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。     

2K-H 行星传动的极限传动比

在行星轮系机构综合中,研究行星轮数目与极限传动比的关系有着极其重要的意义。本文根据２Ｋ─Ｈ行星轮系机构的同心条件、安装条件和邻接条件,采用统一的符号,对满足上述条件的所有（六种）２Ｋ─Ｈ行星轮系机构,导出了行星轮个数与极限传动比的统一关系式,得到了所有２Ｋ─Ｈ行星轮系机构的极限传动比计算公式,为２Ｋ─Ｈ行星轮系机构的综合提供了理论根据。     

外军两栖车辆水上推进装置的分类及评价

给出了当今外军典型两栖车辆的主要性能指标,对水上推进装置进行了分类,定性分析了各种水上推进装置的工作原理及主要影响因素,给出了FT-C牵引车模型的试验结果.通过对水上推进装置产生的推力和两栖车辆水上行驶的阻力分析,得出了有价值的结论.最后根据实际需求对履带划水式、喷水式、螺旋桨式3种水上推进装置进行了综合评估,其结论对新型两栖车辆的论证、研究具有借鉴作用.     

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。     

亚太新局势下美军两栖攻击舰队发展趋势研究

受美国亚太战略的影响,两栖攻击舰队逐步成为美军海外作战力量重要组成部分,其发展建设水平将对未来美军海外战略重心产生深远影响。本文以亚太新局势为背景,以美军两栖攻击舰队作为研究对象,基于美国防部对亚太地区未来军事部署和谋划的构想,对两栖攻击舰队部署情况、主要任务和发展趋势进行分析研究,可以预见,在未来十到十五年内,两栖攻击舰队将成为美国强势介入亚太的主要力量。     

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。     

Study on performance degradation and failure analysis of machine gun barrel

An increase in the use of the gun barrel will cause wear of the inner wall, which reduces the muzzle velocity and the spin rate of the projectile. The off-bore flight attitude and trajectory of the projectile also change, affecting the shooting power and the accuracy. Exterior ballistic data of a high-speed spinning projectile are required to study the performance change. Therefore, based on the barrel's accelerated life test, the whole process of projectile shooting is reproduced using numerical simulation technology, and key information on the ballistic performance change at each shooting stage are acquired. Studies have shown that in the later stages of barrel shooting, the accuracy of shooting has not decreased significantly. However, it is found that the angle of attack of the projectile increases as the wear of the barrel increases. The maximum angle of attack reaches 0.106 rad when the number of shots reaches 4300. Meanwhile, elliptical bullet hole has appeared on the target at this shooting stage. Through combining external ballistic theory with simulation results, the primary reason of this phenomenon is found to be a significant decrease in the muzzle spin rate of the projectile. At the end of the barrel life, the projectile muzzle spin rate is 57.5% lower than that of a barrel without wear.     

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.