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Cluster Computing

, Volume 22, Supplement 2, pp 2875–2887 | Cite as

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

  • Lu Liu
  • Yanqing GuoEmail author
  • Yongling Fu
  • Chuangchuang Li
  • Liang Guo
Article
  • 519 Downloads

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.

Keywords

Stroke-sensing cylinder Signal segmentation Tangent subdivision Support vector regression 

Notes

Acknowledgements

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

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Lu Liu
    • 1
    • 2
  • Yanqing Guo
    • 1
    • 2
    Email author
  • Yongling Fu
    • 3
  • Chuangchuang Li
    • 2
  • Liang Guo
    • 2
  1. 1.Shanxi Key Laboratory of Advanced Manufacturing TechnologyNorth University of ChinaTaiyuanChina
  2. 2.School of Mechanical EngineeringNorth University of ChinaTaiyuanChina
  3. 3.School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina

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