电子测量与仪器学报

赵磊,王远,周南,贾培兴.太赫兹光谱识别木材的影响因素研究[J].电子测量与仪器学报,2021,35(5):161-167

太赫兹光谱识别木材的影响因素研究

Research on influencing factors of wood identification by terahertz spectroscopy

DOI：

英文关键词:wood identification terahertz time-domain spectroscopy radial variation different sections BP neural network

基金项目:中央高校基本科研业务费专项（2016ZCQ08）资助

作者	单位
赵磊	1. 北京林业大学工学院,2. 林业装备与自动化国家林业和草原局重点实验室
王远	1. 北京林业大学工学院,2. 林业装备与自动化国家林业和草原局重点实验室
周南	3. 天津大学精密测试技术及仪器国家重点实验室
贾培兴	1. 北京林业大学工学院,2. 林业装备与自动化国家林业和草原局重点实验室

Author	Institution
Zhao Lei	1. School of Technology, Beijing Forestry University,2. Key Lab of State Forestry and Grassland Administration for Forestry Equipment and Automation
Wang Yuan	1. School of Technology, Beijing Forestry University,2. Key Lab of State Forestry and Grassland Administration for Forestry Equipment and Automation
Zhou Nan	3. State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University
Jia Peixing	1. School of Technology, Beijing Forestry University,2. Key Lab of State Forestry and Grassland Administration for Forestry Equipment and Automation

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中文摘要:

径向变异和不同切面是影响太赫兹光谱识别木材的重要因素。为了探讨径向变异和不同切面对太赫兹光谱识别木材的影响,利用太赫兹时域光谱技术获取杉木和柳杉样品的太赫兹光谱数据,共 600 组木材光谱数据,分析太赫兹光谱,基于 BP 神经网络建立木材识别模型,比较模型预测的正确率。发现木材样品太赫兹光谱因为径向变异和不同切面而存在差异,预测样品与建模样品径向部位相同时和不同时的模型预测正确率差异较小,最高正确率达到 96. 25%,预测样品与建模样品切面相同时和不同时的正确率差异较大。结果表明,基于太赫兹时域光谱技术能够准确实现木材识别,径向变异对木材识别影响较小, 不同切面对木材识别影响较大。

英文摘要:

Radial variation and different sections are essential factors affecting the identification of wood by terahertz spectroscopy. To explore the influence of radial deviation and different facets on terahertz spectrum identification of timber, terahertz time-domain spectroscopy technology was used to obtain terahertz spectrum data of Cunninghamia lanceolata and Cryptomeria fortunei samples, a total of 600 sets of wood spectrum data, and then the terahertz spectrum were analyzed. A wood recognition model was established based on BP neural network. The accuracy of model prediction was compared. It was found that the terahertz spectra of wood samples were different due to radial variation and other sections. The prediction accuracy of the model with the same or different radial positions between the predicted sample and the modeling sample had little difference, and the highest accuracy was 96. 25%. When the expected sample and the modeled sample belong to the same and different sections, the difference in accuracy is massive. The results show that the technology based on terahertz time-domain spectroscopy can accurately realize wood recognition, radial variation has little effect on wood recognition, and different sections have a more significant impact on wood recognition.

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