浅析地铁数字视频系统中的多级架构

对地铁安全防范系统中的视频监控子系统结构单一架构和多级架构进行比较;采用多级架构有利于构建稳定可靠的城市地铁监控系统,是整体联网联调的最佳解决方案;对数字视频系统多级系统架构关键技术进行详细描述.     

基于监控数据挖掘的运行图参数查定与修正方法

采用基于统计的异常数据处理方法对列车运行监控装置记录的海量列车运行实测数据进行处理,剔出其中的异常数据,设计数据补齐算法补齐缺失的数据;应用统计分析方法,在分析实测数据的集中趋势、离散程度、分布形态等规律和按照列车种类对数据进行时距分组的基础上,建立能够合理反映数据变化趋势的拟合曲线回归模型,用于参数的修正。运用给出的数据处理方法和拟合曲线回归模型开发出基于实测数据的运行图参数查定与修正系统。以大同—准格尔铁路为例进行验证,结果表明该方法及系统提高了参数的计算精度和查定效率,并且简便易行,可操作性强。     

汽车发动机液压悬置研究方法探讨

发动机液压悬置是非线性很强的隔振元件,其动特性因激振频率和激振振幅的改变而改变。试验分析和理论研究是研究液压悬置的两种基本方法。文中通过分析典型液压悬置的结构特征,全面总结液压悬置的试验方法、理论模型和优化设计方法等方面的研究现状,分析了将试验研究和理论分析相结合、采用系统参数识别方法对液压悬置进行研究的可行性,并探讨了最优试验设汁准则。     

汽车企业RD数据质量的提升方法

提高科技统计数据的质量,就要保证R＆D数据的准确性、及时性、完整性和适用性.R＆D数据质量的准确度,直接影响着科技统计工作的成效,对科技决策和科技创新有着不可估量的影响.文章归纳了影响R＆D数据质量的因素,针对汽车企业R＆D数据采集现状及存在的问题,提出提高科技统计数据质量的途径,并举例说明判断数据属性的概念,同时阐述了数据折算的方法和理论依据.     

基于环形线圈检测器采集信息的交通状态分类方法应用研究

在智能交通系统中,交通状态判别算法通常被用来进行道路环境中实时交通状态的判断。这些算法将外场设备采集到的实时交通流数据与既有的交通状态分类标准特征作比较,来识别交通系统运行的状态。应用聚类分析方法,结合数据预备技术和交通工程技术,对环形线圈监测系统采集的交通流基础特征数据进行挖掘,实现了一种交通状态分类方法,并对交通管理控制系统中实时交通状态的判断识别提供可靠的参照标准。     

Understanding travellers’ preferences for different types of trip destination based on mobile internet usage data

New mobility data sources like mobile phone traces have been shown to reveal individuals’ movements in space and time. However, socioeconomic attributes of travellers are missing in those data. Consequently, it is not possible to partition the population and have an in-depth understanding of the socio-demographic factors influencing travel behaviour. Aiming at filling this gap, we use mobile internet usage behaviour, including one’s preferred type of website and application (app) visited through mobile internet as well as the level of usage frequency, as a distinguishing element between different population segments. We compare the travel behaviour of each segment in terms of the preference for types of trip destinations. The point of interest (POI) data are used to cluster grid cells of a city according to the main function of a grid cell, serving as a reference to determine the type of trip destination. The method is tested for the city of Shanghai, China, by using a special mobile phone dataset that includes not only the spatial-temporal traces but also the mobile internet usage behaviour of the same users. We identify statistically significant relationships between a traveller’s favourite category of mobile internet content and more frequent types of trip destinations that he/she visits. For example, compared to others, people whose favourite type of app/website is in the “tourism” category significantly preferred to visit touristy areas. Moreover, users with different levels of internet usage intensity show different preferences for types of destinations as well. We found that people who used mobile internet more intensively were more likely to visit more commercial areas, and people who used it less preferred to have activities in predominantly residential areas.     

An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition,planning and cost

National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment.Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain.Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel integrated system for automatic job scheduling is presented; from concept formulation to the examination of the data to information transitional level interface, and at the decision making level. The underlying architecture configures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value.A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise degradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum scheduling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account specific cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The demonstrator structure was found fit for purpose with logical component relationships, offering further scope for research and commercial exploitation.