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Articles you may be interested inSeeking to quantify the ferromagnetic-to-antiferromagnetic interface coupling resulting in exchange bias with various thin-film conformations J. Appl. Phys. 116, 053911 (2014); 10.1063/1.4892177Effect of antiferromagnetic layer thickness on exchange bias, training effect, and magnetotransport properties in ferromagnetic/antiferromagnetic antidot arrays

In practice, it is difficult for Support Vector Machine (SVM) to have a relatively high recognition rate as well as a quite fast recognition speed. In order to resolve this defect, in this paper we build a SVM classification model combining numerical characteristics. We use readings of rotary natural meters as the test temple, do positioning, preprocessing, feature points extracting, classifying and other series of operations to the numeric region of the dial. Then with the idea of cross-validation, we keep doing parameter optimation to SVM. At last, after making a comprehensive contrast of the effects which numerous performance factors make on the experimental outputs, we try to give our explanation of the outputs from different perspectives.

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