文章摘要
吕玉娇,尹 力,刘崇磊,黄海宁.基于改进变分模态分解的北极海域声速剖面分类*[J].,2021,40(3):415-421
基于改进变分模态分解的北极海域声速剖面分类*
Sound speed profile classification of Arctic sea area based on improved VMD
投稿时间:2020-07-25  修订日期:2021-04-16
中文摘要:
      应用支持向量机对北极声速剖面进行分类,特征量提取是关键。本文采用一种基于经验模态分解的改进变分模态分解算法(IEVMD),以准确提取声速剖面特征量。算法首先对声速剖面信号进行经验模态分解,依据最大类间方差原则划分各分量边际谱主频带,以相似度作为最小分解层数判断标准,获得最小分解层数,进行变分模态分解。对北极区海水声速实测数据(信号)处理表明,该方法可有效提取信号经验模态分解各分量的希尔伯特边际谱特征,进行支持向量机分类,实现对北极海域声速剖面的分类识别,解决以往人工分类耗时久的问题。
英文摘要:
      Using support vector machine to classify the North Pole sound velocity profile, feature extraction is the key. In this paper, an improved variational mode decomposition algorithm (IEVMD) based on empirical mode decomposition is used to extract the sound velocity profile features accurately. The algorithm firstly conducts empirical mode decomposition for the sound velocity profile signal, divides the main frequency band of the marginal spectrum of each component according to the principle of maximum intercategory variance, takes similarity as the judgment standard of the minimum decomposition layer number, obtains the minimum decomposition layer number, and performs the variational mode decomposition. The processing of measured acoustic velocity data (signals) shows that this method can effectively extract the Hilbert marginal spectrum characteristics of the empirical mode decomposition of signals, carry out support vector machine classification, realize the classification and identification of acoustic velocity profiles in the Arctic sea area, and solve the problem of time-consuming artificial classification in the past.
DOI:10.11684/j.issn.1000-310X.2021.03.013
中文关键词: 北极  声速剖面  最大类间方差  变分模态分解  支持向量机
英文关键词: Arctic  Sound speed profile  Otsu  Variational Mode Decomposition  Support Vector Machine
基金项目:
作者单位E-mail
吕玉娇 中国科学院声学研究所 北京 中国科学院先进水下信息技术重点实验室 北京 lvyujiao@mail.ioa.ac.cn 
尹 力 中国科学院声学研究所  
刘崇磊 中国科学院声学研究所  
黄海宁 中国科学院声学研究所  
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