Abstract
The particles in the air are collected by the biomonitoring alarm, which produces fluorescence and scattered light under laser irradiation, and is converted into an electrical signal using a photoelectric sensor. Due to the complex composition of particulate matter in the air, it is difficult to classify biological particulate matter using general biological particulate matter identification. Therefore, BP neural network is selected for identification. In the environment where there is a certain concentration of biological particles, the fluorescence and scattered light voltage amplitude generated per unit of time is selected, segmented, normalized, and the characteristic curve is simulated, and the BP neural network is trained as an input. After the bio-alarm starts working, the data per unit time is selected, segmented, normalized and identified.
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Fan, J., Jiang, S., Zhang, C., Yang, Z., Jiang, T. (2021). Biological Particle Recognition Based on BP Neural Network Algorithm. In: Jain, L.C., Kountchev, R., Shi, J. (eds) 3D Imaging Technologies—Multi-dimensional Signal Processing and Deep Learning. Smart Innovation, Systems and Technologies, vol 234. Springer, Singapore. https://doi.org/10.1007/978-981-16-3391-1_25
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DOI: https://doi.org/10.1007/978-981-16-3391-1_25
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