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基于卷积神经网络的红外光谱建模分析综述

王堃 史勇 刘池池 谢义 蔡萍 孔松涛

王堃, 史勇, 刘池池, 谢义, 蔡萍, 孔松涛. 基于卷积神经网络的红外光谱建模分析综述[J]. 红外技术, 2021, 43(8): 757-765.
引用本文: 王堃, 史勇, 刘池池, 谢义, 蔡萍, 孔松涛. 基于卷积神经网络的红外光谱建模分析综述[J]. 红外技术, 2021, 43(8): 757-765.
WANG Kun, SHI Yong, LIU Chichi, XIE Yi, CAI Ping, KONG Songtao. A Review of Infrared Spectrum Modeling Based on Convolutional Neural Networks[J]. Infrared Technology , 2021, 43(8): 757-765.
Citation: WANG Kun, SHI Yong, LIU Chichi, XIE Yi, CAI Ping, KONG Songtao. A Review of Infrared Spectrum Modeling Based on Convolutional Neural Networks[J]. Infrared Technology , 2021, 43(8): 757-765.

基于卷积神经网络的红外光谱建模分析综述

详细信息
    作者简介:

    王堃(1980-),男,博士研究生,主要研究方向为传热反问题,E-mail:3938630@qq.com

    通讯作者:

    孔松涛(1969-),男,四川人,教授,研究生导师,博士。研究方向:流体流动与传热、工业大数据分析及钻井与石油装备,E-mail:kst@tom.com

  • 中图分类号: O657.33

A Review of Infrared Spectrum Modeling Based on Convolutional Neural Networks

  • 摘要: 红外光谱技术存在着数据预处理复杂、预测精度不高,且难以处理大量非线性数据的问题,适于用卷积神经网络进行处理。本文首先分析了卷积神经网络应用在红外光谱上的优点,并对卷积神经网络结构组成进行简单的概述。然后针对卷积神经网络在光谱分析建模中的输入数据维度问题进行详细阐述;针对模型设计中卷积核参数的影响、多任务处理模型以及训练过程中的优化方法进行综述。最后分析了该研究的优点与不足,并展望了未来的发展趋势。
  • 图  1  LeNet-5网络模型示意图[19]

    Figure  1.  Schematic diagram of LeNet-5 network model

    图  2  全连接层示意图

    Figure  2.  Diagram of full connection layer

    图  3  光谱数据编码[38]

    Figure  3.  Spectral data coding

    图  4  维卷积核提取原始红外光谱局部特征模式图[46]

    Figure  4.  One dimensional convolution kernel extraction of original IR local feature pattern

    图  5  不同卷积核尺寸的NIR-CNN模型判别结果[49]

    Figure  5.  The discrimination results of NIR-CNN model with different convolution kernel sizes

    图  6  多任务网络的体系结构[41]

    Figure  6.  The architecture of a multitasking network

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出版历程
  • 收稿日期:  2020-08-16
  • 修回日期:  2020-10-26
  • 刊出日期:  2021-08-20

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