基于H运算的动态网络重要节点识别方法

传统K-shell网络重要节点识别方法迭代时需网络全局拓扑信息,而且难以应用于动态网络。为解决该问题,提出基于邻域优先异步H运算的动态网络重要节点识别方法。首先,证明该算法收敛于Ks值,其次以各节点的度作为h指数初始值;然后,通过节点h指数排序和邻居节点h指数变化选择更新节点,同时针对动态网络节点的增减数目和最大度,修改h指数适应拓扑变化,直至算法收敛并找到重要节点。仿真实验结果表明,该方法通过邻居节点局部信息且以更高效率找到动态网络的重要节点,收敛时间在静态网络中较随机选择更新节点法与变化邻居选点法分别下降77.4%和28.3%,在网络拓扑变化后分别下降84.3%和38.8%。

基于改进H∞算法的线控转向系统仿真研究

针对汽车线控转向系统的鲁棒性问题,对汽车线控转向的原理和结构方面进行了研究;对汽车线控转向的动力学模型进行了建立以及简化;对汽车线控转向控制系统进行了分析与设计。以H∞运算为基础,利用反求法,避开了加权函数的选择,通过观察H∞运算中S/T奇异值曲线,运用符合线控转向鲁棒性能要求的预期S/T曲线逆转了该闭环系统,构建出了闭环传递函数,提出了一种基于改进H∞算法的线控转向系统。通过对线控转向系统的时间域和频率域的分析,以及线控转向系统模型干涉和参数扰动下的仿真结果分析,验证了改进H∞算法的控制策略的准确性。研究结果表明,基于改进H∞算法的线控转向系统在保证了鲁棒稳定性的前提下,具有更好的响应性能;并且改进的控制器比传统的控制器具有更好的鲁棒性。

Fast Motion Estimation Algorithm with Edge Alignment for H.264 Encoder

Image sequences processing and video encoding are extremely time consuming problems. The time complexity of them depends on image contents. This paper presents an estimation of a block motion method for video coding with edge alignment. This method uses blocks of size 4 × 4 and its basic idea is to find motion vector using the edge position in each video coding block. The method finds the motion vectors more accurately and faster than any known classical method that calculates all the possibilities. Our presented algorithm is compared with known classical algorithms using the evaluation function of the peak signal-to-noise ratio. For comparison of the methods we are using parameters such as time, CPU usage, and size of compressed data. The comparison is made on benchmark data in color format YUV. Results of our proposed method are comparable and in some cases better than results of standard classical algorithms.