低地球轨道卫星网络节能方法

通常卫星的唯一能源来源是太阳能,因此星上网络设备的能源供应问题比地面网络的更加严峻。通过修改和扩展链路容量受限的最小代价多商品流模型来适应卫星网络这一特殊的体系结构,并基于低轨道卫星网络的多重覆盖机制和流量分布模型,改进现有的启发式算法来关闭冗余的卫星节点、星地链路和星间链路。在满足链路利用率和路由跳数增加比例约束的条件下,仿真实验中关闭上述三种参数的比例分别可达59%、61%和72%,卫星网络的总体节能比例可达65%。     

二部图的(g,f)匹配

本文证明了二部图存在(g,f)匹配和f 因子的充要条件以及有关的几个结果,并且给出了求二部图的最大(g,f)匹配、最小(g,f)匹配和最小权最大f 匹配、最小权(g,f)匹配、最大权(g,f)匹配的算法。     

基于卷积神经网络的卫星网络协调态势评估方法

为充分发掘利用海量卫星网络数据,提高决策效率,加强空间频轨资源获取与储备的分析手段,尤其是对地球静止轨道资源的协调获取问题,提出基于机器学习算法的卫星网络态势评估策略。通过对卫星网络协调因素进行特征分析,选择卷积神经网络(Convolution Neural Network, CNN)为目标算法模型,并建立算法模型的训练数据集及Label规则,采用分裂信息增益度量方法对数据进行降维处理,建立CNN评估模型,并进行了验证分析。结果表明,CNN模型对卫星网络协调态势评估问题测试的正确率高达80%以上,具有较高的评估效能。随着数据量的增多,CNN评估效果逐步提升,是一种在卫星网络协调态势分析、资源储备的有效评估方法。     

深度神经网络指导下的无线覆盖预测算法

为保证新一代移动无线网络能够根据实时覆盖情况动态地调节小区天线参数,需要实现高效且准确的无线覆盖预测。传统的求解方法通过精确的场强预测判断天线参数的优劣,虽然精度很高但需要大量的计算资源,无法满足5G和后5G移动网络通过实时覆盖预测进行射频参数动态调整的实际需求。现采用基于深度神经网络的算法对给定天线参数的覆盖效果进行预测,以取代对目标区域的精确场强预测。数值结果表明:该方法能够在保持计算准确性的同时显著减少计算量,为5G动态网络规划提供基础性参考数据。     

5G安全及网络监管问题探析

5G作为当今先进的通信技术,其广泛应用将给整个社会生产生活带来全新变革,相关技术和应用的安全问题,事关社会公共安全和军事利益安全,应纳入总体国家安全观视角下重点考量。本文主要梳理5G关键技术可能带来的网络安全风险,以及相关应用可能给网络监管带来的挑战,并从牢牢把控核心知识产权、综合构建安全保障体系、紧跟推进行业法律规范以及着力完善高效应急措施这四方面探讨相关的应对措施。     

Gabor-CNN for object detection based on small samples

Object detection models based on convolutional neural networks (CNN) have achieved state-of-the-art performance by heavily rely on large-scale training samples. They are insufficient when used in specific applications, such as the detection of military objects, as in these instances, a large number of samples is hard to obtain. In order to solve this problem, this paper proposes the use of Gabor-CNN for object detection based on a small number of samples. First of all, a feature extraction convolution kernel library composed of multi-shape Gabor and color Gabor is constructed, and the optimal Gabor convolution kernel group is obtained by means of training and screening, which is convolved with the input image to obtain feature information of objects with strong auxiliary function. Then, the k-means clustering algorithm is adopted to construct several different sizes of anchor boxes, which improves the quality of the regional proposals. We call this regional proposal process the Gabor-assisted Region Proposal Network (Gabor-assisted RPN). Finally, the Deeply-Utilized Feature Pyramid Network (DU-FPN) method is proposed to strengthen the feature expression of objects in the image. A bottom-up and a top-down feature pyramid is constructed in ResNet-50 and feature information of objects is deeply utilized through the transverse connection and integration of features at various scales. Experimental results show that the method proposed in this paper achieves better results than the state-of-art contrast models on data sets with small samples in terms of accuracy and recall rate, and thus has a strong application prospect.     

A novel facial emotion recognition scheme based on graph mining

Recent years have seen an explosion in graph data from a variety of scientific, social and technological fields. From these fields, emotion recognition is an interesting research area because it finds many applications in real life such as in effective social robotics to increase the interactivity of the robot with human, driver safety during driving, pain monitoring during surgery etc. A novel facial emotion recognition based on graph mining has been proposed in this paper to make a paradigm shift in the way of representing the face region, where the face region is represented as a graph of nodes and edges and the gSpan frequent sub-graphs mining algorithm is used to find the frequent sub-structures in the graph database of each emotion. To reduce the number of generated sub-graphs, overlap ratio metric is utilized for this purpose. After encoding the final selected sub-graphs, binary classification is then applied to classify the emotion of the queried input facial image using six levels of classification. Binary cat swarm intelligence is applied within each level of classification to select proper sub-graphs that give the highest accuracy in that level. Different experiments have been conducted using Surrey Audio-Visual Expressed Emotion (SAVEE) database and the final system accuracy was 90.00%. The results show significant accuracy improvements (about 2%) by the proposed system in comparison to current published works in SAVEE database.     

线性系统测试矩阵优化

研究了工程中常见的线性系统循环指数为1(即其约当标准形不同的约当块不出现重根)的情况下测试矩阵优化的方法.通过将系统矩阵变换为约当标准型,能够简单地判定测试矩阵能否保持系统可观测.采用该方法可以在保持系统可观测的情况下,简单而直观地获取系统测试代价最小的测试策略.例子表明,该算法在工程上易于应用.     

基于RBF神经网络的HPA自适应预失真算法

卫星通信中,高速数据传输系统要求使用频谱利用率高的高阶调制技术,但高阶调制对高功率放大器(HPA)的非线性非常敏感,会造成码间干扰和邻信道间干扰.提出一种基于RBF神经网络的自适应预失真算法,以实现HPA的线性化,同时推导了自适应算法的迭代公式.仿真结果表明,该算法能明显改善信号星座图,并能大大提高系统的误比特率性能.     

Sojourn times in G/M/1 fork‐join networks

We consider a processing network in which jobs arrive at a fork‐node according to a renewal process. Each job requires the completion of m tasks, which are instantaneously assigned by the fork‐node to m task‐processing nodes that operate like G/M/1 queueing stations. The job is completed when all of its m tasks are finished. The sojourn time (or response time) of a job in this G/M/1 fork‐join network is the total time it takes to complete the m tasks. Our main result is a closed‐form approximation of the sojourn‐time distribution of a job that arrives in equilibrium. This is obtained by the use of bounds, properties of D/M/1 and M/M/1 fork‐join networks, and exploratory simulations. Statistical tests show that our approximation distributions are good fits for the sojourn‐time distributions obtained from simulations. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008