New applications of EXIT chart for iterative decoding systems

Applications on iterative control and multiple input multiple output （MIMO） system were developed. Two new charts derived from extrinsic information transfer （EXIT） chart were employed as the designing tools, which are called as output mutual information chart with defined iterative degree （DID） chart and near optimum output mutual information （NOMI） chart respectively. Different from the EXIT chart, they can show the iterative performance on the whole signal-to-noise ratio range with one single curve, whereas computation complexity is greatly reduced compared with conventional bit error ratio （BER） performance curve. The iterative control was implemented according to a near-optimum iterative degree vector determined by NOMI chart, the reasonability of uncertain parameters was analyzed in one MIMO system. The concepts were illustrated based on bit-interleaved coded modulation with iterative decoding （BICM-ID）.     

基于曲率变化特征的既有线路整正算法设计

铁路曲线运营过程不可避免会产生几何形位变化,运行速度的提升需要现代化的检测手段,相应的曲线整正算法设计是列车安全、舒适运营的重要保障。采用某线路轨检小车实测5 m间隔数据,以现场实测数据为基础,从曲率和曲率变化率的角度分析不同间隔条件下的轨道几何形位特征,采用基于正交最小二乘和三次样条的平面拟合方法,利用线路的曲率特征进行线路初始参数获取,设计相应的平面曲线线形优化重构算法。编程实现算法在具体线路的应用,以拨道量改正数最小为目标函数,采用5、10 m不同测点间隔的计算结果分析并结合现场实际,取得了较好的计算结果。通过现场实例表明,该方法具有理论简单、计算准确,与现代高速行车检测方法适应好等特点。     

并联混合动力汽车改进模糊控制策略研究

本文提出一种兼顾电池SOC限值方法的混合动力汽车多种群遗传模糊控制策略。引入模糊逻辑控制以增强整车控制系统鲁棒性、实时性;用多种群遗传算法对模糊变量隶属度函数进行优化,使在模糊逻辑控制下整车燃油消耗得到降低;使用电池SOC限值方法避免电池在SOC过低时继续放电。利用matlab平台联合advisor软件进行联合仿真实验,仿真结果表明多种群遗传模糊模糊控制策略能够比advisor软件默认的电机辅住控制策略燃油经济性提高6.96%的情况,SOC限值方法使电池工作在更加合理的SOC值区间范围内,有效保护电池。     

受限状态下的高速列车迭代学习控制方法研究

针对受限状态下的高速列车自动驾驶系统的跟踪控制问题,基于列车动力学模型,提出一种带饱和函数的迭代学习控制算法。根据Lyapunov稳定性原理,利用列车运行过程中的状态偏差,推导出基于迭代学习控制的列车自动运行控制律。建立类Lyapunov的复合能量函数,通过在迭代域的差分,证明了其差分负定性和有界性,所设计的算法能够控制列车在迭代域对期望运行轨迹达到渐近收敛。采用本文提出的迭代学习控制算法对列车的跟踪性能进行验证,并与PID控制和D型迭代学习控制算法进行比较,结果表明:相较于其他两种算法,本文提出的算法在第3次迭代中就能控制列车精确跟踪期望轨迹,说明算法具有较快的收敛速度和较高的跟踪精度,且能够将控制输入约束在允许范围内。     

A finite mixture model of vehicle-to-vehicle and day-to-day variability of traffic network travel times

This study proposes an approach to modeling the effects of daily roadway conditions on travel time variability using a finite mixture model based on the Gamma–Gamma (GG) distribution. The GG distribution is a compound distribution derived from the product of two Gamma random variates, which represent vehicle-to-vehicle and day-to-day variability, respectively. It provides a systematic way of investigating different variability dimensions reflected in travel time data. To identify the underlying distribution of each type of variability, this study first decomposes a mixture of Gamma–Gamma models into two separate Gamma mixture modeling problems and estimates the respective parameters using the Expectation–Maximization (EM) algorithm. The proposed methodology is demonstrated using simulated vehicle trajectories produced under daily scenarios constructed from historical weather and accident data. The parameter estimation results suggest that day-to-day variability exhibits clear heterogeneity under different weather conditions: clear versus rainy or snowy days, whereas the same weather conditions have little impact on vehicle-to-vehicle variability. Next, a two-component Gamma–Gamma mixture model is specified. The results of the distribution fitting show that the mixture model provides better fits to travel delay observations than the standard (one-component) Gamma–Gamma model. The proposed method, the application of the compound Gamma distribution combined with a mixture modeling approach, provides a powerful and flexible tool to capture not only different types of variability—vehicle-to-vehicle and day-to-day variability—but also the unobserved heterogeneity within these variability types, thereby allowing the modeling of the underlying distributions of individual travel delays across different days with varying roadway disruption levels in a more effective and systematic way.     

