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A Neural Network Approach to Predicting Car Tyre Micro-Scale and Macro-Scale Behaviour 汽车轮胎微观和宏观行为预测的神经网络方法
智能学习系统与应用(英文) Pub Date : 2014-01-27 DOI: 10.4236/JILSA.2014.61002
Xiaoguang Yang, M. Behroozi, O. Olatunbosun
{"title":"A Neural Network Approach to Predicting Car Tyre Micro-Scale and Macro-Scale Behaviour","authors":"Xiaoguang Yang, M. Behroozi, O. Olatunbosun","doi":"10.4236/JILSA.2014.61002","DOIUrl":"https://doi.org/10.4236/JILSA.2014.61002","url":null,"abstract":"Finite Element (FE) analysis has become the favoured tool in the tyre industry for virtual development of tyres because of the ability to represent the detailed lay-up of the tyre carcass. However, application of FE analysis in tyre design and development is still very time-consuming and expensive. Here, the application of various Artificial Neural Network (ANN) architectures to predicting tyre performance is assessed to select the most effective and efficient architecture, to allow extensive parametric studies to be carried out inexpensively and to optimise tyre design before a much more expensive full FE analysis is used to confirm the predicted performance.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"6 1","pages":"11-20"},"PeriodicalIF":0.0,"publicationDate":"2014-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 20
SOMS: A Subway Operation and Maintenance System Based on Planned Maintenance Model with Train State 基于列车状态计划维修模型的地铁运维系统
智能学习系统与应用(英文) Pub Date : 2013-11-12 DOI: 10.4236/JILSA.2013.54021
Jianlong Ding, Yong Qin, L. Jia, Shiyou Zhu, Bo Yu
{"title":"SOMS: A Subway Operation and Maintenance System Based on Planned Maintenance Model with Train State","authors":"Jianlong Ding, Yong Qin, L. Jia, Shiyou Zhu, Bo Yu","doi":"10.4236/JILSA.2013.54021","DOIUrl":"https://doi.org/10.4236/JILSA.2013.54021","url":null,"abstract":"This paper aims to propose a modeling framework for subway operation and maintenance system (SOMS), which analyzes the train condition data based on both train sensor network data and basis train maintenance plan. The system is formulated into five function modules, and the research problem is to determine one auxiliary maintains plan, including the time allocation and frequency of maintenance. The case of Guangzhou metro is conducted to illustrate the applicability of SOMS, and the results reveal a number of interesting insights into subway maintenance system, i.e., the worksheet can reduce duplication of redundant maintenance work, the repair cost, and the damage caused by frequent disassembly.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"195-202"},"PeriodicalIF":0.0,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Design of Integrated Monitoring and Early Warning System of Urban Rail Transit Train Running State 城市轨道交通列车运行状态综合监测预警系统设计
智能学习系统与应用(英文) Pub Date : 2013-11-12 DOI: 10.4236/JILSA.2013.54022
Ting Yun, Gang Chen, F. Zhou, Y. Lu, Haiyu Li, Qian Li
{"title":"Design of Integrated Monitoring and Early Warning System of Urban Rail Transit Train Running State","authors":"Ting Yun, Gang Chen, F. Zhou, Y. Lu, Haiyu Li, Qian Li","doi":"10.4236/JILSA.2013.54022","DOIUrl":"https://doi.org/10.4236/JILSA.2013.54022","url":null,"abstract":"The monitoring and warning of urban rail transit is the core of operation management, and the breadth and depth of the monitoring range directly affect the quality of urban rail transit operation. For the current domestic monitoring system, most of the critical equipments and technologies are introduced from abroad; it is diseconomy, and also causes hidden danger. Realizing the localization of monitoring and early warning system is imperative. Based on the analysis of the present situation of urban rail transit operation safety at home and abroad, the paper proposes to use integrated technology to design basic framework of monitoring and warning system of urban rail train, and puts forward the critical technologies to realize the system. Compared with the existing monitoring system, the integrated monitoring system has the characteristics of wide monitoring range, clear division of labor, centralized management, coordination and integration operation and intelligent management, and embodies the concept of people-oriented. It has scientific significance for future construction of domestic Integrated Monitoring and Early Warning System (IMEWS) of urban rail transit.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"2013 1","pages":"203-210"},"PeriodicalIF":0.0,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Research on the Prediction Model for the Security Situation of Metro Station Based on PSO/SVM 基于PSO/SVM的地铁车站安全态势预测模型研究
