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

doi:10.1117/12.2685366

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

Author Affiliations +

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.

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Near infrared spectroscopy

Convolutional neural networks

Performance modeling

Convolution

Nondestructive evaluation

Water quality

Keywords/Phrases

Search In:

Publication Years