원문정보
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
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