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1)  oblique PCA
斜交主元分析
2)  principal component analysis
主元分析
1.
Application of improved principal component analysis method to mould process monitoring in continuous casting;
改进的主元分析法在连铸结晶器过程监测中的应用
2.
The application of principal component analysis on sEMG signals analysis during slips;
主元分析在研究滑倒时肌电信号中的应用
3.
A method for truss structural damage detection based on principal component analysis;
基于主元分析的网架结构损伤识别方法
3)  Principle component analysis
主元分析
1.
Diagnosis for Sensor Faults Based on Principle Component Analysis;
基于主元分析的传感器故障诊断
2.
The principle component analysis (PCA) technique is applied to preprocessing high dimensional input variable so that the dimension of input variable is decreased, and an estimator model based on PCAFRBFN is constructed.
研究以模糊聚类和径向基函数网络结合的模糊径向基函数网络FRBFN,并用主元分析对高维输入变量进 行预处理,降低了模型的输入变量维数,进而构造基于PCA-FRBFN的估计模型。
3.
In this paper,the principle component analysis(PCA) theory is introduced,and the theory is used for fault diagnosis of lock of actuator.
文中介绍了主元分析算法以及在故障检测方面的应用。
4)  PCA
主元分析
1.
Gross Error Detection and Proofreading Concentration Based on PCA In the Measurement of Pulverized-Coal Concentration;
基于主元分析(PCA)的显著误差检测与校正及应用
2.
Fault Diagnosis of Power Electronic Circuits Based on Wavelet Multi-resolution Analysis and PCA;
基于小波多分辨率分析和主元分析的电力电子电路故障诊断
3.
The Multivariate Statistical Theory Based on PCA Analysis Applied in Process Monitoring;
基于主元分析的多变量统计理论在过程监控中的应用
5)  Principal component analysis(PCA)
主元分析
1.
Traditional principal component analysis(PCA) based methods have been proved to be unreliable when applied to nonlinear process monitoring and false alarms might occur.
基于传统主元分析(PCA)方法的过程监测算法假定过程是线性的,对于具有强非线性的生产过程,应用其进行在线监测出现误报率过高的现象。
2.
The authors present a new method to remedy the negative effect arising from slant vehicle license plate s character segmentation and recognition based on principal component analysis(PCA).
为解决机动车牌图像倾斜将对其字符分割与识别带来不利的影响,提出一种基于主元分析(PCA)的车牌图像倾斜校正新方法。
3.
A new method based on combined principal component analysis(PCA)with support vector machine(SVM)techniques was presented after limitation of control chart-recognizer using in practice was analyzed.
该文在分析现有控制图识别技术在实际应用中存在缺陷的基础上,提出了一种基于主元分析(PCA)和支持向量机(SVM)的控制图失控模式识别方法。
6)  principal components analysis
主元分析
1.
The paper use PCA(principal components analysis)algorithm for ball bearing data preprocessing which degrades data dimension and extract feature data.
本文使用PCA(主元分析)算法对滚动轴承振动信号数据进行预处理,这可降低数据维数和提取数据特征信息;将预处理后数据作为SVM(支持向量机)算法的输入,通过SVM算法来检测轴承故障。
2.
The face recognition with the principal components analysis based on classification of face orientation shows the accuracy of face recognition is increased.
在人脸朝向的分类基础上,用主元分析法进行人脸识别。
3.
A new algorithm is proposed incorporating wavelet transformation with principal components analysis techniques in surppressing prediction horizons and control horizons of DMC controllers, which results in lower matrix dimensions and better matrix conditions.
提出用小波变换和主元分析相结合的方法来压缩预测时域及控制时域,在保证算法鲁棒控制性能的前提下降低动态矩阵维数并改善矩阵病态性,从而使动态矩阵控制算法的计算效率大大提高。
补充资料:带束斜交轮胎
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性质:又称半子午线轮胎。指以带束层箍紧斜交轮胎胎体的充气轮胎。其带束层帘线排列与子午线轮胎接近,轮胎使用性能介于斜交轮胎和子午线轮胎之间,其生产方法基本上与斜交轮胎相同。这种轮胎世界上只有美国生产,作为由斜交轮胎向子午线轮胎过渡的产物,随着子午线轮胎的发展,这种轮胎将逐渐被淘汰。

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