防御矩阵满足乘法交换律的证明

在使用马尔柯夫链分析多层防御系统的防御效用值时,发现防御矩阵是否满足乘法交换律将关系到多层防御系统变换部署后的防御效用值,因此有必要对防御矩阵是否满足乘法交换律进行证明。首先介绍了防御矩阵的概念、物理意义、重要性质及计算方法,分析了防御矩阵满足乘法交换律的重要意义,最后综合运用数学归纳法和随机矩阵性质证明了防御矩阵满足乘法交换律的事实,此结论无论对于多层防御系统的防御效用值研究还是矩阵理论研究都有一定的指导作用。     

美国海军水面舰艇导弹防空体系分析

分析了美国海军防空作战思想,在此前提下总结出美海军舰空导弹体系主要的作战任务,说明了美海军典型水面舰艇装备舰空导弹的情况,最后阐述了今后美舰空导弹的发展趋势。     

Petri网在防空C~3I系统建模与分析中的应用

针对野战防空系统中的火力分配问题,首先为旅营两级防空系统建立了Ｐｅｔｒｉ网模型,并运用时间Ｐｅｔｒｉ网理论对其进行了时延分析,论述了为野战防空火力分配建立辅助决策系统的重要性。最后利用谓词Ｐｅｔｒｉ网描述了火力分配的推理过程,为有效分析战术决策的正确性提供了依据和思路     

空间拦截末制导段能量需求分析

为了研究空间拦截末制导段的能量需求,对导航与测轨误差模式和空间拦截交会条件进行了分析,并建立末制导段仿真模型。使用Monte-Carlo法仿真,获得不同交会角下和不同拦截概率下拦截器所需的能量,并讨论了交会角和相对速度对拦截器能量需求的影响。     

防空作战风险决策分析

防空作战决策活动具有极强的风险性。以决策者面对风险的态度为突破点,通过建立效用曲线并在最大效用原则下对有关决策问题进行优化,在一定程度上可大大降低决策方案的风险性。同时对于防空体系设计也具有一定的参考价值。     

国防企业的效用函数及激励机制设计

在定性分析国防企业效用的基础上, 考虑装备生产过程的部分可观察性, 利用委托-代理理论, 建立了国防企业综合激励模型, 并对给定情况进行了激励合同设计。     

新型驱护舰编队防空队形配置

根据新型驱护舰编队和现代防空作战的特点,从电子对抗、火力协同、指挥通信和区域掩护四个方面对编队防空队形配置进行研究,提出了编队防空队形配置的基本要求,建立了编队防空队形配置论证的数学模型并进行了仿真,通过对仿真结果的认真分析得出了实用性的结论。     

The symbolic politics of ballistic missile defense: seeking the perfect defense in an imperfect world

Ballistic missile defense (BMD) politics present an interesting evolution in how the USA, especially Congress, has come to think about BMD both as operational reality and as a symbolic policy. The argument here is that BMD's operational reality is increasingly overshadowed by its symbolic aspects. Such a status arose from rapidly changing international and domestic politics. The end result is a situation in which BMD policy in a sense floats above the question of its actual combat effectiveness. Its primary mission in part is sustaining US capacity to remain a global power and support its allies.     

基于Entropy-Topsis的水面舰艇编队防空配置方案优选研究

合理配置编队防空舰艇是提高编队对空防御作战效能的一项重要手段。以两型四舰六扇面的典型配置方案优选为研究对象,建立了配置方案评价指标体系及相应数学模型,构建了编队防空舰艇配置方案优选问题的决策矩阵,应用Entropy?Topsis模型进行优选,对各方案进行排名,并得到了相应最优解和最劣解。     

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.