This paper proposes a new queuing model and adaptive scheduling scheme which realizes multi-class QoS mechanism under DiffServ architecture. The queuing model is composed of two parallel output subqueues, each output ...This paper proposes a new queuing model and adaptive scheduling scheme which realizes multi-class QoS mechanism under DiffServ architecture. The queuing model is composed of two parallel output subqueues, each output sub-queue adopts random drop algorithm by setting different buffer threshold for different class traffic, so it can provide multi-class QoS. The new proposed scheduling scheme which adaptively changes the parameter A can guarantee the performance target of high class traffic, in the mean time, improve the QoS of low classes traffic.展开更多
翼型及机翼优化设计中,设计变量的个数对优化算法的收敛速度及代理模型的精度有很大的影响。因此,在精确描述翼型的同时,发展较少设计变量的翼型参数化方法对翼型优化设计有着重要的意义。本文基于CST(class function/shape function tr...翼型及机翼优化设计中,设计变量的个数对优化算法的收敛速度及代理模型的精度有很大的影响。因此,在精确描述翼型的同时,发展较少设计变量的翼型参数化方法对翼型优化设计有着重要的意义。本文基于CST(class function/shape function transformation)翼型参数化方法对Kriging模型的预测精度进行研究,并采用改进的粒子群优化算法构建气动优化设计系统。某亚声速机翼单点减阻设计及超临界翼型的稳健性设计表明该系统具有较高的设计质量,方法可靠,有较高的工程应用前景。展开更多
文摘This paper proposes a new queuing model and adaptive scheduling scheme which realizes multi-class QoS mechanism under DiffServ architecture. The queuing model is composed of two parallel output subqueues, each output sub-queue adopts random drop algorithm by setting different buffer threshold for different class traffic, so it can provide multi-class QoS. The new proposed scheduling scheme which adaptively changes the parameter A can guarantee the performance target of high class traffic, in the mean time, improve the QoS of low classes traffic.
文摘翼型及机翼优化设计中,设计变量的个数对优化算法的收敛速度及代理模型的精度有很大的影响。因此,在精确描述翼型的同时,发展较少设计变量的翼型参数化方法对翼型优化设计有着重要的意义。本文基于CST(class function/shape function transformation)翼型参数化方法对Kriging模型的预测精度进行研究,并采用改进的粒子群优化算法构建气动优化设计系统。某亚声速机翼单点减阻设计及超临界翼型的稳健性设计表明该系统具有较高的设计质量,方法可靠,有较高的工程应用前景。
