高速铁路LTE-R改进切换算法的研究

针对高速铁路中LTE-R越区切换对切换时延和切换成功率的严格要求,以3GPP TS 23.401协议中的A3事件判决公式为基础,利用滤波中的测量周期Tm对触发时延进行计算,并结合列车的运行速度,对传统切换算法进行改进。通过对改进算法进行仿真,得到最佳切换参数。最后对两种算法采用相同的基站布置进行仿真比较,得出传统算法在列车运行速度超过205 km/h时,无法满足我国无线通信系统对越区切换成功率99.5%以上的要求,而改进后的算法在速度达到400 km/h时,切换成功率为99.6%,仍满足此要求。     

Dynamic demand estimation and prediction for traffic urban networks adopting new data sources

Nowadays, new mobility information can be derived from advanced traffic surveillance systems that collect updated traffic measurements, both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities that academics and practitioners have only partially explored so far.The paper looks at some of these opportunities within the Dynamic Demand Estimation problem (DDEP). At first, data heterogeneity, accounting for different sets of data providing a wide spatial coverage, has been investigated for the benefit of off-line demand estimation. In an attempt to mimic the current urban networks monitoring, examples of complex real case applications are being reported where route travel times and route choice probabilities from probe vehicles are exploited together with common link traffic measurements.Subsequently, on-line detection of non-recurrent conditions is being recorded, adopting a sequential approach based on an extension of the Kalman Filter theory called Local Ensemble Transformed Kalman Filter (LETKF).Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models. Consequently, the possibility of using collected traffic information is enhanced, thus overcoming most of the limitations of current DDEP approaches found in the literature.     

Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements

There has been an increasing role played by Global Navigation Satellite Systems (GNSS) in Intelligent Transportation System (ITS) applications in recent decades. In particular, centimeter/decimetre positioning accuracy is required for some safety related applications, such as lane control, collision avoidance, and intelligent speed assistance. Lane-level Anomalous driving detection underpins these safety-related ITS applications. The two major issues associated with such detection are (1) accessing high accuracy vehicle positioning and dynamic parameters; and (2) extraction of irregular driving patterns from such information. This paper introduces a new integrated framework for detecting lane-level anomalous driving, by combining Global Positioning Systems (GPS), BeiDou, and Inertial Measurement Unit (IMU) with advanced algorithms. Specifically, we use Unscented Particle Filter (UPF) to perform data fusion with different positioning sources. The detection of different types of Anomalous driving is achieved based on the application of a Fuzzy Inference System (FIS) with a newly introduced velocity-based indicator. The framework proposed in this paper yield significantly improved accuracy in terms of positioning and Anomalous driving detection compared to state-of-the-art, while offering an economically viable solution for performing these tasks.     

全球定位系统和全球卫星导航系统伪距综合法

论述了综合GPS、GLONASS伪距测量值获取定位信息的滤波方法。GPS、GLONASS的坐标系、时间系统、工作频率不同，不同卫星的方位、高度各不相同，因而其测距的系统误差也各不相同，通常的方法是用GPS和GLONASS的钟差2个参数来描述不同系统卫星的系统误差，需用5颗卫星来定位，且精度受限。采用扩展状态维数，对不同卫星的系统误差进行在线估计的滤波方法，可以有效地改进卫星定位的精度。同时既可以用多星定位，充分利用测量信息，也可在任一系统的l颗卫星可用时，给出测者的最优估计位置信息，改进了双星系统的导航性能。     

船舶航向自适应神经网络鲁棒跟踪控制

针对存在不确定性和带有完全未知时变环境扰动的船舶航向非线性控制系统,将指令滤波技术和反步法相结合,设计了一种船舶航向自适应神经网络鲁棒跟踪控制器,该控制器的提出不依赖于外部扰动任何的先验信息。首先,利用神经网络补偿船舶航向非线性控制系统中的非线性项;设计自适应律在线更新神经网络权重向量和估计时变环境扰动的未知界。为了避免传统反步法中对虚拟控制律的反复求导,引入指令滤波技术,使得所设计的航向自适应神经网络跟踪控制器具有结构简单、易于工程实现的特点。理论分析表明,所设计的控制器能使船舶实际输出航向以任意期望的精度跟踪给定的参考航向,保证船舶航向闭环控制系统中所有信号一致最终有界。最后,以大连海事大学远洋实习船“育龙”轮为例进行仿真,仿真结果验证了所提控制器的有效性和鲁棒性。     

基于DirectS how技术船用钢板图像采集设计与实现

基于DirectShow技术,在Windows平台下构建测量船用钢板三维尺寸的图像采集系统,为大尺寸物件的测量提供了一种信息采集方案。首先分析了DirectShow技术基本体系,接着介绍了该系统的应用环境整体布局以及采集系统总体设计方案,其次通过对常见的视频采集卡进行分析,给出了针对USB摄像头,基于MFC框架的图像采集系统实现过程,包括设备Filter的构建,Filter Graph结构设计,开发了本系统所用到的抓图Filter,实现对应用环境画面的实时监控和采集,通过实验表明设计方案的可行性和正确性。     

舰船海底门过滤器杂物自动清除技术研究

由于近海海水中杂物增多,停靠和航行在港口和近海航道上的舰船容易吸入杂物,从而造成海底门过滤器堵塞,影响海水正常供给,进而影响舰船装备的使用。传统的海底门过滤器结构简单,不具备自动清除杂物的功能,只能靠人工清除。本文提出了一种新型海底门过滤器的技术方案,研制了由旋转式离心过滤器、杂物回流排出机构和智能控制器等组成的海底门杂物自动清除装置,实现了杂物自动清除和海水过滤的目的,为解决海底门过滤器堵塞问题提供了一种新的技术手段。     

关于整机系统模拟雷电冲击试验相关问题的探讨

根据对目前雷电冲击试验过程中使用的模拟雷电脉冲波形的频谱分析,找出能量主要集中的频率范围,利用成熟的频域处理方法（滤波、隔离）,在不影响工作信号通过的情况下,将高压脉冲波限制在标准规定的等级范围,使高压脉冲波对去耦网络后端辅助设备的影响变得很小,以有效保障实施整机系统模拟雷电冲击动态试验.     

J-Resolution Fields of Generalized Literals of L14 P（X）

Introduction In order to study the lattice-valued logic system,Xu[1]proposed the concept of lattice implication alge-bra by combining the lattice with the implication alge-bra and discussed its properties systematically. InRefs.[2,3], a lattice-valued propositional logic sys-tem was established using the concepts of free alge-bra, which corresponds to the lattice implication alge-bra, and the semantic and syntactical problems of thissystem were discussed. Xu et al[4]extended the reso-lution p…     

Data Fusion-Based Traffic Density Estimation and Prediction

Traffic congestion has become a major challenge in recent years in many countries of the world. One way to alleviate congestion is to manage the traffic efficiently by applying intelligent transportation systems (ITS). One set of ITS technologies helps in diverting vehicles from congested parts of the network to alternate routes having less congestion. Congestion is often measured by traffic density, which is the number of vehicles per unit stretch of the roadway. Density, being a spatial characteristic, is difficult to measure in the field. Also, the general approach of estimating density from location-based measures may not capture the spatial variation in density. To capture the spatial variation better, density can be estimated using both location-based and spatial data sources using a data fusion approach. The present study uses a Kalman filter to fuse spatial and location-based data for the estimation of traffic density. Subsequently, the estimated data are utilized for predicting density to future time intervals using a time-series regression model. The models were estimated and validated using both field and simulated data. Both estimation and prediction models performed well, despite the challenges arising from heterogeneous traffic flow conditions prevalent in India.