基于机器学习的投票回归法,用于估算轮轨接触时的附着力

IF 3.5 2区 工程技术 Q1 ENGINEERING, MECHANICAL
Abdulkadir Zirek, Can Uysal
{"title":"基于机器学习的投票回归法,用于估算轮轨接触时的附着力","authors":"Abdulkadir Zirek, Can Uysal","doi":"10.1080/00423114.2024.2390578","DOIUrl":null,"url":null,"abstract":"The majority of control methodologies for railway vehicles depend on adhesion data to attain optimal traction. Therefore, researchers have been actively investigating practical and feasible approac...","PeriodicalId":49385,"journal":{"name":"Vehicle System Dynamics","volume":"146 1","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A machine learning based voting regression method for adhesion estimation in wheel-rail contact\",\"authors\":\"Abdulkadir Zirek, Can Uysal\",\"doi\":\"10.1080/00423114.2024.2390578\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The majority of control methodologies for railway vehicles depend on adhesion data to attain optimal traction. Therefore, researchers have been actively investigating practical and feasible approac...\",\"PeriodicalId\":49385,\"journal\":{\"name\":\"Vehicle System Dynamics\",\"volume\":\"146 1\",\"pages\":\"\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Vehicle System Dynamics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/00423114.2024.2390578\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Vehicle System Dynamics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/00423114.2024.2390578","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0

摘要

铁路车辆的大多数控制方法都依赖于附着力数据来获得最佳牵引力。因此,研究人员一直在积极研究切实可行的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A machine learning based voting regression method for adhesion estimation in wheel-rail contact
The majority of control methodologies for railway vehicles depend on adhesion data to attain optimal traction. Therefore, researchers have been actively investigating practical and feasible approac...
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
Vehicle System Dynamics
Vehicle System Dynamics 工程技术-工程:机械
CiteScore
8.40
自引率
13.90%
发文量
110
审稿时长
3 months
期刊介绍: Vehicle System Dynamics is an international journal, providing a source of information for the vehicle engineer and the applied scientist. The journal emphasizes the theoretical background of research and development problems of all kinds of road, rail and other ground-based vehicles. Main topics are: Dynamics of vehicle systems and their components including suspension, steering, braking, chassis systems, noise-vibration-harshness, power train; Control of motion and forces affecting vehicle behaviour; Computer aided modelling and simulation, validation, parameter identification and testing, driver modelling; Vehicle interactions with the environment including wheel-rail and tyre-ground behaviour; Active Safety Systems including collision and derailment warning, avoidance and mitigation; Intelligent vehicles, guided vehicles, automated traffic systems related to vehicle dynamics, unconventional vehicles.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信