构建用于船舶碰撞事故分析的事件图,改善海上交通安全

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Complexity Pub Date : 2024-06-25 DOI:10.1155/2024/4998195
Jun Ma, Yang Wang, Liguang Wang, Luhui Xu, Jiong Zhao
{"title":"构建用于船舶碰撞事故分析的事件图,改善海上交通安全","authors":"Jun Ma,&nbsp;Yang Wang,&nbsp;Liguang Wang,&nbsp;Luhui Xu,&nbsp;Jiong Zhao","doi":"10.1155/2024/4998195","DOIUrl":null,"url":null,"abstract":"<div>\n <p>At present, there are three main methods for analyzing the causes of ship collision accidents: statistical analysis, accident causation models, and knowledge graphs. With the deepening of research, the analysis methods pay more attention to the objective correlation between various factors of the accident, and the analysis results obtained are more objective and accurate. On this basis, this paper proposes a method for analyzing the contribution degree of different causes and accident conduction paths in ship collision accidents based on the construction of the Ship Collision Accidents Event Graph (SCAEG). Firstly, the ontology is constructed based on the grounded theory. Secondly, events and relationships are extracted after fine-tuning the UIE model. Thirdly, the SCAEG is constructed after event coreference resolution. Finally, this research conducts the contribution degree analysis, accident conduction path analysis, and accident spatial distribution analysis based on SCAEG. The advantages of this method include the following: (i) it can construct a more complete and accurate ontology; (ii) adopting this approach can unify various information extraction tasks and achieve good results based on small sample annotation data; and (iii) using this method, we can conduct contribution degree analysis of different causes, accident conduction path analysis, and spatial distribution analysis. Experimental evidence demonstrates the effectiveness of this method. The analytical results obtained from the experiments can provide assistant decision-making for relevant departments to reduce the occurrence of ship collision accidents and improve maritime traffic safety.</p>\n </div>","PeriodicalId":50653,"journal":{"name":"Complexity","volume":"2024 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4998195","citationCount":"0","resultStr":"{\"title\":\"Construction of Event Graph for Ship Collision Accident Analysis to Improve Maritime Traffic Safety\",\"authors\":\"Jun Ma,&nbsp;Yang Wang,&nbsp;Liguang Wang,&nbsp;Luhui Xu,&nbsp;Jiong Zhao\",\"doi\":\"10.1155/2024/4998195\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n <p>At present, there are three main methods for analyzing the causes of ship collision accidents: statistical analysis, accident causation models, and knowledge graphs. With the deepening of research, the analysis methods pay more attention to the objective correlation between various factors of the accident, and the analysis results obtained are more objective and accurate. On this basis, this paper proposes a method for analyzing the contribution degree of different causes and accident conduction paths in ship collision accidents based on the construction of the Ship Collision Accidents Event Graph (SCAEG). Firstly, the ontology is constructed based on the grounded theory. Secondly, events and relationships are extracted after fine-tuning the UIE model. Thirdly, the SCAEG is constructed after event coreference resolution. Finally, this research conducts the contribution degree analysis, accident conduction path analysis, and accident spatial distribution analysis based on SCAEG. The advantages of this method include the following: (i) it can construct a more complete and accurate ontology; (ii) adopting this approach can unify various information extraction tasks and achieve good results based on small sample annotation data; and (iii) using this method, we can conduct contribution degree analysis of different causes, accident conduction path analysis, and spatial distribution analysis. Experimental evidence demonstrates the effectiveness of this method. The analytical results obtained from the experiments can provide assistant decision-making for relevant departments to reduce the occurrence of ship collision accidents and improve maritime traffic safety.</p>\\n </div>\",\"PeriodicalId\":50653,\"journal\":{\"name\":\"Complexity\",\"volume\":\"2024 1\",\"pages\":\"\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/4998195\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Complexity\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1155/2024/4998195\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complexity","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/2024/4998195","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

目前,分析船舶碰撞事故原因的方法主要有三种:统计分析法、事故成因模型法和知识图谱法。随着研究的深入,分析方法更加注重事故各因素之间的客观关联性,得到的分析结果也更加客观准确。在此基础上,本文提出了一种基于船舶碰撞事故事件图(SCAEG)构建的船舶碰撞事故中不同原因和事故传导路径的贡献度分析方法。首先,基于基础理论构建本体。其次,在微调 UIE 模型后提取事件和关系。第三,在事件核心参照解析后构建 SCAEG。最后,本研究基于 SCAEG 进行贡献度分析、事故传导路径分析和事故空间分布分析。该方法的优点如下(i)可以构建更完整、更准确的本体;(ii)采用这种方法可以统一各种信息提取任务,并在小样本标注数据的基础上取得良好效果;(iii)利用这种方法,我们可以进行不同原因的贡献度分析、事故传导路径分析和空间分布分析。实验证明了该方法的有效性。实验得出的分析结果可为相关部门提供辅助决策,减少船舶碰撞事故的发生,提高海上交通安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Construction of Event Graph for Ship Collision Accident Analysis to Improve Maritime Traffic Safety

At present, there are three main methods for analyzing the causes of ship collision accidents: statistical analysis, accident causation models, and knowledge graphs. With the deepening of research, the analysis methods pay more attention to the objective correlation between various factors of the accident, and the analysis results obtained are more objective and accurate. On this basis, this paper proposes a method for analyzing the contribution degree of different causes and accident conduction paths in ship collision accidents based on the construction of the Ship Collision Accidents Event Graph (SCAEG). Firstly, the ontology is constructed based on the grounded theory. Secondly, events and relationships are extracted after fine-tuning the UIE model. Thirdly, the SCAEG is constructed after event coreference resolution. Finally, this research conducts the contribution degree analysis, accident conduction path analysis, and accident spatial distribution analysis based on SCAEG. The advantages of this method include the following: (i) it can construct a more complete and accurate ontology; (ii) adopting this approach can unify various information extraction tasks and achieve good results based on small sample annotation data; and (iii) using this method, we can conduct contribution degree analysis of different causes, accident conduction path analysis, and spatial distribution analysis. Experimental evidence demonstrates the effectiveness of this method. The analytical results obtained from the experiments can provide assistant decision-making for relevant departments to reduce the occurrence of ship collision accidents and improve maritime traffic safety.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
期刊最新文献
Quantification of the Synergistic Inhibitory Effects of an Oncolytic Herpes Virus Plus Paclitaxel on Anaplastic Thyroid Cancer Cells Performance Evaluation of Control Strategies for Autonomous Quadrotors: A Review Chaotic Image Encryption Scheme Based on Improved Z-Order Curve, Modified Josephus Problem, and RNA Operations: An Experimental Li-Fi Approach Finite-Time Boundedness of Conformable Faulty Fuzzy Systems With Time Delay Spatiotemporal Differences in Regional Tourism Efficiency: An Empirical Study From Guangdong Province, China
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1