从表2中可以看出，对指标性成分不同的光谱预处理方法得到的RMSECV和R2有显着不同，其中以Vector Normalization(矢量归一化)处理效果最好。对原始光谱进行必要的预处理之后，更能真实细致地反映指标成分的光谱信息。
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normalization vector space with single 单向规范化向量空间
The results showed that: 1) the vector normalization method and multiplication scattering correction method were suitable to setting up prediction models for leaf structure and oil, respectively;
结果表明：①矢量归一法和乘法散射校正法分别适合建立烟叶叶片结构模型和油分预测模型；
The relations between particle four current vector, normalization condition and distribution function are analysed.
通过规格化条件讨论，分析了统计分布函数与四维粒子流矢量间的关系。
Then, the processes of computing the vector values of POI objects are discussed by the methods of questionnaire survey, multi-density spatial clustering and data normalization respectively.
然后，分别讨论了利用问卷调查、多密度空间聚类和数据规格化的方法计算POI对象的各项显著性指标值的过程；
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