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The Redundancy Reduction Using Fuzzy C-means Clustering and Cosine Similarity on a Very Large Gas Sensor Array for Mimicking Biological Olfaction

생물학적 후각 시스템을 모방한 대규모 가스 센서 어레이에서 코사인 유사도와 퍼지 클러스터링을 이용한 중복도 제거 방법

  • Kim, Jeong-Do (Department of Electronic Engineering, Hoseo University) ;
  • Kim, Jung-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 University) ;
  • Persaud, K.C. (SCEAC University of Manchester) ;
  • Lim, Seung-Ju (Department of Electronic Engineering, Hoseo University)
  • 김정도 (호서대학교 전자공학과) ;
  • 김정주 (호서대학교 전자공학과) ;
  • 박성대 (호서대학교 전자공학과) ;
  • 변형기 (강원대학교 정보통신공학과) ;
  • ;
  • 임승주 (호서대학교 전자공학과)
  • Received : 2011.11.03
  • Accepted : 2012.01.02
  • Published : 2012.01.24

Abstract

It was reported that the latest sensor technology allow an 65536 conductive polymer sensor array to be made with broad but overlapping selectivity to different families of chemicals emulating the characteristics found in biological olfaction. However, the supernumerary redundancy always accompanies great error and risk as well as an inordinate amount of computation time and local minima in signal processing, e.g. neural networks. In this paper, we propose a new method to reduce the number of sensor for analysis by reducing redundancy between sensors and by removing unstable sensors using the cosine similarity method and to decide on representative sensor using FCM(Fuzzy C-Means) algorithm. The representative sensors can be just used in analyzing. And, we introduce DWT(Discrete Wavelet Transform) for data compression in the time domain as preprocessing. Throughout experimental trials, we have done a comparative analysis between gas sensor data with and without reduced redundancy. The possibility and superiority of the proposed methods are confirmed through experiments.

Keywords

References

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Cited by

  1. The Classification Using Probabilistic Neural Network and Redundancy Reduction on Very Large Scaled Chemical Gas Sensor Array vol.22, pp.2, 2013, https://doi.org/10.5369/JSST.2013.22.2.162