Information: Join today and make your research activities more affordable! Technical workshop participation fees and annual registration fees are available at member rates.
Notice: [Important] Announcement of Changes to Registration Fee Payment and Manuscript Upload Procedures for IEICE Technical Meetings
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 2023-10-19 10:00
[Poster Presentation] Study on Prediction Accuracy against Resolution of Input Data in Rain Attenuation Prediction Model using Deep Learning
Yuji Komatsuya, Tetsuro Imai (TDU), Miyuki Hirose (Kyutech) AP2023-87
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, the practical application of NTN (Non-Terrestrial-Network) using HAPS (High Altitude Platform Station) as the next-generation communication platform is studied actively. It is necessary for HAPS to address rain attenuation depends on the frequency used. Hence, we have proposed rain attenuation prediction method using deep learning for HAPS that inputs high-resolution precipitation nowcast which is rainfall observation data provided by the Japan Meteorological Agency using a weather radar with a spatial resolution of 250 m square. On the other hand, there are several countries, not only Japan, where rainfall observation data by meteorological radar is available, and the spatial resolution of these observation data is not limited to 250 m square, such as 500 m and 1 km square. Considering that HAPS will be used in many countries around the world and our rainfall attenuation prediction method will be applied to HAPS, the prediction accuracy of our prediction model using various input data with spatial resolutions other than 250 m square (i.e., prediction accuracy characteristics against resolution of input data) should be clarified. This paper evaluates the change in estimation accuracy when the resolution of the input data is decreased by a power of two, based on 250 m squares.
Keyword (in Japanese) (See Japanese page) 
(in English) Rain attenuation / CNN / HAPS / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 223, AP2023-87, pp. 1-6, Oct. 2023.
Paper # AP2023-87 
Date of Issue 2023-10-12 (AP) 
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 AP2023-87

Conference Information
Committee AP  
Conference Date 2023-10-19 - 2023-10-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Iwate University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Student Session, Antennas and Propagation 
Paper Information
Registration To AP 
Conference Code 2023-10-AP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on Prediction Accuracy against Resolution of Input Data in Rain Attenuation Prediction Model using Deep Learning 
Sub Title (in English)  
Keyword(1) Rain attenuation  
Keyword(2) CNN  
Keyword(3) HAPS  
Keyword(4)  
Keyword(5)  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Yuji Komatsuya  
1st Author's Affiliation Tokyo Denki University (TDU)
2nd Author's Name Tetsuro Imai  
2nd Author's Affiliation Tokyo Denki University (TDU)
3rd Author's Name Miyuki Hirose  
3rd Author's Affiliation Kyushu Institute of Technology (Kyutech)
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
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 ()
21st Author's Name  
21st Author's Affiliation ()
22nd Author's Name  
22nd Author's Affiliation ()
23rd Author's Name  
23rd Author's Affiliation ()
24th Author's Name  
24th Author's Affiliation ()
25th Author's Name  
25th Author's Affiliation ()
26th Author's Name / /
26th Author's Affiliation ()
()
27th Author's Name / /
27th Author's Affiliation ()
()
28th Author's Name / /
28th Author's Affiliation ()
()
29th Author's Name / /
29th Author's Affiliation ()
()
30th Author's Name / /
30th Author's Affiliation ()
()
31st Author's Name / /
31st Author's Affiliation ()
()
32nd Author's Name / /
32nd Author's Affiliation ()
()
33rd Author's Name / /
33rd Author's Affiliation ()
()
34th Author's Name / /
34th Author's Affiliation ()
()
35th Author's Name / /
35th Author's Affiliation ()
()
36th Author's Name / /
36th Author's Affiliation ()
()
Speaker Author-1 
Date Time 2023-10-19 10:00:00 
Presentation Time 120 minutes 
Registration for AP 
Paper # AP2023-87 
Volume (vol) vol.123 
Number (no) no.223 
Page pp.1-6 
#Pages 6 
Date of Issue 2023-10-12 (AP) 

[Return to Top Page]

[Return to IEICE Web Page]
The Institute of Electronics, Information and Communication Engineers (IEICE), Japan