Browse > Article
http://dx.doi.org/10.7471/ikeee.2018.22.3.700

Design of Gas Classifier Based On Artificial Neural Network  

Jeong, Woojae (School of Electronics and Information Engineering, Korea Aerospace University)
Kim, Minwoo (School of Electronics and Information Engineering, Korea Aerospace University)
Cho, Jaechan (School of Electronics and Information Engineering, Korea Aerospace University)
Jung, Yunho (School of Electronics and Information Engineering, Korea Aerospace University)
Publication Information
Journal of IKEEE / v.22, no.3, 2018 , pp. 700-705 More about this Journal
Abstract
In this paper, we propose the gas classifier based on restricted column energy neural network (RCE-NN) and present its hardware implementation results for real-time learning and classification. Since RCE-NN has a flexible network architecture with real-time learning process, it is suitable for gas classification applications. The proposed gas classifier showed 99.2% classification accuracy for the UCI gas dataset and was implemented with 26,702 logic elements with Intel-Altera cyclone IV FPGA. In addition, it was verified with FPGA test system at an operating frequency of 63MHz.
Keywords
machine learning; artificial neural network; RCE-NN; gas classification; FPGA;
Citations & Related Records
연도 인용수 순위
  • Reference
1 G.Dong, M.Xie, "Color Clustering and Learning for Image Segmentation Based on Neural Networks," IEEE Transactions on Neural Network, vol.16, ISSUE 4, pp. 925-936, July. 2005. DOI:10.1109/TNN.2005.849822   DOI
2 Alexander Vergara, "UCI Machine Learning Repository," https://archive.ics.uci.edu/ml/datasets/gas+sensor+array+drift+dataset
3 Alexander Vergara, Shankar Vembu, Tuba Ayhan, Margaret A.Ryan, Margie L. Homer, Ramon Huerta, "Chemical Gas Sensor Drift Compensation Using Classifier Ensembles," Sensors and Actuators B: Chemical, Vol. 166-17, No. 20, pp. 320-329, May 2012. DOI:10.1016/j.snb.2012.01.074   DOI
4 Kun Wang, Wenbin Ye, Xiaojin Zhao, Xiaofang Pan, "A Support Vector Machine-Based Genetic Algorithm Method for Gas Classification," in Proc. of the 2017 2nd international Conference on Frontiers of Sensors Technologies, 2017, pp. 363-366. DOI:10.1109/ICFST.2017.8210537   DOI
5 Duk-Dong Lee, Dae-Sik Lee, "Environmental Gas Sensors," IEEE SENSORS, vol.1, no.3, pp. 214-224, October. 2011. DOI:10.1109/JSEN.2001.954834
6 Chengxiang Wang, Longwei Yin, Luyuan Zhang, Dong Xiang, Rui Gao, "Metal Oxide Gas Sensors: Sensitivity and Influencing Factors," Sensors, vol.10, pp. 2088-2106, March. 2010. DOI:10.3390/s100302088   DOI
7 Xiaojun Zhai, Amine Ait Si Ali, Abbes Amira, Faycal Bensaali, "MLP Neural Network Based Gas Classification System on Zynq SoC," IEEE Access, vol.4, pp. 8138-8146, October. 2016. DOI:10.1109/ACCESS.2016.2619181   DOI
8 F. Benrekia, M. Attari, M. Bouhedda, "Gas sensors characterization and multilayer perceptron (MLP) hardware implementation for gas identification using a field programmable gate array (FPGA)," Sensors, vol.13, no.3, pp. 2967-2985, March, 2013. 10.3390/s130302967   DOI
9 Pai Peng, Xiaojin Zhao, Xiaofang Pan, Wenbin Ye, "Gas Classification Using Deep Convolutional Neural Networks," Sensors, vol.18, no.1, pp. 1-11, January, 2018. DOI:10.3390/s18010157   DOI