基于分水岭变换和蚁群聚类的图像分割

针对传统分水岭分割算法对噪声敏感和易于产生过分割问题,提出一种新的基于分水岭和蚁群智能聚类的图像分割方法(CWAC,Combining watersheds and ant colony clustering).CWAC方法首先用分水岭变换对图像做初分割,然后用蚁群方法在区域之间进行聚类合并,获得最终的分割结果.CWAC不但成功地解决了分水岭存在的过分割问题,还大大提高了蚁群聚类算法的搜索效率;本文利用分水岭变换后的灰度信息和空间信息,定义了一种新的引导函数,可更准确有效引导蚁群聚类.实验结果表明CWAC可以快速准确地分割出目标,是一种有效的图像分割方法.     

基于用户反馈的兴趣模型在信息检索中的应用

兴趣模型是个性化信息服务的关键技术,介绍了基于用户反馈的兴趣模型的构造过程,并将其应用于信息检索中,通过利用反馈对检索结果集重新排序、利用反馈形成新的查询请求两种方式优化检索结果,使其能更好地符合用户的个性化需求,并说明了利用反馈对兴趣模型进行调整的原则.     

无线传感器网络能量异构分簇算法的研究

在无线传感器网络能量异构环境下对低功耗自适应的分簇算法(Low Energy Adaptive Clustering Hierarchy,LEACH)与稳定选举协议(Stable Election Protocol,SEP)算法进行了分析,针对其存在的不足提出了一种改进的方案。在簇头选举过程中提高了剩余能量高、距离基站较近节点当选为簇头的概率,同时对当选为簇头的节点设定能量阈值,避免能量过低的节点当选为簇头。仿真结果表明,改进后的算法较好地均衡了网络中节点的能量消耗,有效地提高了网络中能量的利用效率,并且极大地延长了网络正常工作的生命周期。     

基于聚类的网络异常检测

探索一种基于聚类来识别异常的方法,这个方法不需要手动标示的训练数据集却可以探测到很多不同类型的入侵行为.实验结果表明该方法是可行的和有效的,使用它来进行异常检测可以得到探测率和误报率的一个平衡,从而为异常检测问题提供一个较好的解决办法.     

一种基于概率统计的自适应网格聚类算法

针对固定网格划分技术存在的维度可扩展性差,而自适应网格划分技术未充分考虑数据集分布特征等问题,提出了一种基于概率统计理论的自适应网格聚类算法.采用概率统计和图覆盖技术,且能识别任意形状和大小的聚类,时间复杂度是数据集大小和数据维度的线性函数.实验结果表明该聚类是有效的.     

基于聚类分析的车牌定位算法的研究

提出一种基于数学形态学和均值聚类的实时车牌定位算法,将边缘处理与聚类分析相结合,得到车牌字符的垂直边缘信息,然后通过数学形态学运算处理,结合车牌的多个特征对车牌进行定位,试验证明,该算法准确率高,抗干扰性强.     

融合层次聚类的高分辨率遥感影像超像素分割方法

为解决遥感影像分割尺度自动选取难的问题,提出了融合层次聚类的高分辨率遥感影像超像素分割方法。首先采用自适应形态重建的分水岭分割算法将影像分割成多个超像素;然后提取各超像素的灰度特征向量;最后利用层次聚类方法进行超像素合并,实现高分辨率遥感影像的精确分割。实验选用4组景遥感影像;采用定性和定量相结合的方法评价实验结果。实验结果表明,该方法有效提高了遥感影像分割精度,并取得了较好的分割视觉效果。     

Stochastic geometry of network of randomly distributed moving vehicles on a highway

Vehicular ad hoc networks (VANETs) have become an extensively studied topic in contemporary research. One of the fundamental problems that has arisen in such research is understanding the network statistical properties, such as the cluster number distribution and the cluster size distribution. In this paper, we analyze these characteristics in the case in which vehicles are located on a straight road. Assuming the Rayleigh fading model and a probabilistic model of intervehicle distance, we derive probabilistic distributions of the aforementioned connectivity characteristics, as well as distributions of the biggest cluster and the number of disconnected vehicles. All of the results are confirmed by simulations carried out for the realistic values of parameters.     

Prolong network lifetime and improve efficiency in WSN‐UAV systems using new clustering parameters and CSMA modification

Since unmanned aerial vehicles (UAVs) have been introduced as mobile nodes for data gathering, wireless sensor networks (WSNs) have progressed considerably. The resulting WSN‐UAV systems are employed for emergency applications and also for remote monitoring purposes. WSN‐UAV systems yield an optimum data gathering method using the WSN. In the proposed method, the nodes' data are transferred using a remotely operated vehicle (drone) rather than the conventional data transferring methods like the direct and hop‐to‐hop data transmission approaches. Then, the gathered data are delivered in the pre‐determined destination point. WSN‐UAV systems, in fact, are a special case of the systems with the mobile sink in which the sink path is previously specified and controlled. In this paper, the effects of clustering parameters on the WSNs are studied; then, the network's lifetime is prolonged by applying some parameters. In addition, the network's performance is enhanced to some extent by assigning some changes in the media access control (MAC) layer. Also, the effect of drone's path pattern on the lifetime of the network is studied.     

GWO‐C: Grey wolf optimizer‐based clustering scheme for WSNs

A wireless sensor network (WSN) is a prominent technology that could assist in the fourth industrial revolution. Sensor nodes present in the WSNs are functioned by a battery. It is impossible to recharge or replace the battery, hence energy is the most important resource of WSNs. Many techniques have been devised and used over the years to conserve this scarce resource of WSNs. Clustering has turned out to be one of the most efficient methods for this purpose. This paper intends to propose an efficient technique for election of cluster heads in WSNs to increase the network lifespan. For the achievement of this task, grey wolf optimizer (GWO) has been employed. In this paper, the general GWO has been modified to cater to the specific purpose of cluster head selection in WSNs. The objective function for the proposed formulation considers average intra‐cluster distance, sink distance, residual energy, and CH balancing factor. The simulations are carried out in diverse conditions. On comparison of the proposed protocol, ie, GWO‐C protocol with some well‐known clustering protocols, the obtained results prove that the proposed protocol outperforms with respect to the consumption of the energy, throughput, and the lifespan of the network. The proposed protocol forms energy‐efficient and scalable clusters.