Forged and tampered data frames should be identified and filtered out to ensure network security and efficiency. However， the existing schemes usually fail to work when verification devices are attacked or maliciously controlled in the Software Defined Network （SDN）. To solve the above problem， a blockchain-based data frame security verification mechanism was proposed. Firstly， a Proof of Frame Forwarding （PoFF） consensus algorithm was designed and used to build a lightweight blockchain system. Then， an efficient data frame security verifying scheme for SDN data frame was proposed on the basis of this blockchain system. Finally， a flexible semi-random verifying scheme was presented to balance the verification efficiency and the resource cost. Simulation results show that compared with the hash chain based verifying scheme， the proposed scheme decreases the missed detection rate significantly when an equal proportion of switches are maliciously controlled. Specifically， when the proportion is 40%， the decrease effect is very obvious， the missed detection rate can still be kept no more than 32% in the basic verification mode， and can be further reduced to 7% with the assistance of the semi-random verifying scheme. Both are much lower than the missed detection rate of 72% in the hash chain based verifying scheme， and the resource overhead and communication cost introduced by the proposed mechanism are within a reasonable range. Additionally， the proposed scheme can still maintain good verification performance and efficiency even when the SDN controller is completely unable to work.

为了克服传统密度估计方法受限于算法配置工作量高、高等级密度样本数量有限等因素无法大规模应用的缺点,提出一种基于监控视频的全景密度估计方法。首先,通过自动构建场景的权重图消除成像过程中射影畸变造成的影响,该过程针对不同的场景自动鲁棒地学习出对应的权值图,从而有效降低算法配置工作量;其次,利用仿真模拟方法通过低密度等级样本构建大量高密度等级样本;最后,提取训练样本的面积、周长等特征用于训练支持向量回归机(SVR)来预测每个场景的密度等级。在测试过程中,还通过二维图像与全景地理信息系统(GIS)地图的映射,实时展示全景密度分布情况。在北京北站广场地区的深度应用结果表明,所提全景密度估计方法可以准确、快速、有效地估计复杂场景中人群密度动态变化。

In order to discover the special behaviors of Sub Complex System (SCS) under intervention, the authors proposed the concept of contrast inequality, proposed and implemented the algorithm for mining the segmentwise intervention; by imposing perturbance intervention on SCS, the authors proposed and implemented the causality discovery algorithm. The experiments on the real data show that segmentwise intervention algorithm discovers new intervention rules, and the causality discovery algorithm discovers the causality relations in the air pollution data set, and both are difficultly discovered by traditional methods.