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dc.contributor.authorLiu, Zhenzh_CN
dc.contributor.authorZhou, Qifengzh_CN
dc.contributor.authorChi, Qijunzh_CN
dc.contributor.authorZhang, Yuanyuanzh_CN
dc.contributor.authorChen, Youlingzh_CN
dc.contributor.authorQi, Senzh_CN
dc.contributor.author刘臻zh_CN
dc.contributor.author周绮凤zh_CN
dc.date.accessioned2015-07-22T02:40:02Z
dc.date.available2015-07-22T02:40:02Z
dc.date.issued2014 October 15zh_CN
dc.identifier.citationProceedings of the 9th International Conference on Computer Science and Education, ICCCSE 2014, 2014:551-556zh_CN
dc.identifier.other20144800257233zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/86946
dc.descriptionConference Name:9th International Conference on Computer Science and Education, ICCCSE 2014. Conference Address: Vancouver, BC, Canada. Time:August 22, 2014 - August 24, 2014.zh_CN
dc.description.abstractStructural damage detection is a key part of structural health monitoring. In recent years, intelligent detecting methods are used in this field and show good performance. This paper proposed a structural damage detection method based on data fusion and semi-supervised fuzzy C-means clustering. Compared with other intelligent method, our method can detect the damage location and extent, meanwhile, provide a confidence. Experiment results on a benchmark model show effectiveness of the proposed methods.zh_CN
dc.language.isoen_USzh_CN
dc.publisherInstitute of Electrical and Electronics Engineers Inc.zh_CN
dc.source.urihttp://dx.doi.org/10.1109/ICCSE.2014.6926522zh_CN
dc.titleStructural damage detection based on semi-supervised fuzzy C-means clusteringzh_CN
dc.typeConferencezh_CN


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