Next Article in Journal
Using LDR as Sensing Element for an External Fuzzy Controller Applied in Photovoltaic Pumping Systems with Variable-Speed Drives
Previous Article in Journal
Toward Epileptic Brain Region Detection Based on Magnetic Nanoparticle Patterning
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(9), 24428-24444; doi:10.3390/s150924428

Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures

1
Department of Civil and Environmental Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea
2
Department of Public Works and Civil Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
3
Incheon Disaster Prevention Research Center, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 15 July 2015 / Revised: 7 September 2015 / Accepted: 7 September 2015 / Published: 22 September 2015
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [827 KB, uploaded 22 September 2015]   |  

Abstract

The Global Positioning System (GPS) is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge’s short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements’ contents. View Full-Text
Keywords: GPS; de-noise; monitoring; neural network GPS; de-noise; monitoring; neural network
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Kaloop, M.R.; Hu, J.W. Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures. Sensors 2015, 15, 24428-24444.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top