长江通航安全研究的船舶操纵仿真系统

长江水域通航条件高度复杂，有针对性地开展船舶操纵模拟系统的研究显得尤为重要，文中介绍了全功能大型船舶操纵模拟器及通用船舶操纵计算机仿真平台（USManCSP），提出以前者砻实时模拟基础，结合USManCSP中的电子江图信息系统（ECS）、内河船舶操纵运动数学模型、基于模糊控制的引航员控制模型等进行快时模拟研究，建立较完善的通航安全论证分析平台，并给出了二个急弯河段的模拟研究结果．     

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目前中国城市道路交通控制系统基本上是引用SCOOT、SCAT等国外开发的系统，这些系统皆是建立在机动车为主的道路交通条件基础之上的被动型控制系统。实践证明：这些系统不仅不适应中国的混合道路交通情况，在连续流与间断流的协调控制、公共汽车交通优先控制方面也存在问题，更难以适应于中国城市发展智能交通系统（ITS）的学姚。本文是国家重点基础研究规划（973）子项目“城市交通监控与管理系统”的研究成果之一，针对中国城市道路交通的特点及以往交通控制系统的问题和未来发展的需要，提出了适用于中国城市的实时自适应控制与管理系统     

基于波束形成和双目视觉的行驶汽车噪声源识别

提出一种基于波束形成原理,声阵列与双目视觉相结合的行驶汽车噪声源识别方法。通过对两台摄像机所拍摄图像的分析,计算出车辆的实时位置;将运动过程划分为若干时段进行声场重建,得出汽车行驶过程中的动态声场视频。试验结果表明,应用该方法能够准确识别运动中汽车的主要噪声源,且可更加直观地看到汽车噪声的动态变化。     

A distributed origin–destination demand estimation approach for real-time traffic network management

Abstract

This paper describes a distributed recursive heuristic approach for the origin–destination demand estimation problem for real-time traffic network management applications. The distributed nature of the heuristic enables its parallelization and hence reduces significantly its processing time. Furthermore, the heuristic reduces dependency on historical data that are typically used to map the observed link flows to their corresponding origin–destination pairs. In addition, the heuristic allows the incorporation of any available partial information on the demand distribution in the study area to improve the overall estimation accuracy. The heuristic is implemented following a hierarchal multi-threading mechanism. Dividing the study area into a set of subareas, the demand of every two adjacent subareas is merged in a separate thread. The merging operations continue until the demand for the entire study area is estimated. Experiments are conducted to examine the performance of the heuristic using hypothetical and real networks. The obtained results illustrate that the heuristic can achieve reasonable demand estimation accuracy while maintaining superiority in terms of processing time.     

On-line estimation of state-of-charge of Li-ion batteries in electric vehicle using the resampling particle filter

Accurate battery state-of-charge (SOC) estimation is important for ensuring reliable operation of electric vehicle (EV). Since a nonlinear feature exists in the battery system and particle filter (PF) performs well in solving nonlinear or non-Gaussian problems, this paper proposes a new PF-based method for estimating SOC. Firstly, the relationships between the battery characteristics and SOC are analyzed, then the suitable battery model is developed and the unknown parameters in the battery model are on-line identified using the recursive least square with forgetting factors. The proposed battery model is considered as the state space model of PF and then SOC is estimated. All experimental data are collected from the running EVs in Beijing. The experimental errors of SOC estimation based on PF are less than 0.05 V, which confirms the good estimation performance. Moreover, the contrastive results of three nonlinear filters show PF has the same computational complexity as extend Kalman filter (EKF) and unscented Kalman filter (UKF) for low dimensional state vector, but PF have significantly better estimation accuracy in SOC estimation.     

The value of real-time traffic information in urban freight distribution

Routes optimization in urban freight distribution is usually an off-line process based on the knowledge of historical conditions on the network. Real-time data provided by Intelligent Transportation Systems (ITSs) enable online re-optimization on the basis of actual traffic conditions.

