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Open AccessArticle

Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors

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School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China
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School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
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School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada
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College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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Guizhou Tobacco Rebaking Co. LTD, Guizhou 550025, China
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Authors to whom correspondence should be addressed.
Sensors 2020, 20(3), 874; https://doi.org/10.3390/s20030874
Received: 11 November 2019 / Revised: 22 January 2020 / Accepted: 2 February 2020 / Published: 6 February 2020
(This article belongs to the Section Physical Sensors)
Near-infrared (NIR) spectral sensors can deliver the spectral response of light absorbed by materials. Data analysis technology based on NIR sensors has been a useful tool for quality identification. In this paper, an improved deep convolutional neural network (CNN) with batch normalization and MSRA (Microsoft Research Asia) initialization is proposed to discriminate the tobacco cultivation regions using data collected from NIR sensors. The network structure is created with six convolutional layers and three full connection layers, and the learning rate is controlled by exponential attenuation method. One-dimensional kernel is applied as the convolution kernel to extract features. Meanwhile, the methods of L2 regularization and dropout are used to avoid the overfitting problem, which improve the generalization ability of the network. Experimental results show that the proposed deep network structure can effectively extract the complex characteristics inside the spectrum, which proves that it has excellent recognition performance on tobacco cultivation region discrimination, and it also demonstrates that the deep CNN is more suitable for information mining and analysis of big data. View Full-Text
Keywords: NIR sensor; data analysis; convolutional neural network; cultivation region discrimination NIR sensor; data analysis; convolutional neural network; cultivation region discrimination
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Wang, D.; Tian, F.; Yang, S.X.; Zhu, Z.; Jiang, D.; Cai, B. Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors. Sensors 2020, 20, 874.

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