卫星通信抗干扰综合运用模式

从历史战例归纳通信干扰作战样式,从技术角度分析卫星通信潜在干扰威胁,立足当前卫星通信抗干扰能力,针对通信干扰作战样式设计卫星通信抗干扰综合运用模式,并提出后续研究工作。     

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。     

美国陆军作战试验部队管理体系研究

作战试验部队是试验鉴定力量体系的重要支撑,是完成装备作战试验的直接载体。美国在装备试验与鉴定方面走在世界前列,建立了一整套行之有效的管理体制,陆军实行试验与鉴定的一体化管理,具有独立的作战试验鉴定部门,没有编设专业化的作战试验部队,试验任务主要根据装备的特性选取未来典型用户的作战部队在真实对抗环境中完成。作战试验部队主要参加作战评估、初始作战试验与评价以及后续作战试验与评价三个阶段的作战试验,在一体化试验小组的协调下,依托靶场完成试验训练任务。本文主要通过对美国陆军试验部队的研究,分析了美国陆军作战试验与鉴定发展概况,重点研究了作战试验部队管理体系,并从整个试验与鉴定系统归纳总结作战试验部队管理及相关工作的主要做法。     

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.     

实践教学体系的设计与构建

依据系统综合的“大实践”理念,探讨理论教学和实践教学相结合的必要性,推进实践培训体系设计与构建的运行机制。