Kim, Eungyeong Lee, Seok Kim, Jae Hun Kim, Chulki Byun, Young Tae Kim, Hyung Seok Lee, Taikjin 2024-01-20T13:31:26Z 2024-01-20T13:31:26Z 2021-09-05 2012-12 1424-8220 https://pubs.kist.re.kr/handle/201004/128588 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. English MDPI ELECTRONIC NOSE DISCRIMINATION PERFORMANCE AIR Pattern Recognition for Selective Odor Detection with Gas Sensor Arrays Article 10.3390/s121216262 1 SENSORS, v.12, no.12, pp.16262 - 16273 SENSORS 12 12 16262 16273 scie scopus 000312607500016 2-s2.0-84871691490 Chemistry, Analytical Engineering, Electrical & Electronic Instruments & Instrumentation Chemistry Engineering Instruments & Instrumentation Article ELECTRONIC NOSE DISCRIMINATION PERFORMANCE AIR gas sensor array odor monitoring pattern recognition artificial neural networks (ANN) genetic algorithm (GA) neural-genetic classification algorithm (NGCA)