The results of phylogenetic tree and Bayesian clustering analysis were supported by analysis of molecular variance and principal components analysis.
分子方差分析和主成分分析支持了系统树和贝叶斯聚类分析的结果。
The proposed method is divided into two steps: 1 license plate location by the variance of gray image, and 2 license plate segmentation by connected components analysis and projection analysis.
该系统由两个重要步骤组成：1基于灰度方差的车牌定位算法，2结合连通域分析和投影分析的字符切分算法。
Then the data were analyzed by the absolute principal component analysis (APCA) to evaluate principal components and the percent variance explained by them.
对获得的浓度数据进一步作绝对主成分分析(APCA)，获取颗粒物的主要来源成分和它们所占的比值。
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