Proposal on rain attenuation prediction method using convolutional neural network

被引：0
|
作者：
Komatsuya, Yuji [1 ]
Imai, Tetsuro [1 ]
Hirose, Miyuki [2 ]
机构：
[1] Tokyo Denki Univ, Dept Informat & Commun Engn, Adachi Ku, Tokyo 1208551, Japan
[2] Kyushu Inst Technol, Dept Elect Engn & Elect, Tobata Ku, Kitakyushu Shi, Fukuoka 8048550, Japan
来源：
IEICE COMMUNICATIONS EXPRESS | 2024年 / 13卷 / 06期
关键词：
rain attenuation; convolutional neural network; deep learning;
D O I：
10.23919/comex.2024SPL0015
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Recently, the practical application of HAPS (High Altitude Platform Station) as the next -generation communication platform is studied actively. HAPS employs adaptive rain attenuation countermeasure techniques such as site diversity methods, therefore it is ideal to predict rain attenuation on the path in real time. We proposed real-time rain attenuation prediction method by convolutional neural network that inputs image of rainfall rate and path distance. Result showed that prediction accuracy of our proposed method is better than a method using conventional formulas.
引用
收藏
页码：181 / 184
页数：4
相关论文
共 50 条
  • [31] A Pre-routing Net Wirelength Prediction Method Using an Optimized Convolutional Neural Network
    Watanabe, Ryota
    Katsuda, Yuki
    Zhao, Qian
    Yoshida, Takaichi
    2019 SEVENTH INTERNATIONAL SYMPOSIUM ON COMPUTING AND NETWORKING WORKSHOPS (CANDARW 2019), 2019, : 115 - 120
  • [32] Convolutional Neural Network for Trajectory Prediction
    Nikhil, Nishant
    Morris, Brendan Tran
    COMPUTER VISION - ECCV 2018 WORKSHOPS, PT III, 2019, 11131 : 186 - 196
  • [33] A Convolutional Neural Network Based Ensemble Method for Cancer Prediction Using DNA Methylation Data
    Xia, Chao
    Xiao, Yawen
    Wu, Jun
    Zhao, Xiaodong
    Li, Hua
    ICMLC 2019: 2019 11TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING, 2019, : 191 - 196
  • [34] Architecture proposal for data extraction of chart images using Convolutional Neural Network
    Chagas, Paulo
    Freitas, Alexandre
    Daisuke, Rafael
    Miranda, Brunelli
    Araujo, Tiago
    Santos, Carlos
    Meiguins, Bianchi
    Morais, Jefferson
    2017 21ST INTERNATIONAL CONFERENCE INFORMATION VISUALISATION (IV), 2017, : 318 - 323
  • [35] GIS Fault Rate Prediction Method Based on Convolutional Neural Network
    Cui, Guodong
    Liu, Guanghui
    Wang, Jianjun
    Zhang, Zhaoqi
    2020 5TH ASIA CONFERENCE ON POWER AND ELECTRICAL ENGINEERING (ACPEE 2020), 2020, : 21 - 25
  • [36] Mechanical Properties Prediction for Hot Rolled Alloy Steel Using Convolutional Neural Network
    Xu, Zhi-Wei
    Liu, Xiao-Ming
    Zhang, Kai
    IEEE ACCESS, 2019, 7 : 47068 - 47078
  • [37] Prediction Method of Underground Density Abnormal Body in Gravity Data Based on Convolutional Neural Network
    Wang, Rui
    Wang, Chi
    Xu, Zhengwei
    Li, Hua
    Zhang, Yuxin
    Li, Qiang
    Wang, Ziyue
    20TH INT CONF ON UBIQUITOUS COMP AND COMMUNICAT (IUCC) / 20TH INT CONF ON COMP AND INFORMATION TECHNOLOGY (CIT) / 4TH INT CONF ON DATA SCIENCE AND COMPUTATIONAL INTELLIGENCE (DSCI) / 11TH INT CONF ON SMART COMPUTING, NETWORKING, AND SERV (SMARTCNS), 2021, : 190 - 195
  • [38] Automatic method for classification of groundnut diseases using deep convolutional neural network
    Vaishnnave, M. P.
    Devi, K. Suganya
    Ganeshkumar, P.
    SOFT COMPUTING, 2020, 24 (21) : 16347 - 16360
  • [39] Automatic method for classification of groundnut diseases using deep convolutional neural network
    M. P. Vaishnnave
    K. Suganya Devi
    P. Ganeshkumar
    Soft Computing, 2020, 24 : 16347 - 16360
  • [40] Classification of Arabic Alphabets Using a Combination of a Convolutional Neural Network and the Gradient Method
    El Atillah, Mouhssine
    El Fazazy, Khalid
    Riffi, Jamal
    BAGHDAD SCIENCE JOURNAL, 2024, 21 (01) : 252 - 260
12345