一个新混沌系统的非线性反馈同步

研究了一个新的混沌系统的混沌同步问题。基于Lyapunov稳定性理论,采用非线性反馈混沌同步方法,给出了该系统实现自同步的充分条件以及控制律参数的取值范围;结合参数自适应混沌同步方法,实现了该混沌系统与统一混沌系统的异结构系统快速同步。数值仿真证明了该方法的有效性。     

基于复杂网络同步城市快速路协调控制研究

基于复杂网络同步,本文研究城市快速路多入口匝道协调控制问题.采用元胞传输模型建立城市快速路节点耦合的复杂网络动力学模型,以同步为目标设计多入口匝道协调控制器并确定控制策略,其中牵制节点对应需施加控制信号的入口匝道,推导出城市快速路网络系统同步的稳定性条件,以此得到牵制节点和反馈增益矩阵.通过具体例子仿真验证了本文协调控制方法的有效性,能以较小的控制范围代价达到抑制交通拥堵从而提高道路通行效率的目的,控制效果优于传统协调控制方式,可进一步推广到大规模城市交通网络系统.     

基于提箱同步的进口箱堆存策略研究

翻箱问题一直是影响集装箱码头堆场作业效率的重要因素之一.优化卸船箱的堆存顺序是降低进口箱提箱翻箱率的重要基础.对集装箱码头的历史作业日志进行数据挖掘,提取客户的提箱行为特征,并定义了客户提箱行为同步性特征的度量方法--客户提箱行为同步度.利用客户提箱同步特征对卸船箱进行分组,对卸船箱的提箱时间区间进行估计并据此判断其提箱次序.按照同组靠近堆放的原则,以最小压箱数为优化目标建立了箱位指派模型.实验结果表明,新的堆存策略在降低翻箱率和减少平面箱位占用率两个方面比码头现有堆存策略有较大改进.     

无线CBTC信号系统时间同步机制分析

无线CBTC信号系统作为一种实时安全列车自动控制系统,时间同步机制为列车运行记录、系统报警、故障日志提供统一的时间基准。系统地描述了信号系统时间同步机制,不仅包括信号系统的外部时钟源和内部时钟源,也包括信号系统内部各子系统的时间同步机制,同时分析了信号系统作为时钟源对外部系统的影响。     

编程实现双向干线协调参数数值计算的研究

对目前干线协调中存在的难题——双向干线协调问题,及该类问题的协调参数的计算方法进行了分析与研究;在双向干线协调的数值计算方法的基础上用Matlab进行编程,实现了数值计算的程序化,很大程度上提高了数值计算的效率与准确性,解决了数值计算过程的复杂化与数量限制性难题;最后用8个交叉口干线协调的实例验证了该程序的可行性。     

Position synchronization for track geometry inspection data via big-data fusion and incremental learning

Track geometry inspection data is important for managing railway infrastructure integrity and operational safety. In order to use track geometry inspection data, having accurate and reliable position information is a prerequisite. Due to various issues identified in this research, the positions of different track geometry inspections need to be aligned and synchronized to the same location before being used for track degradation modeling and maintenance planning. This is referred to as “position synchronization”, a long-standing important research problem in the area of track data analytics. With the aim of advancing the state of the art in research on this subject, we propose a novel approach to more accurately and expediently synchronize track geometry inspection positions via big-data fusion and incremental learning algorithms. Distinguishing it from other relevant studies in the literature, our proposed approach can simultaneously address data exceptions, channel offsets and local position offsets between any two inspections. To solve the Position Synchronization Model (PS-Model), an Incremental Learning Algorithm (IL-Algorithm) is developed to handle the “lack of memory” challenge for the fast computation of massive data. A case study is developed based on a dataset with data size of 18 GB, including 58 inspections between February 2014 and July 2016 over 323 km (200 miles) of tracks belonging to China High Speed Railways. The results show that our proposed model performs robustly against data exceptions via the use of multi-channel information fusion. Also, the position synchronization error using our proposed approach is within 0.15 meters (0.5 feet). Our proposed data-driven, incremental learning algorithm can quickly solve the complex, data-extensive, position synchronization problem, using an average of 0.1 s for processing one additional kilometer of track. In general, the data analysis methodology and algorithm presented in this paper are also suitable to address other relevant position synchronization problems in transportation engineering, especially when the dataset contains multiple channels of sensors and abnormal data outliers.     

脉冲混沌神经网络的全局指数同步性

为研究脉冲神经网络的动力学行为,基于驱动-响应概念分析了两种具有可变时滞的脉冲混沌神经网络的全局指数同步性.假设激活函数满足严格单调递增,用神经网络的权系数、自反馈函数及激活函数构造不等式.根据向量Lyapunov函数理论,得到了驱动-响应系统全局指数同步充分条件的方程,即该方程小于0.数值仿真结果证明了该充分条件的正确性.