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A segmentation scheme with SVR-based prediction in stroke-sensing cylinder

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Abstract

Signal processing in stroke-sensing system of cylinder has been addressed by researchers from very different fields. However, the low accuracy of subdividing technique results in the difficulties in signal segmentation. This paper proposes methodology analyzes the measuring principle and subdivision error of the displacement detection. A segmentation scheme based on support vector regression (SVR) algorithm is developed to efficiently compensate the complexity and nonlinearity in tangent subdivision, and it enables accurate modeling and predicting of angle variation by applying SVR algorithm. Further, the regression model improves the accuracy by resolving the segmentation interval. Due to the significances presented in this work, the detection error decreases from 1.59 to 0.286%, when processed with the proposed segmentation algorithm. Experimental results are statistically analyzed, which makes it a promising basis for the realization of exact position detection.

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Acknowledgements

This research is a general project supported by Education Department of Hunan Province (16C0298).

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Authors and Affiliations

  1. Shanxi Key Laboratory of Advanced Manufacturing Technology, North University of China, Taiyuan, 030051, China

    Lu Liu & Yanqing Guo

  2. School of Mechanical Engineering, North University of China, Taiyuan, 030051, China

    Lu Liu, Yanqing Guo, Chuangchuang Li & Liang Guo

  3. School of Mechanical Engineering and Automation, Beihang University, Beijing, 100091, China

    Yongling Fu

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  1. Lu Liu
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  2. Yanqing Guo
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  4. Chuangchuang Li
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  5. Liang Guo
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Correspondence to Yanqing Guo.

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Liu, L., Guo, Y., Fu, Y. et al. A segmentation scheme with SVR-based prediction in stroke-sensing cylinder. Cluster Comput 22 (Suppl 2), 2875–2887 (2019). https://doi.org/10.1007/s10586-017-1645-2

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