高维Cotes优化积分数值算法及其余项估计

利用一维Cotes公式及高维积分Cartesian积空间上的求积法则,将Cotes公式推广到了n维空间上,并给出了简单的误差估计.该公式具有比文[4],[5],[6]相关结果更小的误差和更高的收敛阶等优点.     

特征函数的性质

得到了特征函数、条件特征函数的一致可微性和条件特征函数的反演公式.     

颜色视觉匹配中显示器颜色色差阈值的评价

为了对阴极射线管(Cathode ray tube,CRT)显示器颜色恰可辨别的小色差阈值进行评价,在中性灰背景下,用编制的颜色匹配程序,在CRT显示器上随机产生颜色,分别调节程序中R、G、B通道的值,使匹配色与随机色达到视觉上的一致,进行颜色视觉匹配实验,测量CRT显示器上随机色和匹配色的L*a*b*值.用不同的色差公式对实验数据分别从明度、色调、饱和度进行比较分析,色差公式评价小色差的性能是CIEDE2000优于CMC(2∶1)和CIEL*a*b*.在a*b*图上绘制实验颜色恰可辨别图.不同色差公式计算的CRT显示器恰可辨别小色差阈值不同.     

矩形微槽道纳米流体饱和沸腾临界热流密度特性

针对纳米流体在微小尺度传热领域的应用,在常压下对微槽道中纳米流体的流动沸腾临界热流密度进行实验研究。分别以体积浓度为0.2%、0.5%的水基Al2O3纳米流体为工质进行试验,研究不同质量流速、槽道尺寸以及体积浓度等因素对沸腾CHF的影响。对比水为工质实验结果,表明:槽道尺寸、质量流速对于水-Al2O3纳米流体和纯水的CHF影响一致。其它参数一定的工况下,纳米流体CHF比纯水大,且随着纳米流体体积浓度增大,出口壁面过热度会增大。最后介绍一个微槽道沸腾CHF的预测模型,在评价其不足的基础上提出一个关于CHF的预测公式,与实验数据进行对比,验证该公式的适用性。     

Novel delay-distribution-dependent stability analysis for continuous-time recurrent neural networks with stochastic delay

<正>In this paper,the problem of delay-distribution-dependent stability is investigated for continuous-time recurrent neural networks(CRNNs) with stochastic delay.Different from the common assumptions on time delays,it is assumed that the probability distribution of the delay taking values in some intervals is known a priori.By making full use of the information concerning the probability distribution of the delay and by using a tighter bounding technique(the reciprocally convex combination method),less conservative asymptotic mean-square stable sufficient conditions are derived in terms of linear matrix inequalities(LMIs).Two numerical examples show that our results are better than the existing ones.     

激光触发多级多通道开关研究

研究了200kV激光触发多级多通道开关的运行机理,该开关在直流电压下以四倍频Nd:YAG激光器为触发源进行的实验结果表明：开关触发延迟时间及其抖动随开关电压、激光能量和充气压力上升呈指数下降趋势,对混合气体则随气体密度上升而上升。随透镜焦距的增大延迟时间及其抖动呈上升趋势。该开关实现了多通道放电。     

The (S−1,S) inventory model and its counterparts in queueing theory

We explore the relationship between the ($S?1,S$) inventory model and three well-known queueing models: the Erlang loss system, the machine-repair model and a two-node Jackson network. Exploiting this relationship allows us to obtain key performance measures of the ($S?1,S$) model, like the so-called virtual outdating time, the number of items on the shelf in steady state, the long-run rate of unsatisfied demands and the distribution of the empty shelf period.