以色列科学家发现制取氢气新方法

以色列科学家研究发现，利用太阳能提取出的高纯度锌，也许可以用来制取氢气，为燃料电池车辆提供动力。     

车辆全速巡航系统的干扰解耦鲁棒控制

建立了集成被控车辆及车辆间纵向行驶动力学特性的新型全速巡航系统模型, 针对模型中存在的外界干扰、参数不确定性及非线性特性, 提出了一种干扰解耦鲁棒控制方法. 该方法首先根据非线性干扰解耦（nonlinear disturbance decoupling, 即NDD）原理, 实现对模型中确定部分外界干扰的解耦, 在此基础上, 利用变结构控制（variable structure control, 即VSC）中滑动模的不变性特性, 消除模型中余下不确定性部分的影响, 最后将其应用于纵向行驶车辆全速自适应巡航控制（adaptive cruise control, 即ACC）系统, 并通过仿真计算验证其控制效果. 结果表明, 结合NDD原理及VSC方法设计的车辆全速ACC系统, 不仅对被控车辆内部各种不确定因素具有良好的鲁棒性能, 而且能够较好的消除被控车辆低速走停（stop and go, 即SG）工况下的强非线性特性, 以及高速行驶中前导车频繁加/减速的外部干扰影响. 此外, 控制系统结构简单, 可实现其控制性能的全局最优化.     

四轮转向车辆分数阶控制方法研究

四轮转向系统(4WS)可根据前轮转向和车辆的状态通过后轮的转向提高车辆转弯能力, 同样, 利用这一转向系统可以改进车辆的横向稳定性和操纵性能. 当前轮转向相同时四轮转向车辆的转弯半径大于两轮转向, 而且普通的控制方法不考虑前轮转向的动态过程影响. 本文利用分数阶导数理论提出一种新的四轮转向控制方法, 其不仅考虑前轮转角和横摆角速度的大小, 而且也考虑到转向角速度的影响. 同时也给出控制方法的一些设计方法, 数值计算结果验证了该控制方法的有效性. 通过对两轮转向和四轮转向的转弯车辆的侧偏角、车辆绕质心的横摆角速度和转弯半径等动力学和运动学特性的计算结果比较, 本文的控制方法对四轮转向车辆在转向过程中的瞬态响应有所改进, 并减小了转弯半径.     

基于多系统综合控制的车辆侧翻稳定性研究

为提高车辆抗侧翻能力减小其侧翻的危险性,建立了11自由度整车侧翻模型。根据各系统侧翻控制权重及有效作用范围,提出一种基于转向、悬架和制动的多系统侧翻控制策略并设计了相关控制器。对系统单独控制及综合控制进行了仿真对比分析。结果表明,综合控制策略在提高车辆抗侧翻能力的同时也很好的改善了车辆侧向稳定性。     

车辆转弯制动ABS模糊控制及稳定性仿真分析

在Matlab／Simlink下建立了八自由度车辆系统的仿真模型。采用模糊控制方法,设计了基于滑移率的ABS控制算法。针对车辆转弯制动工况,对15m／s、20m／s、25m／s三种车速进行了仿真分析。仿真结果表明所建立的ABS控制器能有效提高车辆的制动性能以及中低车速下的稳定性,但定性分析表明车辆在高速工况下出现不稳定现象。为此利用稳定性判定式,对车辆转弯制动ABS控制下的稳定性做了定量判定。判定结果表明,高速时车辆仅有ABS控制并不能达到稳定性控制的要求。     

