用于汽车ADAS系统测试的软目标车研究进展

软目标车在汽车高级驾驶辅助系统(Advanced Driving Assistance System,ADAS)测试中发挥着重要作用,它提高了试验的安全性和效率。介绍了软目标车研发的关键技术和研究进展,通过分析乘用车的电磁散射特性、视觉及光学特性,论述了软目标车对于毫米波雷达、视觉传感器以及激光雷达在特征识别方面的设计要求,重点阐述了软目标车对毫米波雷达的探测及识别目标的响应特征——雷达散射截面(Radar Cross Section,RCS)的特征分布及其测量和数据处理方法,为软目标车的开发和使用提供参考依据。     

磁道钉导航的横向偏差等值线及其应用研究

阐述了基于磁道钉导航的车辆定位原理;利用磁道钉磁感应强度公式计算磁道钉周围磁场,生成以磁感应强度X和Z方向分量为横、纵轴的横向偏差等值线方程;基于此等值线方程,得到磁感应强度X,Z方向分量数据对所对应的横向偏差的计算方法,并给出实例验证.     

CX55B型挖掘机油液监测系统设计

针对CX55B型挖掘机的油液监测技术上存在的问题,提出了利用AVR处理器控制激光产生脉冲,采用双光路光纤传感器进行信号调制,然后通过对数和差动放大后输入AVR进行处理得到油液污染信息的设计方法。改进后的监测系统较好地提高了测量精度。此方法也适用于其他同类监测技术的挖掘机。     

传感器在水利枢纽岩土工程安全监测中的应用

介绍了各类传感器在水利枢纽岩土工程自动化安全监测中的作用、监测内容和工作原理,分析了自动化安全监测中传感器常易出现的故障,为传感器自动检测装置的设计和施工提供参考。     

基于简单位置传感器永磁电机矢量控制策略的研究

本文给出基于速度保持和加速度保持估计转子转速和转角的两种方法,并且在MATLAB/SIMULINK环境下对调速系统进行了起动过程的仿真,给出仿真结果和分析,验证了方法的可行性.     

BOTDR传感技术在桥梁移动荷载识别中的应用

文章针对我国桥梁移动荷载识别的测试技术还不能实现有效识别的现状,对应用BOTDR技术的可行性进行分析,表明BOT-DR技术用于桥梁移动荷载的识别具有广阔的前景。     

SWG物联网技术在沥青混合料质量控制中的应用

在不改变沥青拌合设备内部结构的前提下,利用传感器技术（sensor）、WIFI通信与3G通讯技术,将原材料、实验数据、配合比、拌和温度、搅拌速度以及成品储藏运输等一系列数据加以采集、对比、监控,以达到将沥青拌合料从入场、拌和到成品的全过程的各项关键数据有机的结合在一起进行分析监控。再通过采集当前气候条件下的施工温度和湿度,与质量反馈相结合,形成一个动态的、高质量的指导性数据库。从而实现对沥青拌和料的质量控制,场内监控指导、场外监督检查的一个目的。     

自动位移补偿液压移运小车的应用

蓄能器式自动位移补偿液压移运小车是为适应海洋工程装备制造开发的新型移运小车,与传统的液压移运小车相比,具有主动位移补偿功能,适用建造场地不平整的大型模块移运及海洋工程产品装船。文章就该小车技术的要点及实际运用进行阐述。     

大型船舶抛、起锚中遇到的问题与应对措施的探讨

确保大型船舶的抛、起锚作业安全,值得重视。详细阐述了大型船舶在抛、起锚中遇到的问题,提出了应采取的应对措施。然后结合几个引航实例,说明大型船舶在抛、起锚时遇到问题时的具体操作方法。     

An innovative localisation algorithm for railway vehicles

In modern railway automatic train protection and automatic train control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. The aim of this work has been developing an innovative localisation algorithm for railway vehicles able to enhance the performances, in terms of speed and position estimation accuracy, of the classical odometry algorithms, such as the Italian Sistema Controllo Marcia Treno (SCMT). The proposed strategy consists of a sensor fusion between the information coming from a tachometer and an Inertial Measurements Unit (IMU). The sensor outputs have been simulated through a 3D multibody model of a railway vehicle. The work has provided the development of a custom IMU, designed by ECM S.p.a, in order to meet their industrial and business requirements. The industrial requirements have to be compliant with the European Train Control System (ETCS) standards: the European Rail Traffic Management System (ERTMS), a project developed by the European Union to improve the interoperability among different countries, in particular as regards the train control and command systems, fixes some standard values for the odometric (ODO) performance, in terms of speed and travelled distance estimation. The reliability of the ODO estimation has to be taken into account basing on the allowed speed profiles. The results of the currently used ODO algorithms can be improved, especially in case of degraded adhesion conditions; it has been verified in the simulation environment that the results of the proposed localisation algorithm are always compliant with the ERTMS requirements. The estimation strategy has good performance also under degraded adhesion conditions and could be put on board of high-speed railway vehicles; it represents an accurate and reliable solution. The IMU board is tested via a dedicated Hardware in the Loop (HIL) test rig: it includes an industrial robot able to replicate the motion of the railway vehicle. Through the generated experimental outputs the performances of the innovative localisation algorithm have been evaluated: the HIL test rig permitted to test the proposed algorithm, avoiding expensive (in terms of time and cost) on-track tests, obtaining encouraging results. In fact, the preliminary results show a significant improvement of the position and speed estimation performances compared to those obtained with SCMT algorithms, currently in use on the Italian railway network.