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| Presentation | 2020-03-06 14:55 Efficient cluster mapping for conditions of weather based on combination of self-organizing map and hierarchical clustering Kazuki Osawa, Keiji Kamei (NIT), Masumi Ishikawa (KIT)  NC2019-113 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recently, applications of Deep Learning(AI) for solving social problems have been frequently proposed. However, there are several difficult cases of that implementation because we are not able to understand the reason of answer from AI. For this, the “Explainable AI” attracts rising attention. We have applied sparse modelization is proposed by Ishikawa to constructing explainable RNN for prediction of power demands. The results showed that model construction under the certain weather conditions was important for prediction of power demands. In this study, we propose to create clusters of the certain weather conditions for prediction of power demands by RNN based on combination of Self-Organizing Map and hierarchical clustering method. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Self-Organizing Map / Hierarchical Clustering / Explainable AI / Recurrent Neural Network / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 453, NC2019-113, pp. 213-218, March 2020. | |
| Paper # | NC2019-113 | |
| Date of Issue | 2020-02-26 (NC) | |
| 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 | NC2019-113 |
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| Conference Information | |
| Committee | NC MBE |
| Conference Date | 2020-03-04 - 2020-03-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | University of Electro Communications |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Neuro Computing, Medical Engineering, etc. |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2020-03-NC-MBE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Efficient cluster mapping for conditions of weather based on combination of self-organizing map and hierarchical clustering |
| Sub Title (in English) | |
| Keyword(1) | Self-Organizing Map |
| Keyword(2) | Hierarchical Clustering |
| Keyword(3) | Explainable AI |
| Keyword(4) | Recurrent Neural Network |
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| Keyword(8) | |
| 1st Author's Name | Kazuki Osawa |
| 1st Author's Affiliation | Nishinippon Institute of Technology (NIT) |
| 2nd Author's Name | Keiji Kamei |
| 2nd Author's Affiliation | Nishinippon Institute of Technology (NIT) |
| 3rd Author's Name | Masumi Ishikawa |
| 3rd Author's Affiliation | Kyushu Institute of Technology (KIT) |
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| Speaker | Author-1 |
| Date Time | 2020-03-06 14:55:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2019-113 |
| Volume (vol) | vol.119 |
| Number (no) | no.453 |
| Page | pp.213-218 |
| #Pages | 6 |
| Date of Issue | 2020-02-26 (NC) |
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