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三维荧光光谱的特征区域选择方法 被引量:8

Characteristic Region Selection Methods for Three-dimensional Fluorescence Spectrometry
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摘要 将数学中的二元凸函数判定和数据挖掘中的聚类分析方法结合,提出了针对三维荧光的光谱区域选择方法,并利用此种方法从光谱图中提取出含有丰富光谱信息的凸集区域。对水体中总有机碳的检测和白酒中黄曲霉素的检测进行了实验研究,实验结果表明,采用本文提出的三维荧光光谱区域选择方法提高了模型的精度,与利用全光谱所建立的回归模型相比,模型精度分别提高了6.17%和4.97%。 Based on combination of binary convex function discriminant theorem with clustering analysis,a new method of characteristic region selection for three-dimensional fluorescence spectrometry is proposed.By this method,the convex regions with valid spectral data are obtained.Experiment for detecting total organic carbon(TOC) in water and AFB1 in liquor are carried out.The experimental results show that the proposed method improves the accuracy of the regress model with increase of 6.17% and 4.97% respectively.
出处 《发光学报》 EI CAS CSCD 北大核心 2012年第3期341-345,共5页 Chinese Journal of Luminescence
基金 国家自然科学基金(60974111) 国家"863"计划(2009AA04Z123)资助项目
关键词 三维荧光 特征光谱区域选择 二元凸函数判别 聚类分析 three-dimensional fluorescence spectrometry characteristic regions selection binary convex function cluster analysis
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  • 1鄢远,王乐天,许金钩,林竹光,陈国珍.三维导数荧光光谱总体积积分法同时测定多环芳烃[J].化学学报,1996,54(8):772-776. 被引量:7
  • 2欧阳二明,张锡辉,王伟.城市水体有机污染类型的三维荧光光谱分析法[J].水资源保护,2007,23(3):56-59. 被引量:39
  • 3同济大学数学教研室主编.高等数学[M].高等教育出版社,2002.
  • 4Jain A K,Murty M N,Flynn P J.Data clustering:a review[J].ACM Computing Surveys,1999,31(3):264-323.
  • 5Han J W,Kambr M.Datamining concepts and techniques[M].Beijing:Higher Education Press,2001:145-176.
  • 6Rakesh A,Johanners G,Dimitrios G,et al.Automatic subspace clustering of high dimensional data for data mining applications[C]// Proc of ACM SIGMOD Int'l Conf on Management of Data.Minneapolis:ACM Press,1994:94-105.
  • 7Ester M,Krigel H P,Sander J,et al.A density-based algorithm for discovering clusters in large spatial databases with noise[C]//Proc of the 2nd International Conference on Knowledge Discovery and-Data Mining,Portland,WA,1996:226-231.
  • 8Hinneburg A,Keim D A.An efficient approach to clustering in multimedia databases with noise[C]//Proc of the 4th Intemational Conference on Knowledge Discovery and Data Mining,New York,1998:58-65.
  • 9Ankerst M,Breunig M M,Kriegel H P,et al.OPTICS:Ordering points to identify the clustering structure[C]//Proc of the ACM SIGMOD'99 International Conference on Management of Data,Philadelphia,PA,1999:49-60.
  • 10Karypis G,Han E H,Kumar V.Chameleon:A hierarchical clustering algorithm using dynamic modeling[J].IEEE Computer,1999,32(8):68-75.

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