牵引网微机保护整定的探讨

通过对牵引网负荷电流、短路电流中的谐波含量的分析和存在的谐波含量的不同点,利用两种电流中的综合谐波含量作为控制量,可以大大提高牵引网距离保护、高阻接地保护躲过牵引重负荷,再生负荷,启动电流及励磁涌流的能力,对于保护整定,通过探讨提出了整定原则和方法。     

铅锌选矿废水治理技术研究

本文通过对甘洛县境内的铅锌选矿废水的测试、分析以及治理工艺路线的研究，提出了一套高效、简便、可行经济的铅锌选矿废水治理技术方案，对其中的主要工艺单元设计参数进行了特别研究。     

提高牵引变电所故障测距装置精确度的对策

指出牵引变电所故障测距装置的精确度是动态变化的,应根据接触网、变电所设备及装置运行情况的变化及时调整故障测距整定值,以提高其精确度,提出电气化铁道复线并联供电时避免线路互感影响牵引变电所故障测距装置精确度的简便方法即复线单线化。     

Neptune轮机模拟器的功能及应用

介绍了NORCON公司研发的Neptune模拟器的主要功能及功能的设计方案,剖析了其搭建框架,分析其故障设置及评分体系的机理,从而阐述了先进模拟器的现状、发展水平及趋势。     

城轨交通定修车辆段静调列位设置方式探讨

介绍城轨车辆静调作业内容及场地标准,对其定修车辆段静调列位的3种设置方式进行了探讨,得出定修车辆段静调库宜独立设置,当双周三月检库能力富裕较多时可与其共库共线设置,不建议与定临修库共库设置的结论。     

隧道外挂设漏泄同轴电缆接口分析

山区铁路隧道外挂设漏泄同轴电缆的情况越来越多,漏缆隧道外挂设所涉及的参数、接口、设置要求等方面尚无可参照的规范标准,基于沪昆客运专线湖南段隧道群挂设漏缆的工程实例,总结隧道外漏缆挂设的工程接口、参数等信息经验,以供其他工程借鉴参考.     

城轨车站不同折返形式对折返能力影响分析

城轨交通车站列车折返形式不同,将直接影响车站的折返能力和车站渡线的设置方式。文章对城轨车站不同折返形式进行了分析,并以南京地铁为背景,探讨了车站不同列车折返形式对折返能力的影响,勾城轨车站渡线的设置提供依据。     

基于启发式Q(λ)学习的铁路绝缘子定位研究

智能化冲洗是解决电气化铁路绝缘子人工冲洗弊端的有效途径,而智能化冲洗的关键在于绝缘子识别、定位。通过对现有智能冲洗设备的分析,提出一种铁路绝缘子定位、跟踪方法,该方法基于传感器信息,采用启发式Q(λ)学习算法,可快速、准确找到绝缘子。首先建立铁路绝缘子水冲洗环境模型,接着为解决传统Q(λ)学习算法盲目学习导致收敛速度慢的问题,通过搜索支柱特征调整奖赏函数,设计启发式策略函数并融入Q(λ)学习中,提高算法的学习和收敛速度。最后分别对传统Q(λ)学习算法和启发式Q(λ)学习算法进行Matlab仿真实验,确定最佳参数设置,仿真结果表明启发式Q(λ)算法的正确性和可行性。     

高速公路施工中的测量工作

根据高速公路施工建设过程中的测量工作的特点全面介绍了施工前各测量工作的方法、步骤和施工期间公路中桩、边桩的计算和放样方法,并且针对不同情况提出了放样点的检核方法.     

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.