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

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.

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