Evaluation of detection sensitivity and count performance of advanced vehicle detection technologies at a signalized intersection

This article evaluates the stop-bar detection and count performance of three advanced vehicle detection sensors under various environmental conditions at a signalized intersection. Continuing advancements to vehicle detection technologies and improvements to their detection capabilities to overcome issues from impacting conditions necessitates testing the performance of new and upgraded sensor products to evaluate their performance and identify the most suitable products for various climates and weather conditions. The three evaluated sensors were Autoscope Encore video, Iteris Vantage Edge 2 video, and Wavetronix SmartSensor Matrix microwave sensors. The three sensors performed with high detection sensitivity during ideal environmental conditions with up to 99.9% detection accuracy levels and are suitable for traffic monitoring centers that rely on remote access to the monitored sites and the collected data. However, they were affected by some extreme adverse weather conditions, mainly daytime and nighttime snow, daytime fog, dawn lighting, and strong winds (for high mounted devices). The selection of a sensor product will depend on the type of application and the priority given to the type of traffic data being collected. Overall, the Iteris video sensor performed with the highest detection sensitivity levels, with the Wavetronix Matrix microwave sensor performing similarly under most conditions (14 of 19 evaluated conditions). Autoscope video provided the highest count accuracies and also provides a much broader data collection capability. The results of this study will help transportation agencies in selecting suitable vehicle detection sensor technologies for future installations within their jurisdiction and improved data collection.     

Fast direction of arrival algorithm based on vector-sensor arrays using wideband sources

An acoustic vector sensor （AVS） can capture more information than a conventional acoustic pressure sensor （APS）. As a result, more output channels are required when multiple AVS are formed into arrays, making processing the data stream computationally intense. This paper proposes a new algorithm based on the propagator method for wideband coherent sources that eliminates eigen-decomposition in order to reduce the computational burden. Data from simulations and lake trials showed that the new algorithm is valid： it resolves coherent sources, breaks left/right ambiguity, and allows inter element spacing to exceed a half-wavelength.     

虚拟仪器技术在传感器实验台中的应用

虚拟仪器技术是微电子、通信、计算机等现代科学技术高速发展的产物.通过对虚拟仪器技术的深入研究,把该技术应用到传感器实验台的硬件驱动、数据采集、结果分析、数据显示、数据输出等,改变了传统的设计方法,实现用计算机技术结合虚拟仪器来实现传感器试验台的功能,使传感器实验台的结构简单合理、灵活方便、一机多用.     

基于比例系数-有效独立法对圆柱壳声辐射预报的测点优化布置研究

文章以圆柱壳结构为研究对象,以反映测点模态振型相关性的比例系数来修正传统的有效独立法,提出了一种基于比例系数-有效独立法的测点优化布置算法。并分别用基于模态和响应的评价准则与现有的三种测点布置方法进行比较。结果表明:比例系数-有效独立法可有效地避免测点间的信息冗余问题,在保留结构振动特性方面具有明显的优势;并且能更加准确地预报圆柱壳结构的声振响应,是一种适合圆柱壳结构较为理想的测点优化布置算法。     

转矩传感器磨损造成汽车跑偏的原因分析

针对市场某车型基本存在转向左跑偏的现状,根据转矩传感器的原理,对转矩传感器的主要部件尺寸进行测量分析,找出跑偏的原因,并模拟试验进行验证。     

自适应加权融合算法在图像型火灾探测系统中的应用

根据船舶火灾探测的特点以及火灾早期预报的要求,设计了带图像信息的火灾自动报警系统,并将自适应加权融合算法应用于该系统。在该系统中信息层完成数据采集、处理,特征层运用自适应加权融合算法进行特征融合,决策层运用概率神经网络进行决策融合,完成船舶火灾探测系统的自动报警输出。实验表明,将该算法应用于此系统能够准确、快速探测火灾,识别日光灯、酒精灯等干扰源,具有较强的抗干扰性。     

一种惯性/星光复合制导系统研究

针对传统的基于姿态角修正的惯性/星光复合制导系统的不足,提出了一种新的基于位置和姿态信息进行融合估计的惯性/星光复合制导修正方案,来提高战略导弹的制导精度和可靠性。一方面利用得到的星敏感器视场中恒星星光矢量在CCD光敏面上的星像点及其在导航星库中的赤经、赤纬,结合高度仪给出的导弹高度值,可得到战略导弹的位置信息,再与惯导系统给出的导弹位置信息相减可得到位置误差测量值;另一方面通过星图识别及坐标变换可得到战略导弹的姿态误差值。以Kalman滤波为基础,将得到的位置误差和姿态角误差进行数据融合,估计出复合制导系统的误差状态量,进而修正惯导系统的位置、速度和姿态角参数。仿真结果表明,该方案有效地抑制了传统复合方案中位置和速度误差的发散,并使系统具有较高的精度和可靠性。     

一种基于单个移动信标的分布式节点定位算法

定位是无线传感器网络中最重要的功能之一。通过利用一个移动信标节点（装备GPS或提前设定了移动路线）来帮助各个未知节点进行自身定位。信标节点周期性的发送自己位置信息给周围的未知节点,未知节点通过非测距技术估计自身位置。信标节点的移动路径在文中也进行了探讨。仿真结果说明了本文方法的有效性。     

基于以太网的城市路灯管理控制系统设计

城市路灯管理控制系统依靠以太网技术和现代传感器技术,可实现对城市路灯的分布式智能管理和集中管理相结合,达到节能的目的。分布式智能管理子系统实时采集环境、状态参数,科学的判断来控制路灯的亮度,达到节能的目的。集中管理可对分布式子系统进行统一管理,并接收、处理以及存储分布式子系统采集的环境、状态数据,用于事后数据分析等。     

硅钢片智能焊接系统设计

设计一种基于机器视觉的硅钢片智能焊接系统,通过CCD传感器对焊缝进行跟踪检测,采用数字图像处理算法对焊缝进行识别,并采用PID控制对焊接过程跟踪焊接。