Multiresolution classification with semi-supervised learning for indirect bridge structural health monitoring
ICASSP(2013)
摘要
We present a multiresolution classification framework with semi-supervised learning for the indirect structural health monitoring of bridges. The monitoring approach envisions a sensing system embedded into a moving vehicle traveling across the bridge of interest to measure the modal characteristics of the bridge. To enhance the reliability of the sensing system, we use a semi-supervised learning algorithm and a semi-supervised weighting algorithm within a multiresolution classification framework. We show that the proposed algorithm performs significantly better than supervised multiresolution classification.
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关键词
semisupervised learning,indirect bridge structural health monitoring,bridge structural health monitoring,bridges (structures),learning (artificial intelligence),structural engineering,multiresolution classification framework,sensing system,semi-supervised learning,modal characteristics,moving vehicle,multiresolution classification,reliability,condition monitoring,vectors,labeling,semi supervised learning,feature extraction,learning artificial intelligence
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