Computer Science ›› 2020, Vol. 47 ›› Issue (12): 262-266.doi: 10.11896/jsjkx.200500085

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Novel Semi-supervised Extreme Learning Machine and its Application in Anti-vibration HammerCorrosion Detection

WANG Hong-xing1, CHEN Yu-quan1, SHEN Jie1, ZHANG Xin1, HUANG Xiang1, YU Bin2   

  1. 1 Jiangsu Fangtian Electric Power Technology Co.Ltd Nanjing 210036,China
    2 Beihang University Beijing 100191,China
  • Received:2020-05-19 Revised:2020-07-08 Published:2020-12-17
  • About author:WANG Hong-xing,,born in 1974,master,professor level senior engineer,member of electromagnetic interference special committee of Chinese Society for Electri-cal Engineering.Research directions in-clude intelligent inspection Technology of UAV for transmission line,automatic path planning of UAV,etc.
  • Supported by:
    Jiangsu Fangtian Electric Power Technology Co. Ltd Research Program(KJ201915).

Abstract: Visual inspection based on machine learning has been widely used in industrial fields including rust detection.In view of the existing problems of high complexity and relying on a large number of manual annotationa new semi-supervised Extreme Learning Machine named HyLap-S3ELM is proposed in this paper and applied to the detection of corrosion defects of shock hammer.Model parameters have closed solutionsso they can be calculated directly and have little dependence on operation resources.A hypergraph Laplacian matrix is introduced to better describe the smoothness of dataso as to improve the accuracy of semi-supervised classification.The risk regularization term is introduced to improve the stability of semi-supervised classifier when the assumption of data smoothness is in accurate or there is deviation of marked samples.Finallythe effectiveness and superiority of the proposed method are proved by a large number of experiments.

Key words: Anti-vibration hammer, Corrosion detection, ELM(Extreme Learning Machine), Visual crack detection

CLC Number: 

  • TM734
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