NDLI: Classification with cooperative semi-supervised learning using bridge structural health data

Content Provider	IEEE Xplore Digital Library
Author	Chongchong Yu Lili Shang Li Tan Yang Yang Xuyan Tu
Copyright Year	2012
Description	Author affiliation: School of Computer & Communication Engineering, University of Science and Technology Beijing, 100083, China (Chongchong Yu; Yang Yang; Xuyan Tu) \|\| School of Computer & Information Engineering, Beijing Technology and Business University, 100048, China (Lili Shang; Li Tan)
Abstract	In the process of bridge structural health monitoring, the parameters monitored by sensors includes strain, vibration, distortion, cable tension etc.. Classification of each parameter can reflect the change of bridge structural health to some extent. According to feature of parameter data, solving methods, namely, improved single-view cooperative-training semi-supervised learning method and multi-view cooperative-training semi-supervised learning method with disagreement are proposed based on problems of single parameter classification and multiple parameters classification separately. The former exploits single-view cooperative-training semi-supervised learning method to classify for single parameter data. The latter takes multiple parameters classification by using different single-view to constitute multi-view, the key of which is to exploit Co-training with disagreement to deal with the problem of two views classification. Bridge structural health information can be obtained effectively through bridge structural data classification with these methods, based on which bridge would be maintained effectively.
Starting Page	1290
Ending Page	1294
File Size	277459
Page Count	5
File Format	PDF
ISBN	9781467301985
e-ISBN	9781467301992
DOI	10.1109/ICSAI.2012.6223271
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bridges Training Bridge structural health monitoring Bridge structural health data Co-training with disagreement Improved single-view cooperative-training semi-supervised learning method Prediction algorithms Data models Multi-view cooperative-training semi-supervised learning method with disagreement Classification algorithms Labeling Monitoring
Content Type	Text
Resource Type	Article

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