手术机器人远程运动中心机构的误差分析方法

利用几何关系的建模与计算,研究了手术机器人中平行四杆型远程运动中心机构的误差问题.首先,对机构的误差进行识别和定义,使用直接线性化方法建立机构的误差传递模型;然后,用概率性方法和确定性方法分别讨论了机构的绝对精度和重复精度;最后,分析了机构各误差因素的敏感性.算例分析结果表明,使用IT6级精度可以达到较高的定位精度,通过敏感性分析可以找出影响机构定位精度的关键因素.     

一类特殊矩阵变量DLM及其预测

讨论了一类特殊矩阵变量ＤＬＭ的观测误差方差阵Ｖｔ为未知常数矩阵情况下的分布，并给出了修正递推及其预测公式。     

Modelling exchange rate dynamics new perspectives from the frequency domain

In this paper the dynamics of foreign exchange rates is sought to be studied via new frequency domain techniques. Stationarity properties of the rates are analysed via a unit root test as well as a test based on the evolutionary spectrum. Linearity and Gaussianity are analysed via bispectral tests and compared with the more frequently employed time domain tests, such as the McLeod-Li and Tsay tests. Finally, an evaluation of the out-of-sample forecasting properties for eight methods—Random Walk, ARMA, Bilinear, State dependent model, dynamic linear model, ARCH, GARCH, and Garch-in-mean—is made. The methods used here seem to shed a great deal of light on hitherto neglected aspects of exchange rate dynamics.     

变负载下直流电机双闭环调速系统建模

针对直流电机双闭环调速系统负载经常发生变化的情况,在适用于恒定负载的直流电机双闭环调速系统简化模型基础上,分析了该模型在变负载情况下其模型参数变化关系,通过直流电机系统驱动电流和电机转动状态建立了直流电机双闭环调速系统动力学模型.通过实测系统转速响应,利用改进遗传算法和曲线拟合进行模型参数辨识,从而得到变负载情况下直流电机双闭环调速系统模型.实验结果表明,模型的动力学响应贴近实际系统,可以很好地代替实际系统进行控制系统设计.     

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.