Authors:
Jihoon Lee
;
Yousok Kim
;
Se-Woon Choi
and
Hyo-Seon Park
Affiliation:
Yonsei University, Korea, Republic of
Keyword(s):
Measurement Faults, Estimating Error Data, Post-processing of ANN.
Related
Ontology
Subjects/Areas/Topics:
Applications and Uses
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Fault Tolerance and Diagnosis
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Obstacles
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Smart Buildings and Smart Cities
;
Soft Computing
;
Theory and Methods
Abstract:
A sensor network is a key factor for successful structural health monitoring (SHM). Although stable sensor network system is deployed in the structure for measurement, it is often inevitable to face measurement faults. In order to secure the continuous evaluation of targeted structure in cases where the measurement faults occur, appropriate techniques to estimate omitted or error data are necessary. In this research, back-propagation neural network is adopted as a basic estimation method. Then, a concept of post-processing is proposed to improve an accuracy of estimation obtained from the neural network. The results of simulation to verify performance of estimation are also shown.