Paper Info
Title
Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors.
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.
Year
DOI
Venue
2020
10.3390/s20030874
SENSORS
Keywords
Field
DocType
NIR sensor,data analysis,convolutional neural network,cultivation region discrimination
Kernel (linear algebra),Normalization (statistics),Pattern recognition,Convolutional neural network,Electronic engineering,Regularization (mathematics),Artificial intelligence,Engineering,Overfitting,Initialization,Kernel (image processing),Big data
Journal
Volume
Issue
ISSN
20
3
1424-8220
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Di Wang11337143.48
Fengchun Tian22710.47
Simon X. Yang31029124.34
Zhiqin Zhu412814.67
Daiyu Jiang500.34
Bin Cai600.34