Knowledge-oriented modeling for influencing factors of battle damage in military industrial logistics: An integrated method

Modeling influencing factors of battle damage is one of essential works in implementing military industrial logistics simulation to explore battle damage laws knowledge. However, one of key challenges in designing the simulation system could be how to reasonably determine simulation model input and build a bridge to link battle damage model and battle damage laws knowledge. In this paper, we propose a novel knowledge-oriented modeling method for influencing factors of battle damage in military industrial logistics, integrating conceptual analysis, conceptual modeling, quantitative modeling and simulation implementation. We conceptualize influencing factors of battle damage by using the principle of hierarchical decomposition, thus classifying the related battle damage knowledge logically. Then, we construct the conceptual model of influencing factors of battle damage by using Entity-Relationship approach, thus describing their interactions reasonably. Subsequently, we extract the important influencing factors by using social network analysis, thus evaluating their importance quantitatively and further clarifying the elements of simulation. Finally, we develop an agent-based military industry logistics simulation system by taking the modeling results on influencing factors of battle damage as simulation model input, and obtain simulation model output, i.e., new battle damage laws knowledge, thus verifying feasibility and effectiveness of the proposed method. The results show that this method can be used to support human decision-making and action.     

Arbitrary-oriented target detection in large scene sar images

Target detection in the field of synthetic aperture radar (SAR) has attracted considerable attention of researchers in national defense technology worldwide, owing to its unique advantages like high resolution and large scene image acquisition capabilities of SAR. However, due to strong speckle noise and low signal-to-noise ratio, it is difficult to extract representative features of target from SAR images, which greatly inhibits the effectiveness of traditional methods. In order to address the above problems, a framework called contextual rotation region-based convolutional neural network (RCNN) with multilayer fusion is proposed in this paper. Specifically, aimed to enable RCNN to perform target detection in large scene SAR images efficiently, maximum sliding strategy is applied to crop the large scene image into a series of sub-images before RCNN. Instead of using the highest-layer output for proposal generation and target detection, fusion feature maps with high resolution and rich semantic information are constructed by multilayer fusion strategy. Then, we put forwards rotation anchors to predict the minimum circumscribed rectangle of targets to reduce redundant detection region. Furthermore, shadow areas serve as contextual features to provide extraneous information for the detector identify and locate targets accurately. Experimental results on the simulated large scene SAR image dataset show that the proposed method achieves a satisfactory performance in large scene SAR target detection.     

二维矩阵卷积在向量处理器中的设计与实现

为了加快卷积神经网络模型的计算速度,便于大规模神经网络模型在嵌入式微处理器中的实现,以FT-matrix2000向量处理器体系结构为研究背景,通过对多核向量处理器体系结构的分析和对卷积神经网络算法的深入研究,提出将规模较小的卷积核数据置于标量存储体,尺寸较大的卷积矩阵置于向量存储体的数据布局方案。针对矩阵卷积中数据难以复用的问题,提出根据卷积核移动步长的不同动态可配置的混洗模式,通过对所取卷积矩阵元素进行不同的移位操作,进而大幅提高卷积矩阵数据的复用率。针对二维矩阵卷积由于存在数据相关性进而难以多核并行的问题,提出将卷积矩阵多核共享,卷积核矩阵多核独享的多核并行方案。设计了卷积核尺寸不变、卷积矩阵规模变化和卷积矩阵尺寸不变、卷积核规模变化的两种计算方式,并在主流CPU、GPU、TI6678、FT-matrix2000平台进行了性能对比与分析。实验结果表明:FT-matrix2000相比CPU最高可加速238倍,相比TI6678可加速21倍,相比GPU可加速663 805倍。     

集中控制混合网络中基于流的资源分配算法

集中控制混合网络中,异构化网络内部的流量具有一定的规律和特性,如果使用原有的单一的离散式最大极值和无状态的网络资源调度算法,忽略了异构网络规律,会造成网络利用率较低、易震荡、部分网络流延迟等问题。通过分析由集中控制网络和普通网络组成的混合网络的拓扑结构,对混合网络结构中常见问题如流闪现、不能估计的流、路径堵塞或连接震荡场景进行分析,并提出基于期望和状态的流量评价资源规划算法POS和POS-FME。算法考虑混合网络的运行状态,对系统可用资源进行评估,为系统中各种流匹配对应可用资源,并具有一定的预测作用,从而避免混合网络出现运行效率低下的场景。通过实验,POS算法和POS-FME算法相对传统算法,利用率提高了10%～30%,并降低了震荡和平均延迟。     

提升我军话语国际影响力需要过好四关

国防话语的一个重要支柱就是军事话语,军事话语是一个国家军事软实力和军队形象建设的重要体现。新时代呼唤强有力的军事话语。习近平总书记关于对外宣传工作以及国防和军队现代化的重要论述为提升我军话语国际影响力、为建构我军国际形象指明了方向、制定了战略、设计了战术、明确了任务、确定了节点。如何切实提升我军话语的国际影响力,成为新形势下实现中国梦和强军目标的时代课题。从具体操作层面,提升我军话语国际影响力需要过好思维立场、语言文化、业务素养和法规制度四个关。     

混沌时间序列中弱信号的检测

介绍了混沌的基本特性,利用混沌时间序列对RBF神经网络进行训练,则训练好的神经网络对混沌序列未来时刻的值具有一定的预测能力.若混沌序列中包含有目标信号,则其混沌特性将受到影响,导致神经网络对其预测误差的增大.这样可根据预测误差对淹没在混沌杂波及混沌杂波加一定强度的白噪声中的弱目标信号进行检测.仿真结果表明这种方法优于传统的目标检测方法,有着较好的杂波抑制能力.     

交互式多模型目标跟踪的研究现状及发展趋势

系统地介绍了交互式多模型算法在机动目标跟踪领域中的发展现状、发展方向及其与数据关联相结合进行目标跟踪的发展趋势.通过分析交互式多模型算法本身存在的问题,指出了一些改进方法的可行性与不足;进一步介绍了数据关联与交互式多模型算法结合进行目标跟踪的发展情况及取得的成果,并对以后与交互式模型相关的研究方向进行了探讨.     

基于代理群的军事自组网拓扑搜索算法

分析现有网络管理模式的基础上,针对军事移动自组网的特点,提出了一种基于动态代理群的网络管理模式,并实现了动态代理群网络管理模式的网络拓扑结构搜索算法,最后对该拓扑搜索算法的有效性、正确性及复杂度进行分析论证.实验证明该算法可以很好地解决节点频繁移动带来的需要实时反映网络拓扑结构的问题.     

军用飞机采购价格的RBF神经网络预测

分析了现代军用飞机采购价格估算中存在的问题.应用基于k-均值聚类算法的RBF神经网络建立了军用飞机采购价格预测模型,并采用该模型对某型军用飞机采购价格进行了预测.与多元线性回归和BP神经网络的预测结果对比,建立的新型军用飞机采购价格预测模型具有更高的预测精度,为军用飞机采购价格预测提供了一种新的有效方法.     

网络攻击螺旋模型构建及运用研究

网络攻击模型是实施网络攻击的基本遵循依据,本文分析了经典网络攻击链模型存在的适用性不足、全面性欠缺、整体性不够三个主要问题,构建了更适合描述AP T攻击的网络攻击螺旋模型.该模型将网络攻击活动描述为侦察、武器化、渗透与破坏、横向移动、撤出以及评估与改进6个阶段,并设置为螺旋循环式结构,通过对攻击链模型的优化与重构,使模...