Railway wheel-flat detection and measurement by ultrasound

This work presents an innovative ultrasound technique designed to detect and quantify flats formed in the rolling surface of railway wheels. Differently from other approaches, ultrasonic pulses (Rayleigh waves) are sent over a measuring rail. The variations in the round-trip time of flight (RTOF) of the ultrasound pulse to the rail-wheel contact point allow detecting and quantifying the wheel-flats. In spite of the wear state of the irregularity, the method provides the loss of material and the length of the flat originally formed by abrasion. A theoretical background supports the technique which offers many advantages for railway maintenance. Simulations and experimental results match the expected ones.     

高速公路场景下的电子稳像技术研究

为了去除高速公路监控视频中自身位置不稳定的事物给全局运动矢量估计带来误差,先通过Canny算子检测图像中的直线,并根据车道边界的特征筛选出道路边界,确定道路区域。再检测道路区域内的Harris角点信息,并与背景图中道路区域内的角点位置进行匹配,从而得到全局运动矢量,并计算出稳定的图像。实验表明,该算法能够满足高速公路场景下的稳像需求。     

Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures

Due to the difficulty of obtaining accurate real-time visibility and vehicle based traffic data at the same time, there are only few research studies that addressed the impact of reduced visibility on traffic crash risk. This research was conducted based on a new visibility detection system by mounting visibility sensor arrays combined with adaptive learning modules to provide more accurate visibility detections. The vehicle-based detector, Wavetronix SmartSensor HD, was installed at the same place to collect traffic data. Reduced visibility due to fog were selected and analyzed by comparing them with clear cases to identify the differences based on several surrogate measures of safety under different visibility classes. Moreover, vehicles were divided into different types and the vehicles in different lanes were compared in order to identify whether the impact of reduced visibility due to fog on traffic crash risk varies depending on vehicle types and lanes. Log-Inverse Gaussian regression modeling was then applied to explore the relationship between time to collision and visibility together with other traffic parameters. Based on the accurate visibility and traffic data collected by the new visibility and traffic detection system, it was concluded that reduced visibility would significantly increase the traffic crash risk especially rear-end crashes and the impact on crash risk was different for different vehicle types and for different lanes. The results would be helpful to understand the change in traffic crash risk and crash contributing factors under fog conditions. We suggest implementing the algorithms in real-time and augmenting it with ITS measures such as VSL and DMS to reduce crash risk.     

Who you are is how you travel: A framework for transportation mode detection using individual and environmental characteristics

With the increasing prevalence of geo-enabled mobile phone applications, researchers can collect mobility data at a relatively high spatial and temporal resolution. Such data, however, lack semantic information such as the interaction of individuals with the transportation modes available. On the other hand, traditional mobility surveys provide detailed snapshots of the relation between socio-demographic characteristics and choice of transportation modes. Transportation mode detection is currently approached using features such as speed, acceleration and direction either on their own or in combination with GIS data. Combining such information with socio-demographic characteristics of travellers has the potential of offering a richer modelling framework that could facilitate better transportation mode detection using variables such as age and disability. In this paper, we explore the possibility to include both elements of the environment and individual characteristics of travellers in the task of transportation mode detection. Using dynamic Bayesian Networks, we model the transition matrix to account for such auxiliary data by using an informative Dirichlet prior constructed using data from traditional mobility surveys. Results have shown that it is possible to achieve comparable accuracy with the most widely used classification algorithms while having a rich modelling framework, even in the case of sparse mobility data.     

船舶电网频率实时检测仿真研究

针对电力推进船舶电网频率的实时精确检测,阐述了基于二阶直接型IIR数字陷波器的基本原理,基于LMP(p=3)自适应算法建立了在跟踪陷波器参数基础上的船舶电网频率实时检测模型,并分别在电网频率不变、频率发生阶跃变化以及频率线性变化的不同情况下进行了基于Simulink的仿真研究。仿真结果验证了提出的频率检测方法的实时性和精确性。     

基于信息衰减的无源干扰作战效能研究

针对箔条干扰云团的作战效能问题，从信息衰减的角度出发，建立了基于雷达最大作用距离的衰减模型和基于发现概率的效费比模型，通过仿真实例验证了箔条走廊密度对雷达最大作用距离的定量关系，提出了箔条走廊干扰取得最佳效费比的方法，结果表明，合理设计箔条走廊密度和抛撒箔条数量，可以取得最佳的效费比，对箔条的实际应用有很强的实用价值。     

信号采样理论在交通流检测点布设中的应用

在对交通流检测点布设领域研究应用现状进行分析与抽象的基础上,提出交通流检测点布设本质上属于空间交通流信号采样问题。依据信号采样理论,建立了确定检测点间距或密度的方法与流程,该流程包括空间交通流信号产生、离散傅里叶变换、频谱分析、间距确定、误差分析5个步骤。对高速公路拥挤状态下的仿真分析表明:100 m的检测点间距可以满足对拥挤交通流状态的真实检测,该数值接近于实际应用中的最小值(125 m),说明该理论方法具有较好的实用性。     

面向交通安全的车辆间距测试技术研究

基于数字图像处理理论,提出了一种车间距离测试的方法,介绍了边缘增强与形态学处理相结合的图像预处理算法、阈值分割与判定树结合的障碍物检测方法以及反转透视测距计算模型,利用VC 平台编写了测距实验程序并组织了测距试验.试验结果表明,该测距方法简洁有效,能满足智能车辆测距工程需要,是一种可行的前方车辆距离测量方法.     

一种基于边缘颜色的彩色车牌定位算法

为解决车牌识别中车牌定位问题,根据车牌局域颜色变化、车牌宽高、车牌中字符数及车牌中字符颜色变化等特征,提出了基于边缘颜色的车牌定位算法;通过实验,证明了该算法能够有效地克服光线和天气变化的影响,具有很强的抗干扰性能、定位精度高达98.4%.     

基于DSP的嵌入式驾驶疲劳监测系统研究

为了进行有效的、实时的、非接触式的驾驶员疲劳检测,采取NHTSA和FHWA推荐的非接触式疲劳检测方法PERCLOS,利用TI公司的专用图像处理DSP芯片TMS320DM642,构建了车载的基于DSP的嵌入式驾驶疲劳监测系统。首先使用肤色模型算法迅速定位人脸,然后利用累积差分帧和Hough变换等实时图像处理技术来检测、跟踪眼睛,分析眼睛的状态和提取眼睛的特征参数,计算PERCLOS值、判断疲劳程度并采取报警或其他措施。试验结果表明,系统的算法简单、快速、鲁棒性强。