提出了空调系统传感器故障检测、故障识别、故障重构的主成分分析方法。
The principal component analysis (PCA) approach for sensor fault detection, identification and reconstruction in HVAC system is presented.
主成分分析(PCA)是自动人脸识别的常用方法。
Principal Component Analysis (PCA) is a common method in face recognition.
基于核主成分分析（KPCA）的人脸识别算法能够提取非线性图像特征，在小样本训练条件下有较好性能。
The algorithm of face recognition based on kernel principal component analysis(KPCA)can abstract nonlinear features of image and can get better performance under less sample training conditions.
方法应用主成分分析法(PCA)对来源于全国不同产地的女贞子进行了化学模式识别研究。
Methods Chemical pattern recognition was performed on the fruits of Ligustrum lucidum collected from various areas of China by PCA (principal component analysis) method.
结果所得的数据通过模式识别法中的主成分分析（PCA），在得分散点图中实现了对不同种类的制剂的区分。
Results Analyzing the data with principal component analysis(PCA) by model recognition, the different agents can be distinguished in the scattered plots.
目的研究便携式拉曼光谱仪对解热镇痛类药品主成分的识别能力。
OBJECTIVE To investigate the identification of principal constituent of the fever-relieved and pain-relieved drugs by Handheld Raman Spectrometer.
基于遥感主成分分析(PCA)，论文提出一种快速、准确及经济地识别填海造地时空分布的新方法。
Based on Principal Component Analysis (PCA) for remote sensing image, a new method was applied into identifying the spatiotemporal distribution of sea reclamation quickly and exactly.
本文通过对人脸检测与识别技术的研究，提出了一种利用眼睛梯度特征的人脸检测方法并对主成分分析方法做了改进以进行人脸识别。
Through studying the face detection and recognition technique, this thesis presents a method of face detection based on eyes feature and improves the PCA to recognize human face.
基于OR L人脸库，识别核主成分分析提取出的主成分的相关性系数。
Based on ORL face database, recognizes correlation coefficients of principal component extracted by KPCA.
提出了一种基于核主成分特征组合的人脸识别方法。
A new face recognition method based on combination of KPCA features is proposed in this paper.
本文对基于主成分分析的特征级图像融合及其在弱小目标匹配识别上的应用做了一定的研究和探索。
This dissertation mainly studied the image feature fusion based on PCA (principal component analyses) and its application of Small-weak target matching.
对于检测到的人脸图像，建立了自己的实验室人脸库，并利用PCA主成分分析法实现了人脸识别。
For the detected face images, has established my own laboratories face images, and using principal component analysis of PCA method to achieve face recognition.
最后提出了基于统计分类的目标检测方法，并实现了基于主成分分析的目标识别系统，实验取得了比较理想的结果。
Finally we propose statistical classification based object detecting method, and realize the PCA based object detecting system. The system is experimented and achieves satisfactory results.
当支持向量机和主成分分析结合后，试验的损伤识别效果有明显的提高。
The structural damage position and deg ree can be identified and classified, and the test result is highly accurate especially combined with principle component analysis.
而用聚类分析和主成分分析对各生等药材的含量进行分类，排除不合格样品，得到合格样品的共有模式，可用于识别未知的样品。
Classify the different quantitative samples with component and cluster analytical methods, excluding the inferior medicine, the common model can be used to identify of any Shengdeng medicine.
提出了一种基于主成分分析法对ECG信号进行特征表述的身份识别新方法。
This paper presents eigen ECG, which is based on principal component analysis, a new method for hu- man identification.
KPCA采用非线性方法提取主成分，描述待识别图像中多个像素之间的相关性。
KPCA extracted principal component with nonlinear method and described the relationship among three or more pixels of the identified images.
针对二维主成分分析（2DPCA）提取的是人脸的全局特征，但局部特征对人脸识别的作用非常大，提出了一种基于局部特征的自适应加权2DPCA。
From researching on the universal principle of feature fusion of image, a new algorithm was proposed which based on the 2 dimension principal component analyses(for short 2DPCA).
通过主成分分析、聚类分析和BP神经网络对实验数据进行了分析和识别。
Principal component analysis (PCA), cluster analysis (CA) and back-propagation artificial neural network (BP-ANN) were used in the data analysis and pattern recognition.
通过主成分分析、聚类分析和BP神经网络对实验数据进行了分析和识别。
Principal component analysis (PCA), cluster analysis (CA) and back-propagation artificial neural network (BP-ANN) were used in the data analysis and pattern recognition.
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