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) |
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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) |
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Keyword(1) |
location estimation |
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machine learning |
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ZigBee |
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sensed information |
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IoT |
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1st Author's Name |
Ren Kawamura |
1st Author's Affiliation |
Tohoku Institute of Technology (Tohoku Institute of Tech.) |
2nd Author's Name |
Eisuke Kudoh |
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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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