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21 July 2023 Improved 1D convolutional neural network for near-infrared spectroscopy for fast measurement of water quality COD values
Rigao Fan, Wu Wang, Zhifang Zheng, Qinqin Chai, Qunyong Han
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Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127173C (2023) https://doi.org/10.1117/12.2685366
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China
Abstract
Chemical oxygen demand value is a key indicator for water quality detection and an important link in water environmental protection. Near infrared spectroscopy is a fast, non-destructive, and green identification method applied to chemical oxygen demand detection. Near infrared spectroscopy has the characteristics of high data dimensions, severe band stacking, and difficult feature extraction. In this paper, an improved one-dimensional convolutional neural network is proposed to analyze the near infrared spectroscopy of water samples to predict their chemical oxygen demand values. Firstly, one-dimensional convolution is used to extract deep spectral features. Secondly, SoftPool is used to reduce the dimension of spectral features to improve the problem of losing some features in traditional pooling methods. Finally, the prediction results are output through full connectivity layer integration features. The proposed method is validated on a near infrared spectral dataset of chemical oxygen demand. The experimental results show that this method can accurately and quickly analyze the chemical oxygen demand value in water.
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Rigao Fan, Wu Wang, Zhifang Zheng, Qinqin Chai, and Qunyong Han "Improved 1D convolutional neural network for near-infrared spectroscopy for fast measurement of water quality COD values", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127173C (21 July 2023); https://doi.org/10.1117/12.2685366
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KEYWORDS
Data modeling
Near infrared spectroscopy
Convolutional neural networks
Performance modeling
Convolution
Nondestructive evaluation
Water quality
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