You are not logged in.
Openly accessible

Predicting vertical acceleration of railway wagons using regression algorithms

Shafiullah, G. M., Shawkat, Ali A. B. M., Thompson, Adam and Wolfs, Peter J. 2010, Predicting vertical acceleration of railway wagons using regression algorithms, IEEE transactions on intelligent transportation systems, vol. 11, no. 2, Article number : 5419947, pp. 290-299, doi: 10.1109/TITS.2010.2041057.

Attached Files
Name Description MIMEType Size Downloads
shafiullah-predictingvertical-2010.pdf Published version application/pdf 1.11MB 229

Title Predicting vertical acceleration of railway wagons using regression algorithms
Author(s) Shafiullah, G. M.
Shawkat, Ali A. B. M.
Thompson, Adam
Wolfs, Peter J.
Journal name IEEE transactions on intelligent transportation systems
Volume number 11
Issue number 2
Season Article number : 5419947
Start page 290
End page 299
Total pages 10
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2010-06
ISSN 1524-9050
1558-0016
Keyword(s) Fast Fourier transform (FFT)
railway wagons
regression algorithm
vertical acceleration
Language eng
DOI 10.1109/TITS.2010.2041057
Field of Research 080109 Pattern Recognition and Data Mining
090602 Control Systems, Robotics and Automation
Socio Economic Objective 880104 Rail Safety
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2010, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30059264

Document type: Journal Article
Collections: School of Engineering
Open Access Collection
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 9 times in TR Web of Science
Scopus Citation Count Cited 14 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 55 Abstract Views, 229 File Downloads  -  Detailed Statistics
Created: Wed, 08 Jan 2014, 13:20:11 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.