?This paper evaluates the added value generated by re-optimizing the off-line solution with real-time information. The study is carried out for a practical application to the freight distribution of perishable goods in the city of Rome (Italy). The off-line problem is formulated as a vehicle routing problem with soft time windows while in the online problem it is also allowed to skip some customers or to re-sequence the deliveries. Both versions are solved with different algorithms and with different data sets. Results can be used to evaluate the potential return on investment on the acquisition of different kinds of traffic data. At the same time, results can be of interest for information providers, to fix the price of off-line and online information and/or to estimate the associated potential market share.     

基于SDKEY的移动终端数据分区保护

针对传统的安全数字钥匙（Secure Digital Key,SDKEY）硬件存在的性能问题,给出了一种SDKEY硬件改进方案,并设计了SDKEY和移动终端之间的双重认证方案.提交了一种基于SDKEY的移动终端数据分区保护方案,给出了一个基于SDKEY的移动终端应用框架模型.研究分析表明,该方案在安全和性能上得到提高,并具有一定的应用价值.     

A novel method for identifying inertial parameters of electric vehicles based on the dual H infinity filter

ABSTRACT

Accurate identification of vehicle inertial parameters is essential to the design of vehicle dynamics control systems. In this paper, a novel vehicle inertial parameter identification method based on the dual H infinity filter (DHIF) for electric vehicles (EVs) is proposed. The filter algorithm employs a nonlinear longitudinal vehicle model with three vehicle states. A hierarchical framework is engaged by the DHIF to estimate the vehicle states and inertial parameters concurrently. In order to minimise the disturbance of unknown noise, the vehicle states are estimated by using the linear H infinity filter (LHIF), while the nonlinear H infinity filter (NHIF) utilises the observed states to identify the vehicle inertial parameters. Finally, the proposed estimation method is verified and compared through the dSPACE based hardware-in-the-loop (HIL) simulation experiments. The results indicate that the DHIF-based estimation method is effective to identify the vehicle inertial parameters with high precision, remarkable robustness, and quick convergence.     

基于高速公路流水数据的通勤车辆特征研究

随着居民利用高速公路进行通勤出行车辆的增加，高速公路缓行和交通拥堵等问题时有发生，特别是在重大节假日期间.目前，解决上述交通问题的主要方法是交通需求管理措施，而实现有针对性的交通需求管理需要对高速公路收费流水数据进行精确的挖掘分析，掌握车辆在高速公路上的运行状态与时空分布特征.本文基于高速公路收费流水数据，借助 K-means++聚类方法识别使用高速公路日常通勤的车辆，进一步分析通勤车辆的出行时空分布特征.从通勤出行的角度，挖掘城市通勤快速出行廊道分布，研究高速公路网与城市道路网络的关系，对提高交通系统效率和缓解交通问题具有重要的意义.     

A real-time impact detection and diagnosis system of catenary using measured strains by fibre Bragg grating sensors

This paper describes an impact detection system using strain signals based on fibre optic sensors(FBG) for the real-time monitoring of the catenary system. The proposed detection system consists of three subsystems: a measuring system, a data processing and analysis system, and a status display and data access system. Because the strain signals obey the normal distribution, to monitor the catenary system in real time, a novel method that combines mobile standard deviation with the mobile Pauta criterion is proposed to distinguish real impact from the strain signal background. The use of this adaptive judging method reduces the misjudgment rate of impacts and improves the impact recognition accuracy. These impacts can be identified by the data analysis system, which provides impact location and their causes using the features of the catenary system. This method can simplify the detection system compared with the traditional location method. An application to a commercial metro line system indicated that the impacts on the catenary system were main caused by overlaps, expansion joints or steady arms, and were verified by correspondence with the floor plan of the catenary and manual inspection results. These results verified the reliability and effectiveness of the proposed impact detection system.