运用Lyapunov指数方法的车辆横向运动混沌分析及其滑模变结构控制

建立了考虑车辆横摆、侧向以及侧倾运动的3自由度非线性整车模型：运用Lyapunov指数方法对所建立的非线性整车模型进行了混沌的数值仿真分析．通过最大Lyapunov指数图和分岔图发现,车辆的横向运动非常复杂,包含了倍周期、拟周期以及混沌运动,对于车辆极限工况时的横向运动稳定性是不利的．利用滑模变结构控制（sliding mode variable structure control,SMVSC）方法,设计了SMVSC控制器,对车辆横向运动中的混沌进行了控制．为了减少SMVSC控制系统的抖振,进一步提高车辆在极限工况下行驶的横向运动稳定性,采用了幂次趋近律,利用模糊控制的方法实现了趋近律的自适应策略．最后将所设计的自适应趋近律的SMVSC系统在Matlab中进行了仿真,并将未加控制,SMVSC控制以及自适应趋近SMVSC控制三种仿真结果进行了对比分析,发现采用了自适应趋近的SMVSC控制对混沌的控制效果比其他的都要好,有效抑制了车辆横向运动中的混沌,显著提高了车辆在极限工况下行驶的横向稳定性,充分证明了该控制策略是有效的．     

基于两级模糊控制的交叉路口多相位控制

用模糊控制方法对交叉路口的交通信号进行控制,提出一种面向城市交叉路口的交通信号两级模糊控制系统.该系统能够分散处理交通信息参数,有效减少模糊规则数,易于提取模糊规则,实现相位顺序、绿信比和周期均随着交通状况的变化而自适应变化;并利用混沌优化对模糊控制器的隶属度函数进行优化搜索,从而提高交叉路口的通行效率,减少车辆的延误...     

高速工况智能车辆转向系统混杂控制研究

通过分析高速工况下交通事故的主要类型及驾驶员的3种基本驾驶行为,可知该工况下转向系统既有离散状态又有连续状态的特性,进而提出基于混杂系统理论的智能车辆转向系统多模式切换控制策略,并利用跨道时间和安全距离作为切换规律.以线控转向系统为执行机构,分别设计阻尼模式、保持模式和换道模式控制器.其中保持模式引入了一种控制预瞄点处侧向偏差的一阶导数和二阶导数的方法,不但有效地防止了超调,而且减少了车辆回到车道中心线的时间;换道模式利用模型预测控制算法,加入多个约束,提高了车辆换道过程的稳定性.从混杂系统稳定性的角度出发,设计了简单有效的稳定性监督器,防止车辆失稳.最后,在Car Sim/Simulink联合仿真平台及Car Sim/Lab VIEW硬件在环测试平台上验证了本方法的有效性和切换过程的稳定性.     

湿式超微粉碎提取三七总皂苷及粒径对提取率的影响

目的研究湿式超微粉碎法与传统提取方法对三七总皂苷提取的影响及粉碎粒径与提取率的关系。方法以三七总皂苷为指标,高效液相法测定提取率以及粒径分布仪测定粒径。结果湿式超微粉碎法提取率较普通回流法略有提高,但普通回流法需要2h,而湿式超微粉碎法只需10min,节省了时间;超微粉碎随时间的增加,粒径越来越小,但提取率并没有增加。结论湿式超微粉碎与提取同步完成,极大的提高了提取的效率;粉碎粒径与提取率并不呈现比例关系。     

星敏感器星点聚类提取方法

星敏感器现有的星点提取方法运算时间长、存储需求大,从而限制星敏感器姿态初始捕获时间、轻小化等性能指标的提高.针对以上问题,提出了一种星敏感器星点聚类提取方法.首先,介绍和分析了星点聚类提取方法中的预处理滤波算法和聚类算法;然后,给出了该方法在北京控制工程研究所新研小型星敏感器中的实现方式和实现效果,该星敏感器在探月三期月地高速再入返回飞行器中成功实现了首次在轨飞行实验;最后,通过星敏感器观星实验对星点传统提取方法和星点聚类提取方法进行了比对验证,并通过小型星敏感器在轨飞行实验对星点聚类提取方法进行了在轨验证.实验显示,该方法提取星点所需时间最大约为传统方法的16%,星图存储需求不到传统方法的1%,且星点提取正确,在轨表现良好.结果表明,星敏感器星点聚类提取方法能有效减小星点提取所需时间,不需大容量星图存储器件,且逻辑简单、移植性好,适用于各类光学成像敏感器.     

