基于均值漂移的关联关键设施网络抗毁性评估方法

随着不同基础设施网络之间的关联性日渐增强,也使得其在遭受区域毁伤时更为脆弱.区别于传统的节点破坏方式,考虑伤害衰减的区域破坏方式会导致毁伤区域内的节点或边部分失效,继而对网络系统功能造成严重的影响.文章提出了基于区域毁伤方式下关联基础设施网络抗毁性评估模型,用于识别关联网络的脆弱区域并评估网络抗毁性,并设计一种基于均值漂移法的算法搜索关键区域,采用归一化的网络流指标来评估不同规模之间的网络抗毁性水平.最后,通过数值实验验证了算法的有效性,相比于遗传算法和现有的算法,文章所提出算法求解速度更快,也具有更好的可拓展性.

基于PPG的地铁司机驾驶疲劳识别研究

目的本文旨在验证利用光电容积描记术PPG信号代替心电图ECG信号对地铁司机进行疲劳识别的有效性。方法通过设备获取司机PPG信号,并进行心率变异性分析,提取时域、频域及非线性领域等驾驶疲劳相关特征,将瞳孔面积作为疲劳分类的标准,并采用均值漂移聚类划分疲劳等级,最后代入支持向量机SVM模型进行训练。结果地铁司机在驾驶状态下的PPG信号与ECG信号也具有高度一致性;同时疲劳识别的准确率达到85%~90%。结论可以利用PPG信号构建驾驶疲劳实时监测系统,实现地铁司机疲劳状态的实时监测与警示。

Color image segmentation using mean shift and improved ant clustering

To improve the segmentation quality and efficiency of color image,a novel approach which combines the advantages of the mean shift(MS) segmentation and improved ant clustering method is proposed.The regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm,and then they are represented by a graph in which every region is represented by a node.In order to solve the graph partition problem,an improved ant clustering algorithm,called similarity carrying ant model(SCAM-ant),is proposed,in which a new similarity calculation method is given.Using SCAM-ant,the maximum number of items that each ant can carry will increase,the clustering time will be effectively reduced,and globally optimized clustering can also be realized.Because the graph is not based on the pixels of original image but on the segmentation result of MS algorithm,the computational complexity is greatly reduced.Experiments show that the proposed method can realize color image segmentation efficiently,and compared with the conventional methods based on the image pixels,it improves the image segmentation quality and the anti-interference ability.

An algorithm for segmentation of lung ROI by mean-shift clustering combined with multi-scale HESSIAN matrix dot filtering

A new algorithm for segmentation of suspected lung ROI(regions of interest)by mean-shift clustering and multi-scale HESSIAN matrix dot filtering was proposed.Original image was firstly filtered by multi-scale HESSIAN matrix dot filters,round suspected nodular lesions in the image were enhanced,and linear shape regions of the trachea and vascular were suppressed.Then,three types of information,such as,shape filtering value of HESSIAN matrix,gray value,and spatial location,were introduced to feature space.The kernel function of mean-shift clustering was divided into product form of three kinds of kernel functions corresponding to the three feature information.Finally,bandwidths were calculated adaptively to determine the bandwidth of each suspected area,and they were used in mean-shift clustering segmentation.Experimental results show that by the introduction of HESSIAN matrix of dot filtering information to mean-shift clustering,nodular regions can be segmented from blood vessels,trachea,or cross regions connected to the nodule,non-nodular areas can be removed from ROIs properly,and ground glass object(GGO)nodular areas can also be segmented.For the experimental data set of 127 different forms of nodules,the average accuracy of the proposed algorithm is more than 90%.

Moving Target Tracking Based on LogGabor Wavelet and Mean-shift Algorithm

Tracking moving target from image or video sequence is a hot research topic in computer vision. An algorithm based on LogGabor wavelet and Mean-shift has been proposed for moving target tracking under fixed camera setting and complicated environment. Phase coherency of LogGabor wavelet facilitates to extract the edge of moving target and check noise. According to the edge detection,the starting location of Mean-shift can be estimated using the target center coordinate. Eventually,a real-time moving target can be extracted by doing iterative matching pursuit,and experimental results proved the effectiveness of the method proposed.