反舰导弹作战使用中的搜捕研究

通过对导弹雷达末制导技术以及末制导雷达搜捕方式的研究,强调了搜捕方式对反舰导弹作战使用的重大影响,对反舰导弹雷达末制导技术以及搜捕方式的发展进行了探讨。     

雷达/红外成像复合制导面临的干扰及对抗

分析了雷达/红外成像复合制导反舰导弹武器系统可能面临的干扰手段。将受干扰阶段分为开机搜索阶段和跟踪阶段。按照敌方施放干扰的战术意图,将开机搜索阶段可能面临的干扰分为迷惑干扰和冲淡干扰两类,将跟踪阶段可能面临的干扰分为转移干扰、质心干扰、压制干扰和欺骗干扰四类。研究了开机搜索阶段重新搜索时目标的选择方法和依据,以及跟踪阶段对抗各类干扰可采用的措施及预期对抗效果。对研究雷达/红外成像复合制导的干扰与抗干扰技术具有重要参考价值。     

Combined longitudinal and lateral control for automated vehicle guidance

This paper deals with the longitudinal and lateral control of an automotive vehicle within the framework of fully automated guidance. The automotive vehicle is a complex system characterised by highly nonlinear longitudinal and lateral coupled dynamics. Consequently, automated guidance must be simultaneously performed with longitudinal and lateral control. This work presents an automated steering strategy based on nonlinear model predictive control. A nonlinear longitudinal control strategy considering powertrain dynamics is also proposed to cope with the longitudinal speed tracking problem. Finally, a simultaneous longitudinal and lateral control strategy helps to improve the combined control performance. This whole control strategy is tested through simulations showing the effectiveness of the present approach.     

城市交通信号控制系统(ACTRA)应用简介及构想

介绍了ACTRA交通信号控制系统的结构、特点及其主要控制策略,并对该控制系统几种主要的系统功能以图形界面形式进行了描述.对此系统目前在北京市的初步应用进行了简要介绍,并在路段调查数据的基础上进行效果评价.提出了ACTRA系统应该如何与路线诱导系统实现数据共享和方案协调,并使交通控制中心在诱导条件下实现交通控制子区自动划分的构想,以便更好地提高路网的运行效益.     

基于改进遗传算法的最优路径求解

动态路径诱导系统(dynamic route guidance system,DRGS)是通过提供基于实时交通信息的最优路径来引导交通流的,因此,最优路径的求解是关键.而遗传算法具有全局寻优和潜在并行的特点,对求解最优路径具有一定优势.但采用序号编码方式进行遗传操作时会产生大量无效路径.文中结合城市道路交叉口左转、右转、直行等转向行为,设计了一种新的基于转向行为的编码方式,减少了染色体在交叉、变异时的无效路径的生成.算例表明,这种编码方式可以有效提高算法收敛性,更容易获得最优解.     

包含禁行路线路网的最优路径HNN算法

为了解决包含禁行路线路网的最优路径快速求解问题,研究了不含禁行路线路网和包含禁行路线路网的特点,建立了相应的路网数学模型。通过路网转化法把包含禁行路线的路网转化为不含禁行路线的路网,降低了最优路径求解的难度。研究了霍普费尔特神经网络(Hopfield Neural Network,HNN)的特点,设计了适合求解路网最优路径的HNN算法,在算法中采用动态邻接矩阵,节省了计算机内存,减少了运算时间。将所研究的路网转化方法和设计的HNN算法应用于所研发的车辆诱导系统中,并进行了实际路网测试,结果表明应用该方法能够在包含禁行路线路网中求解最优路径,且比经典算法的运算效率高。     

基于中关村西区的地下停车诱导信息系统设计

以北京中关村西区地下停车场为背景，对停车诱导信息系统进行了设计与研究。文章主要介绍了中关村西区地下停车场诱导信息发布方案的设计，基于BP神经网络的停车泊位预测以及基于模糊推理的地下环廊路段优化选择，并给出了Matlab环境下的仿真结果。     

Identification of communities in urban mobility networks using multi-layer graphs of network traffic

This paper proposes a novel approach to identify the pockets of activity or the community structure in a city network using multi-layer graphs that represent the movement of disparate entities (i.e. private cars, buses and passengers) in the network. First, we process the trip data corresponding to each entity through a Voronoi segmentation procedure which provides a natural null model to compare multiple layers in a real world network. Second, given nodes that represent Voronoi cells and link weights that define the strength of connection between them, we apply a community detection algorithm and partition the network into smaller areas independently at each layer. The partitioning algorithm returns geographically well connected regions in all layers and reveal significant characteristics underlying the spatial structure of our city. Third, we test an algorithm that reveals the unified community structure of multi-layer networks, which are combinations of single-layer networks coupled through links between each node in one network layer to itself in other layers. This approach allows us to directly compare the resulting communities in multiple layers where connection types are categorically different.     

Estimating potential increases in travel with autonomous vehicles for the non-driving,elderly and people with travel-restrictive medical conditions

Automated vehicles represent a technology that promises to increase mobility for many groups, including the senior population (those over age 65) but also for non-drivers and people with medical conditions. This paper estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. In addition, these bounding estimates indicate which of these demographics could have the greatest increases in annual vehicle miles traveled (VMT) and highlight those age groups and genders within these populations that could contribute the most to the VMT increases. The data source is the 2009 National Household Transportation Survey (NHTS), which provides information on travel characteristics of the U.S. population. The changes to light-duty VMT are estimated by creating and examining three possible travel demand wedges. In demand wedge one, non-drivers are assumed to travel as much as the drivers within each age group and gender. Demand wedge two assumes that the driving elderly (those over age 65) without medical conditions will travel as much as a younger population within each gender. Demand wedge three makes the assumption that working age adult drivers (19–64) with medical conditions will travel as much as working age adults without medical conditions within each gender, while the driving elderly with medical any travel-restrictive conditions will travel as much as a younger demographic within each gender in a fully automated vehicle environment. The combination of the results from all three demand wedges represents an upper bound of 295 billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. Since traveling has other costs besides driving effort, these estimates serve to bound the potential increase from these populations to inform the scope of the challenges, rather than forecast specific VMT scenarios.