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1)  principal component analysis
主组分分析
1.
Research on damage localization of principal component analysis-based probabilistic neural network;
基于主组分分析的概率神经网络损伤定位研究
2.
In this study, a principal component analysis (PCA) method is presented to deal with data for feature vectors reduction, and a PNN is applied to the damage location.
针对PNN在有噪声情况下进行结构损伤检测时输入矢量数目大,匹配模式多,训练时间长的缺点,提出了运用主组分分析法来处理数据的概率神经网络损伤定位方法。
3.
Using principal component analysis in chemometrics,a distribution feature of the nutritive elements was discussed.
运用化学计量学中的主组分分析方法 ,讨论了这些营养元素分布特征 。
2)  grouping PCA
分组主成分分析
3)  Principal Components Analysis(PCA)
主组分分析(PCA)
4)  composition analysis
组分分析
1.
The composition of NiAl alloys have great effects on the structure and phase transformation temperatures, but the composition analysis is often disturbed by the difficulties in the separation of Ni and Al.
NiAl合金的组分对其结构和相变温度有着重要的影响 ,然而其组分分析受Ni、Al难以分离的困挠 。
2.
A new composition analysis method was developed based on the gas chro-matograph and the characteristics of binary hydrofluorocarbon (HFC) mixtures, and was used to analyze nine binary hydrofluorocarbon mixtures related to the mass fraction composition and the ratio of peak area.
针对氢氟烃二元混合物的特点,本文搭建了气相色谱法混合工质组分分析实验台,测定了9种氢氟烃二元混合物组元质量分数与峰面积百分比的对应关系。
5)  component analysis
组分分析
1.
Extraction and component analysis of polysaccharide of Fructus Trichosanthis;
瓜蒌多糖的提取及组分分析
2.
Attractive activity to natural enemies and component analysis of the rinses from tea aphid body surface;
茶蚜体表漂洗物对天敌的引诱活性及组分分析
3.
Enlightened by the independent component analysis (ICA),the authors presented a novel approach named component analysis based on kurtosis(CABK) to identify the overlapped peaks.
本实验提出了基于峭度的组分分析 (componentanalysisbasedonkurtosis,CABK)方法来解析重叠峰 ,从中分离出纯组分信息。
6)  components analysis
组分分析
补充资料:痕量组分分析
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性质:又称痕量组分分析。按试样中欲测组分相对含量划分的一种分析方法。一般指组分相对含量小于0.01%的分析称为痕量分析。通常痕量分析是指试样中痕量元素总量的测定,不包括化学形态分析和微区分析。对于纯金属、超纯物质的杂质含量、生物体及环境保护样品中某些有害物质的测定,常属于痕量分析范畴。若其相对含量小于10-6(旧称ppm)时,则称它为超痕量分析(ultratrace analysis)。应注意的是此类划分界限并不严格,随着测定方法的改进、测试仪器灵敏度的提高,其检测含量会更低。

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