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时间序列三维荧光光谱的数据压缩
引用本文:于绍慧,肖雪,徐格.时间序列三维荧光光谱的数据压缩[J].光谱学与光谱分析,2017,37(4):1163-1167.
作者姓名:于绍慧  肖雪  徐格
作者单位:1. 合肥师范学院数学与统计学院,安徽 合肥 230601
2. 中国科学院安徽光学精密机械研究所环境光学与技术重点实验室,安徽 合肥 230031
基金项目:国家自然科学基金项目,安徽省自然科学基金项目,国家自然科学基金委员会国际(地区)合作与交流项目
摘    要:时间序列三维荧光光谱数据量大和信息丰富的特点虽然有利于有机物的定性和定量分析,但是大量冗余信息的存在增加了计算的复杂度和计算量。在分析了时间序列三维荧光光谱的时频特征后,分别利用聚类分析和二维小波变换从时间维和光谱维对三维荧光光谱进行了压缩。在聚类分析中探讨了样本距离、类间距离、复合相关系数和R平方统计量等关键因素。相关系数和R平方统计量的结合不仅提高了聚类分析的精确度,而且减少了二维小波在光谱维进行数据压缩的工作量。相关的数值实验表明压缩后的数据保留了原有时间序列三维荧光光谱的重要信息。

关 键 词:三维荧光光谱  聚类分析  小波分析  数据压缩  
收稿时间:2015-07-29

Data Compression of Time Series Three-Dimensional Fluorescence Spectroscopy
YU Shao-hui,XIAO Xue,XU Ge.Data Compression of Time Series Three-Dimensional Fluorescence Spectroscopy[J].Spectroscopy and Spectral Analysis,2017,37(4):1163-1167.
Authors:YU Shao-hui  XIAO Xue  XU Ge
Institution:1. School of Mathematics and Statistics, Hefei Normal University, Hefei 230601, China 2. Key Laboratory of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
Abstract:There has been abundant data saved in time serie s three-dimensional fluorescence spectroscopy,which is helpful to the qualitat ive and quantitative analysis of organic matter.However,redundant information also complicates the analysis and decreases the computation efficiency.Based on time-frequency of time series three-dimensional fluorescence spectroscopy,th ree-dimensional fluorescence spectroscopy is compressed with cluster analysis a nd 2-D wavelet transform.Some key factors,such as sample distance,inter-cal ss distance,composite correlation coefficient and R-square stastic,are discus sed.The introductions of correlation coefficient and R-square statistic not on ly improve the precision of cluster analysis but also reduce the data for 2-D w avelet transform.Experiment results show that the important information in the original data is still kept in the compressed time series three-dimensional flu orescence spectroscopy.
Keywords:Three-dimensional fluorescence spectroscopy  C luster analysis  Wavelet analysis  Data compression
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