센서학회지 (Journal of Sensor Science and Technology)

  • 제22권2호
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  • Pages.162-173
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  • 2013
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  • 1225-5475(pISSN)
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  • 2093-7563(eISSN)
한국센서학회 (The Korean Sensors Society)
권호 표지
DOI QR코드

DOI QR Code

대규모 가스 센서 어레이에서 중복도의 제거와 확률신경회로망을 이용한 분류

The Classification Using Probabilistic Neural Network and Redundancy Reduction on Very Large Scaled Chemical Gas Sensor Array

  • 김정도 (호서대학교 전자공학과) ;
  • 임승주 (호서대학교 전자공학과) ;
  • 박성대 (호서대학교 전자공학과) ;
  • 변형기 (강원대학교 정보통신공학과) ;
  • ;
  • 김정주 (호서대학교 전자공학과)
  • Kim, Jeong-Do (Department of Electronic Engineering, Hoseo University) ;
  • Lim, Seung-Ju (Department of Electronic Engineering, Hoseo University) ;
  • Park, Sung-Dae (Department of Electronic Engineering, Hoseo University) ;
  • Byun, Hyung-Gi (School of Electronic, Information and Communication Engineering, Kangwon National Unversity) ;
  • Persaud, K.C. (SCEAC University of Manchester) ;
  • Kim, Jung-Ju (Department of Electronic Engineering, Hoseo University)
  • 투고 : 2013.03.20
  • 심사 : 2013.03.26
  • 발행 : 2013.03.31
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초록

The purpose of this paper is to classify VOC gases by emulating the characteristics found in biological olfaction. For this purpose, we propose new signal processing method based a polymeric chemical sensor array consisting of 4096 sensors which is created by NEUROCHEM project. To remove unstable sensors generated in the manufacturing process of very large scaled chemical sensor array, we used discrete wavelet transformation and cosine similarity. And, to remove the supernumerary redundancy, we proposed the method of selecting candidates of representative sensor representing sensors with similar features by Fuzzy c-means algorithm. In addition, we proposed an improved algorithm for selecting representative sensors among candidates of representative sensors to better enhance classification ability. However, Classification for very large scaled sensor array has a great deal of time in process of learning because many sensors are used for learning though a redundancy is removed. Throughout experimental trials for classification, we confirmed the proposed method have an outstanding classification ability, at transient state as well as steady state.

키워드

참고문헌

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