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Paper Abstract and Keywords
Presentation 2022-11-07 09:30
Neural network configuration of machine learning for location estimation at various altitudes using multiple items of sensed information in indoor environment
Ren Kawamura, Eisuke Kudoh (Tohoku Institute of Tech.) SR2022-45
Abstract (in Japanese) (See Japanese page) 
(in English) In an indoor environment, it is difficult to receive radio waves directly from satellites which hinders accurate location estimation by satellite signals. Meanwhile, mobile communication propagation channels suffer from fading and shadowing, so estimation of indoor locations by using only the received signal power of a radio wave is inaccurate as well. Sensed information (e.g., temperature, humidity, illuminance) is often location dependent, and a location can be estimated accurately if such information is used in addition to the received signal power. The use of machine learning for an optimization algorithm has been shown to be promising. In this paper, we apply machine learning for indoor location estimation at various altitudes using multiple items of sensed information. We propose two types of neural networks, a two-dimensional neural network and a nearest node neural network, and experimentally evaluate them in an actual building. The results indicate that location estimation using the nearest node neural network has a greater coincidence probability than that using the two-dimensional neural network.
Keyword (in Japanese) (See Japanese page) 
(in English) location estimation / machine learning / ZigBee / sensed information / IoT / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 243, SR2022-45, pp. 1-6, Nov. 2022.
Paper # SR2022-45 
Date of Issue 2022-10-31 (SR) 
ISSN 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 SR2022-45

Conference Information
Committee SR  
Conference Date 2022-11-07 - 2022-11-08 
Place (in Japanese) (See Japanese page) 
Place (in English) Fukuoka University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Software Defined Radio, Cognitive Radio, Spectrum Sharing, etc. 
Paper Information
Registration To SR 
Conference Code 2022-11-SR 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Neural network configuration of machine learning for location estimation at various altitudes using multiple items of sensed information in indoor environment 
Sub Title (in English)  
Keyword(1) location estimation  
Keyword(2) machine learning  
Keyword(3) ZigBee  
Keyword(4) sensed information  
Keyword(5) IoT  
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Keyword(8)  
1st Author's Name Ren Kawamura  
1st Author's Affiliation Tohoku Institute of Technology (Tohoku Institute of Tech.)
2nd Author's Name Eisuke Kudoh  
2nd Author's Affiliation Tohoku Institute of Technology (Tohoku Institute of Tech.)
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Speaker Author-1 
Date Time 2022-11-07 09:30:00 
Presentation Time 25 minutes 
Registration for SR 
Paper # SR2022-45 
Volume (vol) vol.122 
Number (no) no.243 
Page pp.1-6 
#Pages 6 
Date of Issue 2022-10-31 (SR) 

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