基于图像式行驶记录仪的汽车-自行车交通冲突特性研究

汽车与自行车事故在我国交通事故总数中占有重要比例。为了研究汽车与自行车交通冲突／事故特性,本文选用50辆出租车,利用图像式汽车行驶记录仪在北京市开展了为期一年的交通冲突调查,从冲突类型、冲突时间、冲突地点及交通控制方式、冲突车速及交通流、驾驶员视线遮挡等方面综合分析汽车一自行车冲突特性,研究结果对道路交通安全管理、道路设计及事故预防技术具有重要的意义。     

城市交通拥堵的街区缓冲定价模型及其仿真

随着城市机动车保有量的不断增长,城市主干路网供给能力出现严重不足,适当开放一些封闭街区并进行拥堵收费可以作为缓解主干路网交通压力的一项重要措施。针对城市主干路的交通拥堵问题,以区域路网车辆整体运行时间最短为优化目标,以缓冲街区的拥堵收费价格为决策变量,以交通弹性需求和动态分配以及缓冲街区路网饱和度等为限制条件,建立了缓冲街区定价优化模型,并给出了基于VISSIM软件的仿真求解流程。通过实例仿真结果表明,开放附近街区缓冲并采用模型给出的最优定价方案,既可以分担主干路部分车流从而保障了主干路通畅运行,又不超过缓冲区容量限制,提高了整体路网的运行效率。     

基于交通流特征的可变信息板布局优化方法

为了引导交通流的合理分布并确定城市路网中可变信息板的布局方案,本文提出了一种基于路段交通流特征信息量评估的可变信息板布局优化方法。利用图论对城市路网进行描述,建立了基于信息熵的路段交通流特征信息量评估模型。并结合实际路网的拓扑结构特性,设计了可变信息板布局方案的初步生成方法。12节点的网络实例验证显示:该布局方法是有效的,且便于交通管理者所理解。同时,提出的交通流特征提取方法具有较强的应用性,可为道路规划与安全评估提供基础信息。     

Analysis of the relationship between LSTM network traffic flow prediction performance and statistical characteristics of standard and nonstandard data

The effectiveness of road traffic control systems can be increased with the help of a model that can accurately predict short-term traffic flow. Therefore, the performance of the preferred approach to develop a prediction model should be evaluated with data sets with different statistical characteristics. Thus a correlation can be established between the statistical properties of the data set and the model performance. The determination of this relationship will assist experts in choosing the appropriate approach to develop a high-performance short-term traffic flow forecasting model. The main purpose of this study is to reveal the relationship between the long short-term memory network (LSTM) approach's short-term traffic flow prediction performance and the statistical properties of the data set used to develop the LSTM model. In order to reveal these relationships, two different traffic prediction models with LSTM and nonlinear autoregressive (NAR) approaches were created using different data sets, and statistical analyses were performed. In addition, these analyses were repeated for nonstandardized traffic data indicating unusual fluctuations in traffic flow. As a result of the analyses, LSTM and NAR model performances were found to be highly correlated with the kurtosis and skewness changes of the data sets used to train and test these models. On the other hand, it was found that the difference of mean and skewness values of training and test sets had a significant effect on model performance in the prediction of nonstandard traffic flow samples.     

Forecasting the Antwerp maritime steel traffic flow: A case study

This paper uses multivariate time series models to specify the maritime steel traffic flow in the port of Antwerp. The time series considered are the total outgoing and total incoming maritime steel traffic and the total steel production in the EEC. The obtained time series models provide useful insight into the general behaviour of the maritime steel traffic flow during the period 1971–82. In particular, they provide a quantitative interpretation of important changes which took place in the European steel industry during that period. The multivariate time series models produce forecasts which are a substantial improvement over those obtained by univariate time series models. This is especially the case for the series of total incoming maritime steel traffic in the port of Antwerp, when differencing and transformation of the original data are applied.     

