具有自适应温度补偿的自动变速器起步控制

根据电控机械式自动变速器起步性能指标及起步过程离合器转矩传递特性，提出了确定离合器目标接合速度的加权方法．该方法兼顾了驾驶员起步意图及自动变速器起步性能要求．模仿人工控制的-特点，同时考虑液压油温度变化的影响，提出了基于误差峰值采样的自适应温度补偿的起步过程离合器接合速控制算法．实验表明，该算法既满足了3种典型起步工况(爬行起步、正常起步和急起步)的性能要求，又提高了系统对温度变化的适应能力．     

基于多小波分析的暂态信号去噪方法研究

由于多小波分析方法的特殊性,需要对原始信号进行预处理.不同的预处理方法对多小波去噪效果有着非常明显的影响.文中介绍了多小波理论,选用GHM多小波,采用了4种预处理方法对信号进行预处理,分析了基于这4种方法进行信号去噪的结果,并与单小波处理结果进行了比较.将比较结果应用于检测系统输出电压信号进行分析,结果表明,利用多小波变换方法对暂态信号去噪比用单小波方法具有明显的优越性.     

基于交通需求的路网交通拥堵评价模型

文章提出了微观、中观、宏观3个层次上的用于评价交通拥堵状态的交通拥堵指数指标;确定了基于分层抽样技术的最低路段抽样方法,并采用综合评价法,以从时间角度表征交通需求的指标——车辆行驶时间（VHT）作为加权模型中的权重,建立了基于交通需求的交通拥堵评价加权模型。最后,以北京市为例,对北京市五环内路网的拥堵状况进行了评价。     

Application of advanced sampling for efficient probabilistic traffic modelling

In probabilistic traffic models, consideration of stochasticity in the dynamics of traffic gives a closer representation of a traffic system in comparison to that of a deterministic approach. Monte Carlo simulation is a broadly accepted method to consider variations in traffic within modelling. In this contribution, the possibility of increasing the efficiency of probabilistic traffic flow models using Monte Carlo simulation is analysed using variance reduction techniques and sequencing, for varied capacity and traffic demand values. The techniques of Importance Sampling, Latin Hypercube Sampling and Quasi-Random Sequencing are compared in a dynamic macroscopic traffic model to demonstrate the effectiveness of these techniques for reduction of the computational load when considering multiple input variations. Demonstration of their efficiency in traffic modelling is expected to lead to a wider application of the methods in practice.     

整体式交流发电机的泄漏电流

介绍整体式交流发电机的泄漏电流及其测试方法，分析影响泄漏电流的各种因素，阐述在设计和制造中如何降低泄漏电流。     

电厂煤取样装置的技术改造

取样装置是浙江浙能兰溪发电有限责任公司的输煤皮带设备，该公司技术人员通过对取样装置频繁发生堵煤而无法正常投运的原因进行分析，提出合理的各机构改进方案，并对其实施技术改造，取得了较满意的效果。     

A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction

This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.     

Stochastic analysis of dynamic interaction between train and railway turnout

The performance of a railway turnout (switch and crossing) is influenced by a large number of input parameters of the complex train–turnout system. To reach a robust design that performs well for different traffic situations, random distributions (scatter) of these inputs need to be accounted for in the design process. Stochastic analysis methods are integrated with a simulation model of the dynamic interaction between train and turnout. For a given nominal layout of the turnout, using design of experiments methodology and a two-level fractional factorial screening design, four parameters (axle load, wheel–rail friction coefficient, and wheel and rail profiles) are identified to be the most significant. These parameters are further investigated using a three-level full factorial design and stochastic analysis. The random distributions of transverse wheel profile and set of transverse rail profiles along the switch panel are accounted for by the Karhunen–Loève expansion technique. The influence of the random distributions of the input parameters on the statistical outputs of wheel–rail contact forces, wear and rolling contact fatigue is assessed using Latin hypercube sampling to generate a number of stochastic load realizations.     

变时窗滤波技术压缩瞬变电磁测深数据

研究目的:对采样密度高、数据量大的等间隔瞬变电磁测深数据进行反演解释时,必须对数据进行无损滤波和压缩,得到反映信号衰减规律的不等间隔类对数采样瞬变电磁测深数据.本文特对此进行研究.研究结论:在瞬变测深晚期,尽管数据信噪比非常低,利用变时窗滤波技术仍然可以获得符合规律的瞬变衰减曲线.因此,变时间(时窗)依赖滤波方法是对密度非常大的等间隔瞬变电磁测深数据进行无损压缩的良好工具.     

相关变量下失效概率的计算机模拟

岩土工程中基本变量大多数都为互相关，因此在计算其可靠度指标时要考虑其相关性。Ｍｏｎｔｅ－Ｃａｒｌｏ模拟法是求解失效概率Ｐｆ和可靠度指标β较为精确的方法，但只能对自相关的随机向量进行抽样；对于互相关的不同分布随机变量，则只能根据其联合概率分布求出其边缘分布和条件分布，化为独立的变量分布，然后在独立空间中抽样，但大多数情况下无法知其联合概率分布。