主分量分析(PCA)在动物分类学中的运用

就主量分析(PCA)的基本原理、 运用发展过程及在动物分类学上的应用作了 阐明。并以亚洲疣猴类5个属为例, 利用其面颅和颅骨的6项变量进行分析, 且 叙述了PCA的主要计算过程。 结果表明, 在分类过程中, 主要是面颅的凸度和宽 度结构起贡献作用。图5参21

对-氯代苯胺(PCA)在填充床生物反应器中的降解作用

以氯代苯胺（ＰＣＡ）为选择基质，用驯化技术从降解对二氯苯（ｐＤＣＢ）的富集培养物中得到了以同化ＰＣＡ为唯一碳源和氮源的混合微生物．将这种固定在填充床反应器中的微生物用于ＰＣＡ的降解作用研究中．在该反应器里，ＰＣＡ的生物降解遵循Ｌｏｇｉｓｔｉｃ方程ｑ＝ｑｍａｘ／（１＋ｅα－βＵｖ）．由方程求出了主要的动力学常数，Ｋｓ（半速率常数）和ｑｍａｘ（最大比基质降解速率）．于ＰＣＡ降解的同时，释放氯离子到培养基中．在水力停留时间３ｈ，进水ＰＣＡ浓度为３６０ｍｇ·Ｌ－１情况下，基质的体积降解率达到１２５ｍｇ·Ｌ－１

Face recognition based on PCA and multi-degree of freedom neurons

An algorithm of PCA face recognition based on Multi-degree of Freedom Neurons theory is proposed, which based on the sample sets' topological character in the feature space which is different from "classification". Compare with the traditional PCA+NN algorithm, experiments prove its efficiency.

PCA在语音检测中的应用研究

提出主元分析PCA(Principal Component Analysis)用于语音检测的方法研究．用主元分析法在多维空间中建立坐标轴，将待处理信号投影到该坐标轴中，通过分析投影结果判断是否为语音信号．通过将语音和非语音分别建立子空间，来区分语音和非语音信号．该方法不同于常规的语音时域、频域处理方法，而是在多维空间中对信号进行分析·实验结果表明，该方法准确率高、简单、容易实现，而且能区分多种非语音信号．

基于仿生模式识别和PCA/ICA的DOA估计方法

本文提出了一种基于仿生模式识别和PCA/ICA的DOA估计方法.这种方法的建模过程是用在实际环境下采集的训练样本构造人工神经网络模型,对环境的适应能力较强;且这种方法采用PCA/ICA进行特征提取,使数据得到有效压缩,可以实现系统实时处理.实验结果表明:在信噪比为20dB和0dB时,该方法的正确估计率可达100%;在信噪比降为-20dB时,该方法仍有83%的可识别率.

一种新的基于PCA变换多源遥感影像像素级融合方法

在多源遥感影像融合中,基于传统PCA变换的多源遥感影像融合的光谱分辨率受到较大影响。提出了一种新的基于PCA变换的多源遥感影像像素级融合方法,通过在传统PCA变换融合算法基础上引入小波变换融合,保留了多波段遥感图像光谱特性的有用信息,进一步提高融合后遥感影像的效果。给出实验的融合结果,并与传统PCA变换方法进行对比,证明了该方法的有效性。

Comparison of nonlinear and linear PCA on surface wind, surface height, and SST in the South China Sea

We compared nonlinear principal component analysis (NLPCA) with linear principal component analysis (LPCA) with the data of sea surface wind anomalies (SWA), surface height anomalies (SSHA), and sea surface temperature anomalies (SSTA), taken in the South China Sea (SCS) between 1993 and 2003. The SCS monthly data for SWA, SSHA and SSTA (i.e., the anomalies with climatological seasonal cycle removed) were pre-filtered by LPCA, with only three leading modes retained. The first three modes of SWA, SSHA, and SSTA of LPCA explained 86%, 71%, and 94% of the total variance in the original data, respectively. Thus, the three associated time coefficient functions (TCFs) were used as the input data for NLPCA network. The NLPCA was made based on feed-forward neural network models. Compared with classical linear PCA, the first NLPCA mode could explain more variance than linear PCA for the above data. The nonlinearity of SWA and SSHA were stronger in most areas of the SCS. The first mode of the NLPCA on the SWA and SSHA accounted for 67.26% of the variance versus 54.7%, and 60.24% versus 50.43%, respectively for the first LPCA mode. Conversely, the nonlinear SSTA, localized in the northern SCS and southern continental shelf region, resulted in little improvement in the explanation of the variance for the first NLPCA.