title: Pattern recognition for selective odor detection with gas sensor arrays.
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.
authors: Environment Sensor System Research Center, Korea Institute of Science and Technology, Hwarangno 14-gil 5, Seongbuk-Gu, Seoul 136-791, Korea. eungyeong@kist.re.kr
authors: Eungyeong
authors: E
authors: Kim
authors: Seok
authors: S
authors: Lee
authors: Jae Hun
authors: JH
authors: Kim
authors: Chulki
authors: C
authors: Kim
authors: Young Tae
authors: YT
authors: Byun
authors: Hyung Seok
authors: HS
authors: Kim
authors: Taikjin
authors: T
authors: Lee
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D005740:Gases / Q000032:analysis* / Q000145:classification / Q000302:isolation & purification*; D006801:Humans; D016571:Neural Networks, Computer; D009812:Odorants / Q000032:analysis*
chemical_list: D005740:Gases