智能学习系统与应用(英文) Pub Date : 2013-11-12 DOI: 10.4236/JILSA.2013.54028
Yong Qin, Zhenyu Zhang, Bo Chen, Z. Xing, Jing Liu, Jun Li
{"title":"Research on the Prediction Model for the Security Situation of Metro Station Based on PSO/SVM","authors":"Yong Qin, Zhenyu Zhang, Bo Chen, Z. Xing, Jing Liu, Jun Li","doi":"10.4236/JILSA.2013.54028","DOIUrl":"https://doi.org/10.4236/JILSA.2013.54028","url":null,"abstract":"Security situation awareness is a new \u0000technology about security. This paper brings it to the assessment of security \u0000situation of metro station which serves as a new way to secure the security of \u0000passengers as well as the operation of the metro station. This paper sets up an \u0000index system for assessing the security situation awareness and makes a \u0000prediction model for the security situation of metro station based on PSO/SVM \u0000after doing lots of researches and analyses. Furthermore, through case studies, we find that the \u0000model has high accuracy and ability to accurately predict the security \u0000situation of metro station in the future and a certain practical value.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"05 1","pages":"237-244"},"PeriodicalIF":0.0,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Detection and Diagnosis of Urban Rail Vehicle Auxiliary Inverter Using Wavelet Packet and RBF Neural Network 基于小波包和RBF神经网络的城市轨道车辆辅助逆变器检测与诊断
智能学习系统与应用(英文) Pub Date : 2013-11-12 DOI: 10.4236/JILSA.2013.54023
Guangwu Liu, Jingjing Long, Lingzhi Yang, Z. Su, Dechen Yao, Xiangli Zhong
{"title":"Detection and Diagnosis of Urban Rail Vehicle Auxiliary Inverter Using Wavelet Packet and RBF Neural Network","authors":"Guangwu Liu, Jingjing Long, Lingzhi Yang, Z. Su, Dechen Yao, Xiangli Zhong","doi":"10.4236/JILSA.2013.54023","DOIUrl":"https://doi.org/10.4236/JILSA.2013.54023","url":null,"abstract":"This study concerns with fault diagnosis of urban rail vehicle auxiliary inverter using wavelet packet and RBF neural network. Four statistical features are selected: standard voltage signal, voltage fluctuation signal, impulsive transient signal and frequency variation signal. In this article, the original signals are decomposed into different frequency subbands by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are taken as fault samples to train RBF neural network. The result shows that the RBF neural network is effective in the detection and diagnosis of various urban rail vehicle auxiliary inverter faults.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"211-215"},"PeriodicalIF":0.0,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Reliability Analysis of Metro Door System Based on FMECA 基于FMECA的地铁门系统可靠性分析
智能学习系统与应用(英文) Pub Date : 2013-11-12 DOI: 10.4236/JILSA.2013.54024
Xiaoqing Cheng, Z. Xing, Yong Qin, Y. Zhang, Shaohuang Pang, J. Xia
{"title":"Reliability Analysis of Metro Door System Based on FMECA","authors":"Xiaoqing Cheng, Z. Xing, Yong Qin, Y. Zhang, Shaohuang Pang, J. Xia","doi":"10.4236/JILSA.2013.54024","DOIUrl":"https://doi.org/10.4236/JILSA.2013.54024","url":null,"abstract":"The metro door system is one of the high failure rate subsystems of metro trains. The Failure Mode, Effects and Criticality Analysis (FMECA) method is applied to analyze the reliability of metro door system in this paper. Firstly, failure components of the door are statistically analyzed, and the major failure components are determined. Secondly, failures are classified according to their impacts on operation, and methods of calculating failure mode criticality and the related coefficients are illustrated. Finally, the FMECA is detailed in the selected 12 failure modes, and the failure modes are discovered that they have the most significant effect on metro door system. The obtained results can be used for optimal design and maintenance of the metro door system.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"216-220"},"PeriodicalIF":0.0,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
The Research of Urban Rail Transit Sectional Passenger Flow Prediction Method 城市轨道交通分段客流预测方法研究
智能学习系统与应用(英文) Pub Date : 2013-11-12 DOI: 10.4236/JILSA.2013.54026
Qian Li, Yong Qin, Zi-yang Wang, Z. Zhao, Minghui Zhan, Yu Liu, Zhiguo Li