偏离设计工况时离心泵内部流场的数值模拟

通过高效数值模拟的方法分析通流部分的气动性能,对叶轮进行优化改进,扩容改造,从而提高效率,可靠性,安全性.主要采用FLUENT,在双参考坐标系下,利用有限体积法对雷诺时均Navier-Stokes方程进行数值离散,选用标准k-s湍流模型,SIM-PLEC方法求解,对原模型泵进行非设计工况下内部流场的叶轮和蜗壳的耦合数值模拟,主要是变转速和变流量调节.得出变转速调节下全流场的速度和压力分布均与原模型相似,但转速过低时则出现明显不同;变流量模拟时,无论是大流量还是小流量,在叶轮流道和蜗壳区均出现了较为严重的回流和漩涡区,并对这一现象进行了分析.最后对原模型蜗壳提出了一些改造意见,指出了偏心蜗壳在蜗舌处的回流情况较对称蜗壳好一些.     

交通流宏微观参数关系方程组实证分析

本文以James H．Banks关于平均时间间隔的研究为基础，提出交通流宏微观参数关系方程组，理论上较全面地描述两类交通参数的关系；应用实测交通数据对该方程组进行数据实证。分析表明，使用关系方程组产生的偏差不仅与统计时间间隔有关，而且与检测点的位置、交通状态相关。通过分析关系方程组的推导可知，偏差源于两个等式在理论上与实际运用中的不同。     

Statistical analyses of freeway traffic flows

This paper concerns the exploration of statistical models for the analysis of observational freeway flow data, and the development of empirical models to capture and predict short‐term changes in traffic flow characteristics on sequences of links in a partially detectorized freeway network. A first set of analyses explores regression models for minute‐by‐minute traffic flows, taking into account time of day, day of the week, and recent upstream detector‐based flows. Day‐ and link‐specific random effects are used in a hierarchical statistical modelling framework. A second set of analyses captures day‐specific idiosyncrasies in traffic patterns by including parameters that may vary throughout the day. Model fit and short‐term predictions of flows are thus improved significantly. A third set of analyses includes recent downstream flows as additional predictors. These further improvements, though marginal in most cases, can be quite radically useful in cases of very marked breakdown of freeway flows on some links. These three modelling stages are described and developed in analyses of observational flow data from a set of links on Interstate Highway 5 (I‐5) near Seattle. Copyright © 2002 John Wiley & Sons, Ltd.     

Short‐term prediction of motorway travel time using ANPR and loop data

Travel time is a good operational measure of the effectiveness of transportation systems. The ability to accurately predict motorway and arterial travel times is a critical component for many intelligent transportation systems (ITS) applications. Advanced traffic data collection systems using inductive loop detectors and video cameras have been installed, particularly for motorway networks. An inductive loop can provide traffic flow at its location. Video cameras with image‐processing software, e.g. Automatic Number Plate Recognition (ANPR) software, are able to provide travel time of a road section. This research developed a dynamic linear model (DLM) model to forecast short‐term travel time using both loop and ANPR data. The DLM approach was tested on three motorway sections in southern England. Overall, the model produced good prediction results, albeit large prediction errors occurred at congested traffic conditions due to the dynamic nature of traffic. This result indicated advantages of use of the both data sources. Copyright © 2008 John Wiley & Sons, Ltd.     

Comparison of two non‐parametric models for daily traffic forecasting in Hong Kong

The most up‐to‐date annual average daily traffic (AADT) is always required for transport model development and calibration. However, the current‐year AADT data are not always available. The short‐term traffic flow forecasting models can be used to predict the traffic flows for the current year. In this paper, two non‐parametric models, non‐parametric regression (NPR) and Gaussian maximum likelihood (GML), are chosen for short‐term traffic forecasting based on historical data collected for the annual traffic census (ATC) in Hong Kong. These models are adapted as they are more flexible and efficient in forecasting the daily vehicular flows in the Hong Kong ATC core stations (in total of 87 stations). The daily vehicular flows predicted by these models are then used to calculate the AADT of the current year, 1999. The overall prediction and comparison results show that the NPR model produces better forecasts than the GML model using the ATC data in Hong Kong. Copyright © 2006 John Wiley _ Sons, Ltd.