Earticle

초록

영어

As a key part of track circuit, the state of insulation joints is related to safe, normal and efficient operation of railway. In order to accurately obtain different degrees of insulation joints, a prediction model based on support vector machines has been proposed to study damage to insulation joints. For unbalanced data sets in the research process, a KNN under-sampling is presented to remove redundant and noise samples. By means of BSMOTE over-sampling method to further take full advantage of the data, KNN-BSMOTE-SVM algorithm of hybrid sampling is given to achieve balanced data sets. The theoretical analysis and simulation results show that the proposed algorithm increases classification performance of SVM classifier. Compared with KNN classifier, the classification results of SVM are better, support vector machines used in insulation damaged joints prediction is feasible and effective.

목차

Abstract
 1. Introduction
 2. Theoretical Background
 3. Insulation Joints Description
 4. KNN-BSMOTE-SVM Algorithm
  4.1 KNN Under-Sampling
  4.2 BSMOTE Over-Sampling
  4.3 KNN-BSMOTE-SVM Algorithm
 5. Experimental Analysis
  5.1 Experimental Data
  5.2 Evaluation Standard
  5.3 Simulation Results
 6. Conclusion
 Acknowledgement
 References

키워드

저자

• Dong Yu [ School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China ]
• Xiao Zi-Qiang [ School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605