A fast quantitative identification algorithm of colorimetric visual-sensor-array based on basic units matching
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Abstract:
There are many difficulties to process a colorimetric visual-sensor-array by traditional processing methods, such as complicated manual analysis due to many data and hard to finish varieties and concentrations identification simultaneously, etc. In view of colorimetric-sensor-array’s same location response to the same gas, a fast quantitative identification algorithm of colorimetric visual-sensor-array based on basic units matching which can solve these problems is proposed. First, denoising and feature extraction are processed by setting experienced threshold to reduce redundancies and lessen manual analysis. Second, a creative qualitative analysis method based on basic units is put forward, which not only reduces computation, but also increases efficiency and precision. Finally, a ANFIS of NH3 concentration recognition utilizing advantages of fuzzy logic and neural network is used to distinguish low concentration NH3. The advantage of this algorithm is that varieties and concentrations of different gases could be detected successively, solving the problem of recognition errors caused by characteristic data infection when varieties and concentrations of different gases are detected simultaneously.The results of template matching based on basic units show that the classification accuracy of NH3, Cl2 and SO2 are 100%. The low concentration NH3 classification accuracy is also very high after species identification with measurement errors below 5%.