考虑客流空间分布的地铁列车节能时刻表优化方法

客流变化引起的列车质量变化是影响地铁列车能耗与节能运行的重要因素之一. 本文考虑地铁线路客流空间分布差异，研究耗散型再生制动能利用方式下的列车节能时刻表优化方法. 结合各区间载荷差异和列车运动方程，建立以净能耗最小为目标的时刻表优化模型，通过适度优化计划停站时间、区间运行时间和折返时间协同多列车牵引、巡航、惰行和制动过程的时空分布，设计二分法和粒子群算法对模型求解. 以北京地铁某线路进行实例研究，结果表明，优化模型能有效协同多列车的节能运行，考虑客流空间分布差异比假定列车载荷为常数能进一步提升节能效果.     

A differential evolution approach for the vehicle routing problem with backhauls and time windows

This paper presents a differential evolution algorithm (DEA) to solve a vehicle routing problem with backhauls and time windows (VRPBTW) and applied for a catering firm. VRPBTW is an extension of the vehicle routing problem, which includes capacity and time window constraints. In this problem, customers are divided into two subsets: linehaul and backhaul. Each vehicle starts from a depot and goods are delivered from the depot to the linehaul customers. Goods are subsequently brought back to the depot from the backhaul customers. The objective is to minimize the total distance that satisfies all of the constraints. The problem is formulated using mixed integer programming and solved using DEA. Proposed algorithm is tested with several benchmark problems to demonstrate effectiveness and efficiency of the algorithm and results show that our proposed algorithm can find superior solutions for most of the problems in comparison with the best known solutions. Hence, DEA was carried out for catering firm to minimize total transportation costs. Copyright © 2013 John Wiley & Sons, Ltd.     

基于地铁-货车联运的物流配送路径优化

为应对配送车辆引起道路拥堵和环境污染问题，提出地铁与货车联合运输. 在不改变地铁运行方案的前提下，利用地铁非高峰时段开展货物配送；考虑地铁剩余运能、货车容量、最大行驶距离、客户服务时间窗等限制条件，以配送距离最短为优化目标，构建基于地铁-货车联运的物流配送路径优化模型. 通过地铁配送路径设计不规则二维矩阵编码结构，使用改进自适应遗传算法求解. 以某市地铁货物运输为例，验证模型和算法的实用性、有效性. 结果表明，地铁-货车联合配送距离短，在客户时间窗范围内送达比例高，有效提高客户满意度.     

利用激光点云的城市无人驾驶路径规划算法

为了解决随机采样算法受感知环境不确定性影响下的弱鲁棒性以及弱可靠性问题，采用一种基于激光空间势场的渐优随机采样算法框架来设计符合无人驾驶需要的规划算法。针对感知环境的不确定性，首先基于势场原理与激光障碍物点云构建一个融入了斥力场的规划空间，解决激光障碍物提取中的过分割等问题。其次，利用规划空间来处理随机采样算法中的采样策略、最优母节点选取策略、修剪策略以及最终路径选择策略。再次，在算法中加入了Anytime策略来提高优化解的利用率，使得算法的计算效率满足无人驾驶实时性的要求。同时，为了保证无人驾驶中规划路径的鲁棒性与可靠性，创建了一个综合5重因素的代价函数来选择最优路径，并根据不同的无人驾驶场景来调整相对应的参数；最后在城市测试道路上进行了实地测试。结果表明：设计的算法框架能够适应最高时速40 km·h^-1的城区驾驶环境，并能完成跟驰、换道、融入以及静动态障碍物的避障决策。在与SST算法的对比试验中，所提出的算法在各个试验中的轨迹、方向盘转角以及速度的平滑性都优于SST算法，其轨迹与障碍物的距离也优于SST算法。