[1]雷进宇,初秀民,蒋仲廉,等.内河船舶自动识别系统异常数据的可视分析[J].哈尔滨工程大学学报,2020,41(6):840-845.[doi:10.11990/jheu.201901017]
 LEI Jinyu,CHU Xiumin,JIANG Zhonglian,et al.Abnormal automatic identification system data by visual analytics[J].hebgcdxxb,2020,41(6):840-845.[doi:10.11990/jheu.201901017]
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内河船舶自动识别系统异常数据的可视分析(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
41
期数:
2020年6期
页码:
840-845
栏目:
出版日期:
2020-06-05

文章信息/Info

Title:
Abnormal automatic identification system data by visual analytics
作者:
雷进宇12 初秀民123 蒋仲廉12 钟诚12 吴明洋14 郭涛12
1. 国家水运安全工程技术研究中心, 湖北 武汉 430063;
2. 武汉理工大学 能源与动力工程学院, 湖北 武汉 430063;
3. 闽江学院 物理与电子信息工程学院, 福建 福州 350108;
4. 武汉理工大学 物流工程学院, 湖北 武汉, 430063
Author(s):
LEI Jinyu12 CHU Xiumin123 JIANG Zhonglian12 ZHONG Cheng12 WU Mingyang14 GUO Tao12
1. National Engineering Research Center for Water Transport Safety, Wuhan 430063, China;
2. School of Energy and Power Engineering, Wuhan University of Technology, Wuhan 430063, China;
3. Department of Physics and Electronic Information Engineering, Minjiang University, Fuzhou 350108, China;
4. School of Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
关键词:
水路运输异常数据可视分析自动识别系统内河船舶通航环境脏数据数据丢失
分类号:
P208
DOI:
10.11990/jheu.201901017
文献标志码:
A
摘要:
内河水路运输由于其特殊的通航环境,导致内河船舶的自动识别系统数据中存在着大量异常数据。针对传统异常数据处理常用的数据剔除和数据恢复方法中对原始数据造成的资源浪费问题,本文利用可视分析方法实现资源重复利用,帮助分析人员对长江内河的"脏"数据存在的异常模式和导致其异常的内河环境因素进行探索。结合散点图来反映轨迹点间的时空距离的查分从而挑选船舶自动识别系统中存在数据丢失和偏移现象的"脏"数据,对所有被标记为脏数据的船舶自动识别系统轨迹点展示在OpenStreetMap地图,利用内河中的实际案例对可视分析方法进行实证测试。通过自动识别系统数据的基站覆盖范围分析,船舶自动识别系统的数据偏移致因分析等结果展示可视分析方法在船舶自动识别系统异常数据挖掘中具有适用意义价值。

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-01-05。
基金项目:国家自然科学基金项目(51479155);福建省自然科学基金项目(2018J0106);福建省教育厅中青年教师教育科研项目(JAT170439).
作者简介:雷进宇,男,博士研究生;初秀民,男,研究员,博士生导师;郭涛,男,博士研究生.
通讯作者:郭涛,E-mail:1940856771@qq.com.
更新日期/Last Update: 2020-07-22