基于二重差分法的光流场运动检测

对视频图像序列中运动目标的检测,是计算机视觉领域长期关注的问题.传统的Horn-Schunck光流算法非常耗时,不利于在视频图像序列中做实时分析,故提出采用结合背景差分法与帧间差分法的二重差分法(DS)来做光流算法的预处理.实验结果也表明二重差分法的光流算法(DSOF)比单纯用Horn-Schunck方法计算光流效率更高,用时更短,能更好地满足对视频图像序列做实时运动检测分析的需求.     

高炉炉顶布料的自学习控制

传统的炉顶料流调节阀开度控制是由生产操作人员凭自身经验进行人工操作,难以达到最为合理的布料效果。采用自学习控制算法使料流调节阀能根据不同的原料特性及炉况条件,自动的、适时的调节布料速度,以达到理想的布料效果。笔者在本钢3、4高炉开发成型,红钢3高炉成功稳定运用,效果良好,程序模型稳定。     

微小流量pVTt法气体流量标准装置的研制

介绍了20LpVTt法气体流量标准装置的设计原理和方案,并给出了装置中主要参数的测试方法及装置的不确定度评定。     

基于手势识别的机器人人机交互技术研究

研究了基于视觉的动态手势识别技术,采用基于肤色的高斯模型与改进的光流场跟踪算法结合的方 法,实现了复杂背景下实时的手势跟踪,具有快速和准确的特点,且具有较好的鲁棒性．对于动态手势识别器,采 用了隐马尔可夫模型（HMM）作为训练识别算法．考虑到动态手势特征本身的一些特点,对HMM 参数优化算法重 估式加以修正,调整了算法比例因子,从而推导了最佳状态链的确定算法、HMM 参数优化算法．最后将研究开发 的动态手势识别算法成功地应用到了基于网络的远程机器人控制系统中．     

网络流算法的分析及研究

为了改善网络的性能,提出了一种网络流的分析方法,流量控制与某个发送方和接收方之间的点到点的通信量控制有关,即解决网络中出现快发慢收的问题,还受到通信线路容量的限制.网络的流量问题是一个网络传输中的重要问题,这里给出的算法可以解决实际问题.     

基于非结构化控制流的进化测试方法

结构性测试中,进化测试是一种高效的自动生成高质量测试用例的技术.然而,当程序中出现非结构化特征时,在面向节点的测试标准下,其效率极其低下甚至不及随机测试生成方法.在考虑循环体内部出现跳转(goto、retum)语句的情况下,提出了一种适应度计算方法.该方法在结合传统进化测试适应度计算的基础上,充分考虑了循环次数对于进化搜索的影响.实验结果表明,本适应度函数可以很好地引导进化搜索,并以较小的代价生成测试用例.     

常用的基于控制流的测试充分性准则

本文介绍了几种在软件测试中常用的基于控制流的测试充分性评价准则。这些测试充分性准则适用于用过程式语言、面向对象语言编写的程序。测试人员可用这些准则判断究竟测试了多少代码,还有多少代码未测试到。     

BP神经网络隐层单元数的确定方法及实例

针对BP神经网络隐层单元数不易确定的问题,提出一种在传统的经验公式基础上快速确定隐层单元数的方法。该方法首先借助经验公式确定隐层单元数的取值范围,然后将其扩大,在这个扩大的范围内寻找最优值。以BP神经网络预测交通流量为例,解释说明了具体的步骤,以及网络模型的隐层结构对模型仿真精度的影响。结果表明,采用该方法可快速决定隐层单元数,在实例中采用16个隐层单元数为最佳。     

Investigation of viscosity effect on droplet formation in T-shaped microchannels by numerical and analytical methods

Both numerical and analytical models have been developed to explore the viscosity effect of the continuous phase on drop formation at a T-shaped junction in immiscible liquids. The effects of the generalized power law coefficient, the power law exponent and the yield stress on the mechanism of drop breakup, final drop size and frequency of drop formation are studied by using the numerical three-dimensional volume of fluid model. Droplets coalescence in Bingham fluids is observed in the beginning transient period. The effect of yield stress on drop extension is also discussed. Predictions of drop size by using an analytical force balance show satisfactory agreement with simulation results for Newtonian and power law fluids with different viscosity ratios. The approximation error associated with the analytical model for Bingham fluids is also acceptable. This analytical model can greatly shorten the prediction time as compared with the numerical model, which is helpful for on-line control.     

MEMS-based gas flow sensors

Micro-electro-mechanical system (MEMS) devices integrate various mechanical elements, sensors, actuators, and electronics on a single silicon substrate in order to accomplish a multitude of different tasks in a diverse range of fields. The potential for device miniaturization made possible by MEMS micro-fabrication techniques has facilitated the development of many new applications, such as highly compact, non-invasive pressure sensors, accelerometers, gas sensors, etc. Besides their small physical footprint, such devices possess many other advantages compared to their macro-scale counterparts, including greater precision, lower power consumption, more rapid response, and the potential for low-cost batch production. One area in which MEMS technology has attracted particular attention is that of flow measurement. Broadly speaking, existing micro-flow sensors can be categorized as either thermal or non-thermal, depending upon their mode of operation. This paper commences by providing a high level overview of the MEMS field and then describes some of the fundamental thermal and non-thermal micro-flow sensors presented in the literature over the past 30 years or so.