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

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ID: 112412
2020
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Abstract
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.
Reference Key
cai2020sensorsimproved Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Di Wang,Fengchun Tian,Simon X. Yang,Zhiqin Zhu,Daiyu Jiang,Bin Cai;Di Wang;Fengchun Tian;Simon X. Yang;Zhiqin Zhu;Daiyu Jiang;Bin Cai;
Journal sensors
Year 2020
DOI
10.3390/s20030874
URL
https://sciprofiles.com/publication/view/aa7aa91f017d97999cf0696b5a70c6dd
Keywords
Convolutional neural network data analysis nir sensor cultivation region discrimination Convolutional neural network data analysis nir sensor cultivation region discrimination

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