Paper Info
Title
Defect Identification by Sensor Network Under Uncertainties
Abstract
This paper presents a theoretical framework for identification of defects by a sensor network under uncertainties. While location of sensors are not known due to their inspection due to limited knowledge on the structure to be inspected, existing inspection methods do not take uncertainties of sensor locations into account for the localization of defects. The proposed theoretical framework formulates the uncertainties of sensor states stemming from both motion and measurement and allows stochastic identification of defects using recursive Beyesian estimation. Multi-sensor belief fusion further allows a network of sensors to jointly identify defects and improve the accuracy of identification. Parametric studies and application to practical defect identification have shown the validity of the proposed framework.
Year
DOI
Venue
2010
10.1109/BWCCA.2010.64
BWCCA
Keywords
Field
DocType
Bayes methods,belief networks,inspection,sensor fusion,stochastic processes,structural engineering,Beyesian estimation,defect identification,multisensor belief fusion,sensor localization,sensor network,sensor states stemming,stochastic identification,defect identification,recursive Bayesian estimation,sensor network,sensor uncertainties
Data mining,Computer science,Stochastic process,Recursive Bayesian estimation,Sensor fusion,Parametric statistics,Probabilistic logic,Wireless sensor network,Recursion,Bayesian probability
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Tomonari Furukawa136444.93
Jinquan Cheng211.02
Shen Hin Lim311.07
Fei Xu41112.78
Ryuji Shioya501.69