[1]孙淼,Guindo Mahamed Lamine,庄振华,等. 基于LIBS技术和卷积神经网络的土壤铅含量等级快速分类[J].浙江科技学院学报,2019,(05):373-380.
 SUN Miao,Guindo Mahamed Lamine,ZHUANG Zhenhua,et al. On fast classification of lead(Pb) levels in soil based on LIBS technology and convolutional neural network[J].,2019,(05):373-380.
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 基于LIBS技术和卷积神经网络的土壤铅含量等级快速分类()
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《浙江科技学院学报》[ISSN:1001-3733/CN:61-1062/R]

卷:
期数:
2019年05期
页码:
373-380
栏目:
出版日期:
2019-10-29

文章信息/Info

Title:
 On fast classification of lead(Pb) levels in soil based on LIBS technology and convolutional neural network
文章编号:
1671-8798(2019)05-0373-08
作者:
 孙淼Guindo Mahamed Lamine庄振华仲海鹏赵芸
 浙江科技学院 信息与电子工程学院,杭州 310023
Author(s):
 SUN Miao Guindo Mahamed Lamine ZHUANG Zhenhua ZHONG Haipeng ZHAO Yun
 School of information and electronic engineering, Zhejiang University of Science and Technology, Hangzhou 310023, Zhejiang, China
关键词:
 激光诱导光谱技术卷积神经网络快速分类土壤修复
分类号:
TP183;S153.6
文献标志码:
A
摘要:
 将激光诱导击穿光谱(LIBS)技术与卷积神经网络(CNN)相结合用于土壤中铅(Pb)含量的分类研究,对5类不同污染程度的样本进行了分类试验。结果表明LIBSCNN方法可以实现土壤中Pb质量浓度等级的快速准确分类,准确度达到99%以上。相较于常用的化学方法,LIBS技术可以原位快速地对待测样品进行检测,样品预处理简单。因此LIBS CNN方法可以为后期土壤修复技术提供更加准确有效的数据,且节约大量的时间及成本,提高检测分辨率。

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备注/Memo

备注/Memo:
收稿日期: 2019-03-13基金项目: 国家自然科学基金项目(61605173);浙江省自然科学基金项目(LY16C130003)通信作者: 赵芸(1981—),女,浙江省杭州人,副教授,博士,主要从事信息工程与人工智能研究。
更新日期/Last Update: