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- Title
基于层次语义多项式 DS 融合的铁路扣件状态分布学习.
- Authors
黄翰鹏; 罗建桥; 李柏林
- Abstract
Aiming at the problems of weak adaption and high false alarm in established algorithms for fastener condition detection, sample labels are smoothed to alleviate the over-fitting problem during the process of convolutional neural network training, and a fastener condition distribution learning algorithm is proposed based on Dempster Shafer (DS) fusion of hierarchical semantic multinomial (SMN). Firstly, the convolutional features of image sub-block are represented as Gaussian mixture models in a weakly supervised manner, and consequently sample SMNs are computed according to the Gaussian mixtures. Then, in order to improve the description ability of SMNs for fastener samples, DS fusion is conducted on the SMNs derived from multi-level features, which leads to the conditional distribution of each sample. Such distributions reflect the description degrees of different labels, and realize the smoothing of single labels. Experimental results shown that, by replacing the single labels with the condition distribution for CNN training, the gap between training and validation accuracies is reduced, and the proposed algorithm yields the false alarm rate of 1. 9% and the missing rate of 2. 3%, the rate of false alarm is reduced by 54% compared to conventional single label networks. The proposed algorithm can alleviate the over-fitting symptom, improve the generalization ability of networks, and realize robust detection of fastener conditions.
- Subjects
CONVOLUTIONAL neural networks; MACHINE learning; GAUSSIAN mixture models; FALSE alarms; FASTENERS; GENERALIZATION
- Publication
Railway Standard Design, 2022, Vol 66, Issue 7, p48
- ISSN
1004-2954
- Publication type
Article
- DOI
10.13238/j.issn.1004-2954.202103090006