整合铁路基础设施拓扑描述元素,从microL2到macroN0、L0级别的细节

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Network World Pub Date : 2023-01-01 DOI:10.14311/nnw.2023.33.002
Adam Hlubuček
{"title":"整合铁路基础设施拓扑描述元素,从microL2到macroN0、L0级别的细节","authors":"Adam Hlubuček","doi":"10.14311/nnw.2023.33.002","DOIUrl":null,"url":null,"abstract":"The paper presents the method of integration, which is supposed to be applied to the structure of the railway infrastructure topological description system expressed at the level of detail designated as microL2 in order to transform it into the structure expressed at the level of detail designated as macroN0,L0 . The microL2 level is the level of detail at which individual tracks in the structural sense and turnout branches are recognized, while the macroN0,L0 level is the level of individual operational points and line sections. The proposed integration algorithm takes into account both the parameter values of the individual elements appearing at the reference level of detail microL2 and their topological interconnectedness. Based on these aspects, these elements are integrated into the elements of the derived level of detail macroN0,L0 that can be described by the transformed parameter values. The relations between the respective elements are also transformed accordingly. While describing the transformation algorithm, the terminology and principles of the UIC RailTopoModel are used.","PeriodicalId":49765,"journal":{"name":"Neural Network World","volume":"1 1","pages":""},"PeriodicalIF":0.7000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integration of railway infrastructure topological description elements from the microL2 to the macroN0,L0 level of detail\",\"authors\":\"Adam Hlubuček\",\"doi\":\"10.14311/nnw.2023.33.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents the method of integration, which is supposed to be applied to the structure of the railway infrastructure topological description system expressed at the level of detail designated as microL2 in order to transform it into the structure expressed at the level of detail designated as macroN0,L0 . The microL2 level is the level of detail at which individual tracks in the structural sense and turnout branches are recognized, while the macroN0,L0 level is the level of individual operational points and line sections. The proposed integration algorithm takes into account both the parameter values of the individual elements appearing at the reference level of detail microL2 and their topological interconnectedness. Based on these aspects, these elements are integrated into the elements of the derived level of detail macroN0,L0 that can be described by the transformed parameter values. The relations between the respective elements are also transformed accordingly. While describing the transformation algorithm, the terminology and principles of the UIC RailTopoModel are used.\",\"PeriodicalId\":49765,\"journal\":{\"name\":\"Neural Network World\",\"volume\":\"1 1\",\"pages\":\"\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Network World\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.14311/nnw.2023.33.002\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Network World","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.14311/nnw.2023.33.002","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

本文提出了一种集成方法,拟将其应用于以microL2为细节层次表示的铁路基础设施拓扑描述系统的结构,将其转化为以macroN0,L0为细节层次表示的结构。微l2级别是结构意义上的单个轨道和道岔分支被识别的细节级别,而宏0,L0级别是单个操作点和线路部分的级别。所提出的积分算法既考虑了出现在细节microL2参考层的单个元素的参数值,也考虑了它们的拓扑互联性。基于这些方面,将这些元素集成到派生的详细级别macroN0,L0的元素中,这些元素可以通过转换后的参数值来描述。各个元素之间的关系也相应地进行了转换。在描述转换算法时,使用了UIC railtopomomodel的术语和原理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Integration of railway infrastructure topological description elements from the microL2 to the macroN0,L0 level of detail
The paper presents the method of integration, which is supposed to be applied to the structure of the railway infrastructure topological description system expressed at the level of detail designated as microL2 in order to transform it into the structure expressed at the level of detail designated as macroN0,L0 . The microL2 level is the level of detail at which individual tracks in the structural sense and turnout branches are recognized, while the macroN0,L0 level is the level of individual operational points and line sections. The proposed integration algorithm takes into account both the parameter values of the individual elements appearing at the reference level of detail microL2 and their topological interconnectedness. Based on these aspects, these elements are integrated into the elements of the derived level of detail macroN0,L0 that can be described by the transformed parameter values. The relations between the respective elements are also transformed accordingly. While describing the transformation algorithm, the terminology and principles of the UIC RailTopoModel are used.
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
Neural Network World
Neural Network World 工程技术-计算机:人工智能
CiteScore
1.80
自引率
0.00%
发文量
0
审稿时长
12 months
期刊介绍: Neural Network World is a bimonthly journal providing the latest developments in the field of informatics with attention mainly devoted to the problems of: brain science, theory and applications of neural networks (both artificial and natural), fuzzy-neural systems, methods and applications of evolutionary algorithms, methods of parallel and mass-parallel computing, problems of soft-computing, methods of artificial intelligence.
期刊最新文献
Water quality image classification for aquaculture using deep transfer learning Enhanced QOS energy-efficient routing algorithm using deep belief neural network in hybrid falcon-improved ACO nature-inspired optimization in wireless sensor networks Vibration analyses of railway systems using proposed neural predictors A self-adaptive deep learning-based model to predict cloud workload Integration of railway infrastructure topological description elements from the microL2 to the macroN0,L0 level of detail
×
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