基于DPM模型的街谷内颗粒物扩散特性研究

通过采用离散相模型（DPM）,对等高对称街谷和非等高不对称街谷在参考风速Zref=2m/s时,粒径dp=10μm的固体颗粒物在街谷内的扩散分布进行了数值研究。计算湍流模型选用重正化群两方程模型,计算结果表明在城市风场作用下会导致街谷内空气呈漩涡流动,街谷几何形状变化影响街谷内污染物浓度分布,且在深街谷（B/H＝0.5）和上游建筑高于下游建筑的非对称街谷（H1/H2=2）中街谷内空气颗粒无量纲浓度远高于同类型其它几何尺寸街谷。     

The dynamic power management for embedded system with Poisson process

The mass of the embedded systems are driven by second batteries, not by wired power supply. So saving energy is one of the main design goals for embedded system. In this paper we present a new technique for modelling and solving the dynamic power management (DPM) problem for embedded systems with complex behavioural characteristics. First we model a power-managed embedded computing system as a controllable Flow Chart. Then we use the Poisson process for optimisation, and give the power management algorithm by the help of Dynamic Voltage Scaling (DVS) technology. At last we built the experimental model using the PXA 255 Processors. The experimental results showed that the proposed technique can achieve more than12％ power saving compared to other existing DPM techniques.     

基于DPM模型的街谷内颗粒物扩散特性研究

通过采用离散相模型（DPM），对等高对称街谷和非等高不对称街谷在参考风速Zref=2m/s时，粒径dp=10μm的固体颗粒物在街谷内的扩散分布进行了数值研究。计算湍流模型选用重正化群两方程模型，计算结果表明在城市风场作用下会导致街谷内空气呈漩涡流动，街谷几何形状变化影响街谷内污染物浓度分布，且在深街谷（B/H＝0.5）和上游建筑高于下游建筑的非对称街谷（H1/H2=2）中街谷内空气颗粒无量纲浓度远高于同类型其它几何尺寸街谷。     

The dynamic power management for embedded system with Poisson process

The mass of the embedded systems are driven by second batteries, not by wired power supply. So saving energy is one of the main design goals for embedded system. In this paper we present a new technique for modelling and solving the dynamic power management （DPM） problem for embedded systems with complex behavioural characteristics. First we model a power-managed embedded computing system as a controllable Flow Chart. Then we use the Poisson process for optimisation, and give the power management algorithm by the help of Dynamic Voltage Scaling （DVS） technology. At last we built the experimental model using the PXA 255 Processors. The experimental results showed that the proposed technique can achieve more than 12% power saving compared to other existing DPM techniques.