基于AHP和云重心方法的装甲兵指挥信息系统效能评估

针对装甲机械化部队指挥信息系统作战效能评估问题,运用云理论进行了探索。首先,对云重心理论的步骤进行了阐述,结合层次分析法,最后用加权偏离度来衡量系统与理想状态的偏离度,将结果用Matlab三维图形对装甲机械化部队指挥信息系统的作战效能进行描述,图形较直观的反应了其效能问题。此方法简单易行,可操作性强,为装甲机械化部队指挥信息系统评估提供了一种较好的途径。     

军用信息系统能力评价方法研究*

信息能力直接影响信息优势能力和指挥决策能力,如何分析和评军用信息系统信息能力对作战的影响是一个重要的研究课题。在分析信息能力概念及其流程的基础上,详细研究了信息能力的三个度量指标:完备性、准确性和时效性的评估方法,给出评估信息能力的解析数学模型,研究结果对信息战和信息优势具有参考价值。     

模糊Petri网在装备保障指挥决策中的应用研究

针对装备保障指挥决策的非结构化特点,结合模糊Petri网的基本理论,建立了一类装备保障指挥决策的模糊Petri网模型(ZYFPN).给出了模型中模糊推理过程的形式化推理算法,算法考虑了推理过程中的众多约束条件,包括命题在规则中的权重、变迁触发的闲值、规则的可信度以及多结论规则等.以装备保障配置地域转移决策为例,描述了从决策问题分析到ZYFPN模型建立,以及基于矩阵运算的决策推理过程的相关问题.应用这一模型,可以提高基于规则的装备保障指挥辅助决策系统的设计、分析和维护效率.     

越级指挥对指挥自动化系统生存能力的影响

采用一种基于概率统计的单指标评估方法,对导弹部队指挥自动化系统在无越级指挥和有越级指挥两种情况下的生存能力进行模拟计算和比较.结果证明有越级指挥的导弹部队指挥自动化系统,在受到外部L重打击时,其生存能力比无越级指挥的导弹部队指挥自动化系统显著增强.     

数据挖掘技术及其在指挥控制系统中的应用

数据挖掘作为一项从大量数据中获取有用知识的实用技术 ,已广泛应用于各行各业并取得了巨大的经济和社会效益 ,而其在军事领域的应用也具有巨大的潜力。概要介绍了数据挖掘技术的功能、过程及其广泛应用的实例 ,阐明了数据挖掘技术在军用指挥控制系统中的应用前景。     

Consumer drone evolutions: trends,spaces, temporalities,threats

ABSTRACT

While the drone has become synonymous with the War on Terror, the asymmetric iconography of the battlefield is shifting. Commercially available off-the-shelf (COTS) drones are increasingly prevalent features of global battlefields, employed by non-state actors in both visualising such spaces, and the directing and inflicting of harm. As such usage increases, so too do concerns around their evolving adoption, adaptation, and potential portability into homeland spheres. While cognizant of the range of positive drone applications, it is asserted that drones nonetheless remain simultaneously bound to an inverse potential for exploitation. In examining drone risk, this article approaches the consumer drone through a series of sites and spaces through which it is technically and socially constructed. Reflecting upon industry innovation, community-driven experimentation, and evolving airspace – it calls for greater attention to the drone’s malleability, arguing that understandings of COTS drones must remain attentive to both drone potential and potential drone threat.     

Samudra: India’s convoluted path to undersea nuclear weapons

ABSTRACT

India is advancing slowly toward operationalizing its nuclear triad. Its first nuclear-propelled ballistic-missile submarine (SSBN), the INS Arihant, conducted its maiden deterrent patrol in November 2018. However, doubts exist around the capability of India’s SSBN, the effectiveness of its command and control, and its effects on regional stability in South Asia. This article examines the history and future trajectory of India’s sea-based nuclear forces and describes how India seeks to maintain robust command and control over its undersea nuclear weapons.     

The U.S. Navy's task forces: 1–199

ABSTRACT

Grouping warships for combat has evolved greatly over the centuries. In the early 1940s, the United States Navy began to group its warships for combat in much more flexible task forces than the previous single-type-of-warship formations. This system has evolved and spread to naval forces ashore but remains fundamentally unchanged. It now covers numbers between 1 to over 1000, of which the most prominent is the first 100 or so which U.S. Navy combat forces use. The numbered fleets worldwide utilize the series covering 20-79; 1-19 and 90-99 appear to be reserved for special allocations and Commander, Pacific Fleet; numbers over 80 to the Atlantic; 100-119 for Northern Europe and briefly Tenth Fleet; the 120 series for Second Fleet as a Joint Task Force leader; the 150 series for Naval Forces Central Command; and the 180 series for Atlantic Fleet and now-Fleet Forces Command.     

基于部件模型的无人机系统小样本车辆目标识别方法

对地目标检测与识别是无人机系统典型任务之一,但受限于任务特殊性,往往难以获取足够的目标样本数据以实现高可靠的目标识别.为此,结合人的认知特性,提出一种基于部件模型的小样本车辆目标识别方法,可有效提高无人机感知能力.采用视觉显著性检测与物体性检测相结合的检测方法,提取目标可能区域;采用基于图论的GrabCut方法与最大类...     

CSCW 协作支持平台 GZ-PCCS 的研究与设计

如何提供有效的协作机制是设计CSCW系统的关键技术。本文首次将CSCW的概念引入公安指挥中心系统的设计之中,全面介绍了一个基于CSCW的公安指挥中心系统的协作支持平台的设计和实现。在平台设计中,提出了一个层次的基于任务的快作支持方案,讨论了各种具体的协作支持方法,并在”集中一分布”混合式的体系结构的基础上实现了一个公安指挥中心系统的协作支持平台的原型GZ—PCCS。