{"title":"The Research of Urban Rail Transit Sectional Passenger Flow Prediction Method","authors":"Qian Li, Yong Qin, Zi-yang Wang, Z. Zhao, Minghui Zhan, Yu Liu, Zhiguo Li","doi":"10.4236/JILSA.2013.54026","DOIUrl":"https://doi.org/10.4236/JILSA.2013.54026","url":null,"abstract":"This paper studies the short-term prediction methods of sectional passenger flow, and selects BP neural network combined with the characteristics of sectional passenger flow itself. With a case study, we design three different schemes. We use Matlab to realize the prediction of the sectional passenger flow of the Beijing subway Line 2 and make comparative analysis. The empirical research shows that combining data characteristics of sectional passenger flow with the BP neural network have good prediction accuracy.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"05 1","pages":"227-231"},"PeriodicalIF":0.0,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Importance Analysis of Urban Rail Transit Network Station Based on Passenger 基于乘客的城市轨道交通网络站点重要性分析
智能学习系统与应用(英文) Pub Date : 2013-11-12 DOI: 10.4236/JILSA.2013.54027
J. Jin, Man Li, Yan-hui Wang, Lingxi Zhu, Li Ping, Boxuan Wang, Ping Li
{"title":"Importance Analysis of Urban Rail Transit Network Station Based on Passenger","authors":"J. Jin, Man Li, Yan-hui Wang, Lingxi Zhu, Li Ping, Boxuan Wang, Ping Li","doi":"10.4236/JILSA.2013.54027","DOIUrl":"https://doi.org/10.4236/JILSA.2013.54027","url":null,"abstract":"Current urban rail transit has become a major mode of transportation, and passenger is an important factor of urban rail transport, so this article is based on passenger and the degree of the road network structure, calculating the point intensity of stations of urban rail transit, and then reaching a station importance by integrating many point intensities in a survey cycle time, and getting the station importance of urban rail transit network through concrete examples.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"05 1","pages":"232-236"},"PeriodicalIF":0.0,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case 基于D-S证据理论的轻轨车辆悬架系统故障隔离及改进应用案例
智能学习系统与应用(英文) Pub Date : 2013-11-12 DOI: 10.4236/JILSA.2013.54029
Xiukun Wei, Kun Guo, L. Jia, Guangwu Liu, Minzheng Yuan
{"title":"Fault Isolation of Light Rail Vehicle Suspension System Based on D-S Evidence Theory and Improvement Application Case","authors":"Xiukun Wei, Kun Guo, L. Jia, Guangwu Liu, Minzheng Yuan","doi":"10.4236/JILSA.2013.54029","DOIUrl":"https://doi.org/10.4236/JILSA.2013.54029","url":null,"abstract":"This paper presents an innovative \u0000approach for the fault isolation of Light Rail Vehicle (LRV) suspension system \u0000based on the Dempster-Shafer (D-S) evidence theory and its improvement \u0000application case. The considered LRV has three rolling stocks and each one \u0000equips three sensors for monitoring the suspension system. A Kalman filter is \u0000applied to generate the residuals for fault diagnosis. For the purpose of fault \u0000isolation, a fault feature database is built in advance. The Eros and the norm \u0000distance between the fault feature of the new occurred fault and the one in the \u0000feature database are applied to measure the similarity of the feature which is \u0000the basis for the basic belief assignment to the fault, respectively. After the basic belief \u0000assignments are obtained, they are fused by using the D-S evidence theory. The \u0000fusion of the basic belief assignments increases the isolation accuracy \u0000significantly. The efficiency of the proposed method is demonstrated by two case studies.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"5 1","pages":"245-253"},"PeriodicalIF":0.0,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70330451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Preliminary Study on Selling Tickets in Reason for Last Trains on Beijing Rail Transit Network 北京轨道交通末班车合理售票初探
智能学习系统与应用(英文) Pub Date : 2013-11-12 DOI: 10.4236/JILSA.2013.54030
Yang Wang, Jie Xu, L. Jia, Jianyuan Guo, Ping Liang, Bochen Wang, Jinxin Xie
{"title":"Preliminary Study on Selling Tickets in Reason for Last Trains on Beijing Rail Transit Network","authors":"Yang Wang, Jie Xu, L. Jia, Jianyuan Guo, Ping Liang, Bochen Wang, Jinxin Xie","doi":"10.4236/JILSA.2013.54030","DOIUrl":"https://doi.org/10.4236/JILSA.2013.54030","url":null,"abstract":"With the increase of Beijing urban rail transport network, the structure \u0000of the road network is becoming more complex, and passengers have more travel \u0000options. Together with the complex paths and different timetables, taking the last train is \u0000becoming much more difficult and unsuccessful. To avoid losses, we \u0000propose feasible suggestions to the last train with reasonable selling tickets system.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":"05 1","pages":"254-260"},"PeriodicalIF":0.0,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70329996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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