基于局部均值分解与局部离群因子动力电池故障诊断

动力电池故障诊断是保证电动汽车正常运行的关键。提出一种基于局部均值分解和局部离群因子的动力电池故障诊断方法,用于电池组故障识别与定位。通过局部均值分解对电压信号预处理,并根据相关系数高低重构电压信号。进一步提取重构信号的峭度因子作为故障特征输入到局部离群因子算法中,根据局部离群因子算法自适应阈值输出故障电池。采用实车数据验证了所提方法能有效、准确地检测出故障,具有较好的可靠性与鲁棒性。     

基于遗传算法的潜艇首端耐压平面舱壁构架分级优化研究

探讨了潜艇首端耐压平面舱壁的结构优化设计,针对舱壁结构复杂、设计变量多、取值离散规格化等难点,确定两级优化方案,即先后分别对舱壁内部主构架和肘板进行优化,采用遗传算法作为优化方法,并利用ANSYS程序对舱壁结构进行受力分析.对遗传算法进行了改进,并在此基础上用MATLAB语言编程实现遗传算法操作,同时编程与ANSYS结合起来,完成结构优化设计.经过迭代优化,得到一个优化方案,优化部分的结构重量较初始方案降低了18.39%.     

Short-term forecasting of high-speed rail demand: A hybrid approach combining ensemble empirical mode decomposition and gray support vector machine with real-world applications in China

Short-term forecasting of high-speed rail (HSR) passenger flow provides daily ridership estimates that account for day-to-day demand variations in the near future (e.g., next week, next month). It is one of the most critical tasks in high-speed passenger rail planning, operational decision-making and dynamic operation adjustment. An accurate short-term HSR demand prediction provides a basis for effective rail revenue management. In this paper, a hybrid short-term demand forecasting approach is developed by combining the ensemble empirical mode decomposition (EEMD) and grey support vector machine (GSVM) models. There are three steps in this hybrid forecasting approach: (i) decompose short-term passenger flow data with noises into a number of intrinsic mode functions (IMFs) and a trend term; (ii) predict each IMF using GSVM calibrated by the particle swarm optimization (PSO); (iii) reconstruct the refined IMF components to produce the final predicted daily HSR passenger flow, where the PSO is also applied to achieve the optimal refactoring combination. This innovative hybrid approach is demonstrated with three typical origin–destination pairs along the Wuhan-Guangzhou HSR in China. Mean absolute percentage errors of the EEMD-GSVM predictions using testing sets are 6.7%, 5.1% and 6.5%, respectively, which are much lower than those of two existing forecasting approaches (support vector machine and autoregressive integrated moving average). Application results indicate that the proposed hybrid forecasting approach performs well in terms of prediction accuracy and is especially suitable for short-term HSR passenger flow forecasting.     

Public transit subsidies, output effect and total factor productivity

This paper extends previous works on total factor productivity decomposition when firms receive both operating and capital subsidies. It shows that previous works considered either the lump-sum or substitution effects of these subsidies but not together. Using constrained cost minimization as the framework it offers formal proofs to show that cost increases are inevitable if the total effects of the subsidies are considered, and that total factor productivity growth results from increasing amounts of subsidies under economies of scale and in the absence of technical change. Applications of the decomposition equations derived to a sample of transit systems finds near constant returns to scale and negative contributions of these subsidies to total factor productivity growth. Technical change reverses this decline and results in total factor productivity growth. Further, it finds that the lump-sum effects of the subsidies reduce total factor productivity more than does the substitution effect.     

Parallel computing with domain decomposition for the vehicle crashworthiness simulation

The research presented in the paper deals with explicit nonlinear finite element calculation with domain decomposition for vehicle crashworthiness simulation. This is very important for vehicle design. Parallel computing is an efficient solution method to speedup and enhance the solving ability of large-scale numerical simulation. In this paper, a cost-effective domain decomposition method based on contact balance is presented, and the algorithm flowchart including contact computing is provided, and the parallel computing process and communication overhead are analyzed. Furthermore, scalability of the parallel computing method on different hardware platforms, the SGI Onyx 3800 and the ShenWei cluster, is studied. Finally, the effect of different domain decomposition strategy on vehicle crashworthiness simulation computing efficiency is presented. To end users, the research results should provide a reference for vehicle design and choosing appropriate hardware platform and computing software.     

区级海绵城市建设规划中指标管控方法探讨

针对区级海绵城市建设规划中管控单元划分和指标分解等关键环节,提出最小管控单元和管控指标源头-系统两级分解体系.上海市虹口区的案例表明,最小管控单元可有效衔接源头地块、控规单元和排水系统,管控指标源头-系统两级分解体系可有效体现系统性工程在海绵指标中的贡献,合理减轻源头项目的达标压力.     

乔列斯基分解应用于偏相干问题的可行性研究

在应用传统的偏相干方法进行噪声源识别时,计算条件输入和耦合较为繁琐、复杂。文章首先通过对偏相干和乔列斯基分解的分析,得到两者之间的关系;进一步运用数学归纳法,从理论上证明了乔列斯基分解应用于偏相干问题的可行性;然后指出对归一化互谱矩阵进行乔列斯基分解,可直接得到各源的贡献比大小;最后给出了应用乔列斯基分解解决偏相干问题的一般步骤。舱段试验验证了该方法的可行性和实时性。     

基于虚拟装配体绑定的产品层次化装配序列生成

装配是产品制造中的重要环节之一.计算机辅助装配顺序的生成为实施快速、灵活、优化的装配,提高装配质量和效率提供有力的支持.任务层次与分解方法的设计是层次任务网规划方法应用的关键技术.文中提出了一种基于虚拟装配体绑定的底层装配操作实施机制,在此基础上建立了产品装配序列生成的任务分解策略.装配实例表明,该方法在产品装配序列生成中具有建模层次清晰、任务分解策略易于制定、装配序列生成比较灵活的特点.     

复杂情况下大规模船队规划模型与求解方法

以某大型航运企业船队为例进行实证分析,利用Benders分解算法对在多航线复杂情况下,由多种类型船舶构成的大规模船队规划问题进行求解,验证所提出的模型及算法对于大规模实际问题的应用效果.结果表明,该方法实现了多航线、多型船、大规模混合整数船队规划问题的优化求解,能够为大型航运企业进行船队规划决策提供支持.     

A theoretical foundation for the relationship between generalized origin–destination matrix and flow matrix based on ordinal graph trajectories

This paper shows the relationship between flow, generalized origin–destination (OD), and alternative route flow from a set of ordinal graph trajectories. In contrast to traffic assignment methods that employ OD matrix to produce flow matrix, we use ordinal trajectory on a network graph as input and produce both the generalized OD matrix and the flow matrix, with the alternative and substitute route flow matrices as additional outputs. By using linear algebra‐like operations on matrix sets, the relationship between network utilization (in terms of flow, generalized OD, alternative route flow, and desire line) and network structure (in terms of distance matrix and adjacency matrix) are derived. Copyright © 2012 John Wiley & Sons, Ltd.