中文版 | English
Title

基于Resnet的雷电电场波形识别与分类

Alternative Title
Recognition and Classification of Lightning Electric Field Waveform Based on Resnet
Author
Publication Years
2023
DOI
Source Title
ISSN
1006-9348
Volume40Issue:7Pages:244-248
Abstract
雷电更多时候会给人们的生产生活带来灾难性的打击,因此雷电的观测与预警作为防雷减灾工作的基础和前提就显得格外重要.在人工智能技术不断发展的背景下,图像识别作为人工智能的一个重要分支,已在各个领域得到了广泛应用.基于雷电发生时所收集的电场波形图像数据,利用卷积神经网络构建了图像识别模型,根据不同类型雷电电场波形的特征作为数据集分类的判定标准且保证数据量充足,对网络模型进行逐步优化以提高图像的识别率,最终实现雷电波形的快速分类,获取雷电定位信息达到实时定位的目的.
Keywords
URL[Source Record]
Language
Chinese
SUSTech Authorship
Others
Funding Project
:中科院大气物理研究所夏季在北京地区进行雷电探测网观测的所有参与人员及相关单位和老师 ; GML2019ZD0203:南方海洋科学与工程广东省实验室(广州)人才团队引进重大专项 ; KQTD20170810111725321:深圳市科技计划资助项目
Data Source
WanFang
WanFangID
jsjfz202307047
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/571377
Affiliation
1.北京信息科技大学,北京 100192
2.南方海洋科学与工程广东省实验室(广州),广东 广州511458
3.南方科技大学,广东 深圳518055
Recommended Citation
GB/T 7714
张潇艺,王彩霞,田杨萌,等. 基于Resnet的雷电电场波形识别与分类[J]. 计算机仿真,2023,40(7):244-248.
APA
张潇艺,王彩霞,田杨萌,&杨辉.(2023).基于Resnet的雷电电场波形识别与分类.计算机仿真,40(7),244-248.
MLA
张潇艺,et al."基于Resnet的雷电电场波形识别与分类".计算机仿真 40.7(2023):244-248.
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