动态贝叶斯网络的无人机炮火校射

动态贝叶斯网络(Dynamic Bayesian Networks-DBNs),是对具有随机过程性质的不确定性问题进行建模和处理的一个有力工具.提出将隐马尔可夫模型(Hidden Markov Models-HMMs)图形模式与贝叶斯网络结合起来构成DBN,将其用于无人机照相侦察情报的推理分析,决定炮火优先打击区域.首先建立动态贝叶斯网络的战场态势变化模型,而后应用HMM的推理算法获得当前隐含序列最优估计,且可预测出未来战场态势.最后应用模糊推理获得优先打击的区域号.仿真结果表明了模型的可行性.该方法有效解决了贝叶斯网络对于瞬间变化战场态势推理的不足的缺陷,为炮兵指挥员更好地运用火力,分出主次奠定了基础.     

一种基于加权DSmT的序列图像目标识别方法

通过对自动目标识别系统研究与分析,将DSmT引入到飞机序列图像识别中,构建基本置信指派及分类规则,提出了一种基于加权DSmT的序列图像目标识别方法,在分析权值选择特点的基础上,根据特征信息的置信指派组合有效权值,并且给出了基于加权DSmT融合规则的序列图像目标识别的流程,仿真结果表明,该方法提高了序列图像目标识别的准确性.     

非致命防暴动能弹作战效能评估

提出了非致命防暴动能弹作战效能的评估法,分析了非致命防暴动能弹的技术性能,建立了以非致命性为核心指标的防暴动能弹效能指标体系。采用定性分析与定量分析相结合,将集对分析法应用到了非致命防暴动能弹的效能评估中,对3种防暴动能弹进行了效能评估。为非致命防暴动能弹的研发生产提供了重要的理论支撑。     

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。     

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。     

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.     

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。