• Abstract
• Full-Text PDF [860 KB]
• Full-Text XML
• Article Versions Notes

• Article Statistics
• PubMed/Medline
• Google Scholar
• Order Reprints

• Cite this article
• Export to BibTeX
• Export to EndNote

• [+] on DOAJ
• Kim, E
• Lee, S
• Kim, JHun
• Kim, C
• Byun, YTae
• Kim, HSeok
• Lee, T
• [+] on Google Scholar
• Kim, E
• Lee, S
• Kim, JHun
• Kim, C
• Byun, YTae
• Kim, HSeok
• Lee, T
• [+] on PubMed
• Kim, E
• Lee, S
• Kim, JHun
• Kim, C
• Byun, YTae
• Kim, HSeok
• Lee, T

•  CiteULike
•  Facebook
•  Mendeley
•  Twitter

This article is

• freely available
• re-usable

Sensors 2012, 12(12), 16262-16273; doi:10.3390/s121216262

Abstract

Pattern Recognition for Selective Odor Detection with Gas Sensor Arrays

Eungyeong Kim ¹ , Seok Lee ¹ , Jae Hun Kim ¹ , Chulki Kim ¹ , Young Tae Byun ¹ , Hyung Seok Kim ²  and Taikjin Lee ^1,*

¹ Environment Sensor System Research Center, Korea Institute of Science and Technology (KIST), Hwarangno 14-gil 5, Seongbuk-Gu, Seoul 136-791, Korea ² Department of Information and Communication Engineering, Sejong University, 98 Gunja-Dong, Gwangjin-Gu, Seoul 143-747, Korea

* Author to whom correspondence should be addressed.

Received: 20 September 2012; in revised form: 15 November 2012 / Accepted: 19 November 2012 / Published: 23 November 2012

(This article belongs to the Special Issue Odor Detection: Electronic Nose, Olfactometer, and Advanced Instrumentation)

Download PDF Full-Text [860 KB, Updated Version, uploaded 30 November 2012 15:13 CET]

The original version is still available [815 KB, uploaded 23 November 2012 11:34 CET]

Abstract: This paper presents a new pattern recognition approach for enhancing the selectivity of gas sensor arrays for clustering intelligent odor detection. The aim of this approach was to accurately classify an odor using pattern recognition in order to enhance the selectivity of gas sensor arrays. This was achieved using an odor monitoring system with a newly developed neural-genetic classification algorithm (NGCA). The system shows the enhancement in the sensitivity of the detected gas. Experiments showed that the proposed NGCA delivered better performance than the previous genetic algorithm (GA) and artificial neural networks (ANN) methods. We also used PCA for data visualization. Our proposed system can enhance the reproducibility, reliability, and selectivity of odor sensor output, so it is expected to be applicable to diverse environmental problems including air pollution, and monitor the air quality of clean-air required buildings such as a kindergartens and hospitals.

Keywords: gas sensor array; odor monitoring; pattern recognition; artificial neural networks (ANN); genetic algorithm (GA); neural-genetic classification algorithm (NGCA)

Article Statistics

Cite This Article