생물학적 후각 시스템을 모방한 대규모 가스 센서 어레이에서 코사인 유사도와 퍼지 클러스터링을 이용한 중복도 제거 방법
The Redundancy Reduction Using Fuzzy C-means Clustering and Cosine Similarity on a Very Large Gas Sensor Array for Mimicking Biological Olfaction
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초록
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.
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.
참고문헌 (11)
- 송미혜, "웨이브렛과 신경망 기반의 심실 세동 검출 알고리즘에 관한 연구", 전기학회논문지 D권, 53(11-D), pp.780~785 (2004), 대한전기학회
- 신승원, "이동형 심전도 신호의 잡음 제거 및 유사도 평가", 전기학회논문지ABCD, 57(3), pp.507~513 (2008), 대한전기학회
- M. H. Song, "SVM classifier for the detection of ventricular fibrillation", IEEK, 42(5) (2005)
- A. C. Douglas, "An approach to cardiac arrhythmia analysis using hidden Markov models", IEEE Transactions on Biomedical Engineering, 37(9), pp.826~836 (1990)
- A. Pachauri, "Wavelet and energy based approach for PVC detection", International Conference on Emerging Trends in Electronic and Photonic Devices and Systems, pp.258~261 (2009)
- S. R. R. Alfredo, "Automatic detection of arrhythmias using wavelets and self-organized artificial", International Conference on Intelligent Systems Design and Applications, pp.648~653 (2009)
- J. A. Nasiri, "Intelligent arrhythmia detection using genetic algorithm and emphatic SVM(ESVM)", UK Sim European Symposium on Computer Modeling and Simulation, pp.112~117 (2009)
- N. Srinivasan, "A new phase space analysis algorithm for cardiac arrhythmia detection", International Conference of the IEEE EMBS, pp.17~21 (2003)
- 배종수, "Image Tracking Algorithm using Template Matching and PSNF-m", International Journal of Control, Automation, and Systems, 6(3), pp.413~423 (2008), 제어·로봇·시스템학회
- H. Sakoe, "Dynamic programming alogrithm optimization for spoken word recognition", IEEE Trans. on ASSP, 26(1), pp.43~49 (1978)
- R. Beccherelli, "Design of a very large chemical sensor system for mimicking biological olfaction", Sensors and Actuators, B, 146, pp.446~452 (2010)