IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2017-11-10 13:00
Feature Extraction Using Empirical Mode Decomposition for Tire Sensing
Keita Ishii, Takato Goto (BS), Matsui Tomoko (ISM), Gareth Peters (HW), Nourddine Azzaoui (UCA) IBISML2017-78
Abstract (in Japanese) (See Japanese page) 
(in English) This paper describes a novel feature-extraction method for classifying road conditions by using acceleration signals that are measured using tire-mounted sensors. We acquired road data under two conditions: dry and wet. The acceleration signals are caused by the revolution of a tire on a momentary scale; therefore, frequency analysis based on Fourier transforms should not be applied to our signals. In this study, empirical mode decomposition (EMD) was employed to extract features, because the method is suitable for nonlinear and unstable data. By using the features, the calculation cost was significantly reduced and road classification was accomplished with more than 95% accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Tire / Road condition / Empirical mode decomposition / Feature extraction / Machine learning / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 293, IBISML2017-78, pp. 315-320, Nov. 2017.
Paper # IBISML2017-78 
Date of Issue 2017-11-02 (IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IBISML2017-78

Conference Information
Committee IBISML  
Conference Date 2017-11-08 - 2017-11-10 
Place (in Japanese) (See Japanese page) 
Place (in English) Univ. of Tokyo 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information-Based Induction Science Workshop (IBIS2017) 
Paper Information
Registration To IBISML 
Conference Code 2017-11-IBISML 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Feature Extraction Using Empirical Mode Decomposition for Tire Sensing 
Sub Title (in English)  
Keyword(1) Tire  
Keyword(2) Road condition  
Keyword(3) Empirical mode decomposition  
Keyword(4) Feature extraction  
Keyword(5) Machine learning  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Keita Ishii  
1st Author's Affiliation Bridgestone Corporation (BS)
2nd Author's Name Takato Goto  
2nd Author's Affiliation Bridgestone Corporation (BS)
3rd Author's Name Matsui Tomoko  
3rd Author's Affiliation Institute of Statistical Mathematics (ISM)
4th Author's Name Gareth Peters  
4th Author's Affiliation Heriot-Watt University (HW)
5th Author's Name Nourddine Azzaoui  
5th Author's Affiliation Université Clermont Auvergne (UCA)
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker Author-1 
Date Time 2017-11-10 13:00:00 
Presentation Time 150 minutes 
Registration for IBISML 
Paper # IBISML2017-78 
Volume (vol) vol.117 
Number (no) no.293 
Page pp.315-320 
#Pages
Date of Issue 2017-11-02 (IBISML) 


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan