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| Paper Abstract and Keywords | ||
| Presentation | 2023-11-09 17:00 A Comparison of Classification Learning Algorithm for Location Estimation in 2 Dimensional Indoor Environment Using Received Signal Power and Various Items of Sensed Information Shunki Sato, Eisuke Kudoh (Tohoku Institute of Tech.)  SR2023-55 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In the indoor environment, it is difficult to receive radio waves from satellites, and location estimation using GPS, etc. is also difficult. By the way, sensed information such as temperature, humidity, and illuminance depends on location. Accurate location estimation is possible by using so various sensed information in addition to received signal power. In recent years, machine learning has been attracting attention as an estimation algorithm. In this paper, we estimate the location from experimental data obtained from an indoor experiment using eight classification learning algorithms provided by sklearn, which is one of the Python machine learning libraries. As a result, the coincidence probability between the estimated location and the actual location obtained by a classification learner using ensemble learning was found to be higher than the values obtained using other classification learners. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Classification learning / location estimation / ZigBee / Sensed Information / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 249, SR2023-55, pp. 48-51, Nov. 2023. | |
| Paper # | SR2023-55 | |
| Date of Issue | 2023-11-02 (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 | SR2023-55 |
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| Conference Information | |
| Committee | SR |
| Conference Date | 2023-11-09 - 2023-11-10 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Software Defined Radio, Cognitive Radio, Dynamic Spectrum Sharing, Technology Exhibition, Product Exhibition, etc |
| Paper Information | |
| Registration To | SR |
| Conference Code | 2023-11-SR |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Comparison of Classification Learning Algorithm for Location Estimation in 2 Dimensional Indoor Environment Using Received Signal Power and Various Items of Sensed Information |
| Sub Title (in English) | |
| Keyword(1) | Classification learning |
| Keyword(2) | location estimation |
| Keyword(3) | ZigBee |
| Keyword(4) | Sensed Information |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Shunki Sato |
| 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 | 2023-11-09 17:00:00 |
| Presentation Time | 25 minutes |
| Registration for | SR |
| Paper # | SR2023-55 |
| Volume (vol) | vol.123 |
| Number (no) | no.249 |
| Page | pp.48-51 |
| #Pages | 4 |
| Date of Issue | 2023-11-02 (SR) |
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