[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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《浙江科技学院学报》[ISSN:2097-5236/CN:33-1431/Z]

卷:
期数:
2019年05期
页码:
373-380
栏目:
出版日期:
2019-10-31

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: