements by two thirds.
KW - VOC gas leak detection
KW - additive
KW - and generative adversarial (GAN) neural networks
KW - convolutional
KW - sensor drift
UR - http://www.scopus.com/inward/record.url?scp=85068955330&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068955330&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2019.8682204
DO - 10.1109/ICASSP.2019.8682204
M3 - Conference contribution
AN - SCOPUS:85068955330
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 8296
EP - 8300
BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Y2 - 12 May 2019 through 17 May 2019
ER -