datum dimension การใช้

• Tackling the data dimension of interoperability
• Digital 21 : tackling the data dimension of interoperability
• Main technical data dimension of duct
• The tcq algorithm takes advantage of convolution code , and enlarges the euclidean distances among quantized data by expanding data dimensions
• Conventionally , due to the huge volume of data , the analysis of erp data , viewed as a multivariate problem , usually adopts principle component analysis ( pca ) to reduce data dimension
• Pca can project complicated related multi - dimensional data into an orthogonal system to extract independent components , and discard unimportant components to reduce data dimension
• On the basis of request to untouch measure online , this paper put forward the method of " datum dimension precision back " , and then the mathematics model is being established
• Analyze item by item the position of unintact cycle , the running clearance of unintact cycle , locking - deform , datum dimension regulating , repeatly install , power voltage wave and marking running etc . at the same time , we give the calculating formula to calculating the running marking random error , and use it to calculate the system error of big diameter measure instrument - - datum dimension frame error , gyro - wheel diameter error , error caused by circumstance temperature , error caused by backing distance , angle error , delay error of data collecting circuit , lathe main shaft running error , workpiece install partial error
• Especially , the idea of " datum dimension precision back " is being put forward , and this idea is being realized by backing composition composed by outer - diameter micrometer , airproof shafting , microwatch . at the same time , use " non - lock " to replace " lock " , this way make the system compact , eliminate many bigger error and resolve th
• Applying statistic learning theory and support vector machine in high dimensional multispectral data classification , the hughes phenomenon is mitigated and higher classification accuracy is obtained . the relation between the performance of svm and kernel function , support vector , training set , data dimension and so on is studied . 2
• The high dimensional multispectral data , which features high spectral resolution , high spatial resolution , and large dynamic range , have provided luxuriant information about earth surface for people . because the number of training samples is limited and data dimension is high , the performance of traditional pattern classification algorithms is deteriorated
• The dissertation explores the modeling approaches of normal behavior of process on the ground of knowledge representation and rule acquisition of rough set . besides it , a hybrid anomaly detection algorithm associating reduct of rough set with classification of svm is proposed . the underlying idea is reducing data dimension in virtue of attribute reduct , then operating reduced and normalized datum using the binary v - svm algorithm