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科技导报 |专题:人工智能 2018 , 36 (17) : 83 -90
深度学习在海洋大数据挖掘中的应用
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孙苗, 符昱, 吕憧憬, 姜晓轶
作者信息
    国家海洋信息中心, 国家海洋局数字海洋科学技术重点实验室, 天津 300171
通讯作者:
姜晓轶(通信作者),研究员,研究方向为海洋信息化技术,电子信箱:andyjiangxy@126.com
作者简介:
孙苗,博士,研究方向为海洋大数据挖掘分析,电子信箱:miaosun_public@163.com
Deep learning application in marine big data mining
  • SUN Miao, FU Yu, LÜ Chongjing, JIANG Xiaoyi
  • Affiliations
      National Marine Data and Information Service;Key Laboratory of Digital Ocean of State Oceanic Administration, Tianjin 300171, China
    出版时间: 2018-09-13 doi: 10.3981/j.issn.1000-7857.2018.17.010
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    介绍了深度学习的关键发展节点和应用发展历程,分析了深度学习在国内外主要领域的发展现状;概述了多个深度学习的关键算法原理,分析了深度学习在海洋数据重构、分类识别和预测等海洋大数据挖掘中的相关应用;提出了深度学习未来可能面临的问题,并从加强顶层设计、信息安全和强化算法鲁棒性等方面,展望了深度学习在海洋大数据挖掘中的应用前景。
    深度学习  /  海洋大数据  /  数据挖掘
    We introduce the development and application background of deep learning and analyze the state-of-the-art deep learning technology in related domains. The key algorithms of deep learning and their related applications in the marine field are given from the aspects of marine data reconstruction, classification, and prediction. Furthermore, we also discuss the potential problems of the deep learning application in marine big data mining. The prospect of deep learning application is discussed in terms of optimizing the mechanism, strengthening information security and intensifying algorithm robustness.
    deep learning  /  marine big data  /  data mining
    孙苗, 符昱, 吕憧憬, 姜晓轶. 深度学习在海洋大数据挖掘中的应用. 科技导报, 2018 , 36 (17) : 83 -90 . DOI: 10.3981/j.issn.1000-7857.2018.17.010
    SUN Miao, FU Yu, LÜ Chongjing, JIANG Xiaoyi. Deep learning application in marine big data mining[J]. Science & Technology Review, 2018 , 36 (17) : 83 -90 . DOI: 10.3981/j.issn.1000-7857.2018.17.010

      国家重点研发计划项目(2016YFC1401900)

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    2018年第36卷第17期
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    doi: 10.3981/j.issn.1000-7857.2018.17.010
    • 接收时间:2018-06-01
    • 首发时间:2018-09-18
    • 出版时间:2018-09-13
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    • 收稿日期:2018-06-01
    • 修回日期:2018-08-29
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    通讯作者:

    姜晓轶(通信作者),研究员,研究方向为海洋信息化技术,电子信箱:andyjiangxy@126.com
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    Family
    属数
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    Number of
    species
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    Percentage of
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    Genus
    种数
    Number of
    species
    占总种数比例
    Percentage of total
    species (%)
    鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
    小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
    多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
    红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
    小菇属 Mycena 11 5.26
    光柄菇属 Pluteus 5 2.39
    红菇属 Russula 17 8.13
    栓菌属 Trametes 5 2.39
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