固定节点3D网格部署的水下传感器网络分簇路由算法

水下传感器网络是应用于水下通信的重要传感器网络技术。提出了基于固定节点3D网格部署的水下无线传感器网络分簇算法,设计了3D网格的编址和分簇方法,实现了基于地址分配的节点定位,构建了算法的能耗分析模型。采用MATLAB完成了算法的性能仿真,对比了DS-VBF、IAR和GEDAR 3个算法的平均数据传输延迟和网络生存时间(TTL)。实验结果表明,此算法的平均数据传输延迟较短,可明显提高UWSNS的生存时间。     

相依网络研究综述

网络相依性和相依网络研究已经成为近年来复杂网络领域的热点,但是目前对相关研究进展进行综合归纳的文献却不多见。在对国内外相关文献进行系统分析的基础上,简要介绍相依网络研究涉及的渗流理论;描述该类型网络的级联失效过程;从相依网络的子网络特性、相依边的方向和类型、子网络组合方式等三个角度对相依网络鲁棒性的研究成果进行总结;并从理论和应用两方面对未来相依网络研究的发展方向进行展望。     

Chains,networks and shifting paradigms: the UK defence industry supply system

Industrial supply chains have emerged as the focus of much economic and business research over the previous decade and defence is no exception. This paper reviews contributions made to the body of knowledge in this field with particular attention given to primary research undertaken on the UK defence industrial supply system. In doing so, the author assesses the quantity and quality of knowledge available to researchers, analysts and policy makers. There have been a number of valuable contributions to the literature and this paper reveals a subject that is extremely complicated, dynamic and yet under‐researched. It is suggested that it may well be this complexity and dynamism, coupled with difficulty in accessing data, that has inhibited research development in this field. Defence industry supply systems are repeatedly referred to as ‘chains’; however, primary research consistently suggests this is a misnomer – the author concludes that future analysis of the UK defence sector must embrace directly the economics of networks if understanding of the same is to progress and industrial ‘invisibility’ is to be avoided.     

External security determinants of Greek military expenditure: An empirical investigation using Neural networks

Greece has regularly ranked as the country with the highest defence burden in NATO and the European Union. Over the past decades she has allocated an averatge 6% of GDP to defence yearly. This study using neural networks examines the external security determinants of Greek military expenditure in the context of the ongoing Greek‐Turkish conflict.     

密集回波环境下的一种交互式多模型算法

利用神经网络算法对基于机动目标“当前”统计模型的均值和方差自适应滤波算法进行修改 ,提高该算法的性能 ;然后与匀速模型交互作用 ,利用概率数据关联处理密集回波环境下机动目标的跟踪问题 ,这样既保持了对强机动目标的跟踪性能 ,同时又提高了对弱机动目标的跟踪精度。仿真结果证明该算法确为一种非常有效的方法     

Cyberpower in Strategic Affairs: Neither Unthinkable nor Blessed

Abstract

This article explores the effect of connectivity on strategic affairs. It argues that the effect on war's character is potentially, although not yet shown in practice, considerably large. Its effect upon the distribution of power among states in the international system is small, contrary to the claims of ‘cyberwar’ alarmists. All told, however, its effect upon strategic affairs is complex. On the one hand, it represents a significant advance in the ‘complexification’ of state strategies, understood in the sense of the production of intended effects. On the other hand, strategists today – still predominantly concerned with the conflicts and confrontations of states and organised military power – are generally missing the power which non-traditional strategic actors, better adapted to the network flows of the information age, are beginning to deploy. These new forms of organization and coercion will challenge the status quo.     

Ballistic response of armour plates using Generative Adversarial Networks

It is important to understand how ballistic materials respond to impact from projectiles such that informed decisions can be made in the design process of protective armour systems. Ballistic testing is a standards-based process where materials are tested to determine whether they meet protection, safety and performance criteria. For the V₅₀ ballistic test, projectiles are fired at different velocities to determine a key design parameter known as the ballistic limit velocity (BLV), the velocity above which projectiles perforate the target. These tests, however, are destructive by nature and as such there can be considerable associated costs, especially when studying complex armour materials and systems. This study proposes a unique solution to the problem using a recent class of machine learning system known as the Generative Adversarial Network (GAN). The GAN can be used to generate new ballistic samples as opposed to performing additional destructive experiments. A GAN network architecture is tested and trained on three different ballistic data sets, and their performance is compared. The trained networks were able to successfully produce ballistic curves with an overall RMSE of between 10 and 20 % and predicted the V₅₀ BLV in each case with an error of less than 5 %. The results demonstrate that it is possible to train generative networks on a limited number of ballistic samples and use the trained network to generate many new samples representative of the data that it was trained on. The paper spotlights the benefits that generative networks can bring to ballistic applications and provides an alternative to expensive testing during the early stages of the design process.     

基于ANP的多目标网络目标重要度评估方法

摘　要：目标重要度分析是军事计划人员开发作战计划方案的前提和基础,是形成作战行动的基本依据。针对多目标网络中重心的抽象性及不易打击的特点,分析影响重心的关键要素及其依赖关系,提出了基于改进网络分析法的多目标网络重心模型,利用该模型分析目标相对于重心的重要度。研究了该模型涉及的两个关键技术,即改进判断矩阵以提高决策的速度和克服决策的主观性,利用协调理论确定要素之间的依赖关系。以分析某地区多目标网络的过程为例,验证了该模型的有效性和合理性。     

基于RSSI线性回归分析的无线传感器网络定位方法

在无线传感器网络（WSN）定位应用中,针对接收信号强度指示（RSSI）容易受到环境的影响不能实现准确测距的问题,提出了一种基于RSSI线性回归分析的定位方法。该方法通过信号衰减模型和线性回归理论相结合,修正实际环境下每个锚节点的测距模型,同时利用相关系数和剩余标准差对测距模型进行评估,制定出更加合理的定位策略。实验表明,采用修正的测距模型和新的定位策略,使得节点定位精度明显提高。     

基于统一理论框架的故障诊断模型及学习进化方法

本文以概率图理论为基础，系统研究了基于这一理论框架的故障诊断模型，对模型的构建方法以及在不同场景下的模型演化方案进行了探讨，使得在统一理论框架下可实现多模式系统故障诊断、耦合故障诊断、动态故障诊断、故障预测等复杂情形的诊断。为了弥补单独利用基于模型的方法和基于数据的方法的缺陷，研究了诊断模型的学习进化策略，实现了诊断效果的改进和优化。另外，对模型后续的能力扩展和可能的研究方向进行了展望，为后续理论研究提供了参考。