舰船电缆热老化寿命的研究

介绍了某船用电缆热老化寿命研究的有关试验和结果分析．对电统绝缘材料失效的判断、机械性能变化的预测,以及如何根据电缆的负载情况和周围的环境温度确定绝缘材料的老化温度等,提出了相应的观点和方法．     

基于ANN的高炮火力单元射击有利度评估

采用ANN(人工神经网络)方法对高炮火力单元射击有利度进行评估是一种新的尝试。在对各影响因素进行分析与量化处理的基础上,建立了基于ANN的高炮火力单元射击有利度评估网络模型,并提供了网络的解算方法。通过某型高炮对不同目标射击的已知有利度评估值对网络进行的训练,确定了网络的各权值,并使学习误差达到了预先规定的标准,实现了对该型高炮对空射击有利度的评估。     

Decision support model for effects estimation and proportionality assessment for targeting in cyber operations

Cyber operations are relatively a new phenomenon of the last two decades.During that period,they have increased in number,complexity,and agility,while their design and development have been processes well kept under secrecy.As a consequence,limited data(sets)regarding these incidents are available.Although various academic and practitioner public communities addressed some of the key points and dilemmas that surround cyber operations(such as attack,target identification and selection,and collateral damage),still methodologies and models are needed in order to plan,execute,and assess them in a responsibly and legally compliant way.Based on these facts,it is the aim of this article to propose a model that i))estimates and classifies the effects of cyber operations,and ii)assesses proportionality in order to support targeting decisions in cyber operations.In order to do that,a multi-layered fuzzy model was designed and implemented by analysing real and virtual realistic cyber operations combined with interviews and focus groups with technical-military experts.The proposed model was evaluated on two cyber operations use cases in a focus group with four technical-military experts.Both the design and the results of the evaluation are revealed in this article.     

现有军用光电装备快速智能化手段

近年来,人工智能技术在图像处理领域取得了突破性进展,使得目标识别和目标检测算法的性能得到大幅提高,也为军用光电装备智能化创造了有利条件.本文从算法、数据和计算能力等人工智能三要素入手,分析了快速实现军用光电装备智能化的有效途径及过程中可能面临的问题,指出:现有的技术理论基本满足装备升级需求,智能化过程中还应重点加强实时...     

论智能控制（IC）在水面兵器中的应用

对智能控制的应用情况、控制特点、基本型式及其在应用中有待解决的若干问题进行了论述。     

无人机避障航路规划方法研究综述

随着无人机作业空域从中高空不断向低空甚至超低空拓展,复杂的低空障碍环境对无人机造成了严重的威胁。研究无人机避障航路规划理论与方法,对于保障无人机的飞行安全和提升其任务效率具有重要作用。对无人机避障航路规划方法的研究现状进行了梳理,首先,根据航路规划问题所建立的优化模型,将规划方法划分为基于数学规划的方法、基于路标图的方法、基于空间分解的方法、基于势场的方法、基于随机规划的方法和基于机器学习的方法六个大类。然后,分别介绍了各类型方法的基本原理、代表性研究以及优缺点。最后,对避障航路规划方法未来可能的研究方向进行了展望。综述表明,复杂环境下无人机三维航路规划方法的研究仍有提升空间;未来应考虑将传统规划方法与新一代人工智能技术相结合;航路规划方法研究应充分考虑机载传感器的实际性能和工作特性;规划航路的可跟踪性问题也亟待解决。     

利用RLC电路合成高压方波脉冲

基于过阻尼RLC电路分时放电,提出了一种新型高压方波脉冲产生方法。理论分析表明过阻尼RLC电路产生的双指数电压波与PFL产生的方形电压波具有类似的上升沿和平顶。电路模拟表明通过人工过零技术可以对双指数电压波进行截尾,从而形成完整的高压方波脉冲。建立了原理验证性样机,由两组RLC电路构成,每组电路包含一台400 nF脉冲电容器和一只三电极场畸变气体开关,两组电路共用一个上升沿调节电感。实验证明样机可以在250 Ω电阻负载上输出幅值17 kV,平顶宽度330 ns~5.8 μs,上升沿100~ 350 ns 的单极性高压方波脉冲。该方法适应性强,对负载变化不敏感,同时具有良好的可调节性,方波上升沿、平顶宽度连续独立可调。     

定应变作用下NEPE推进剂老化特性及寿命预估研究

为考察定应变作用下NEPE推进剂的老化特性,研究了20%定应变作用下NEPE推进剂贮存老化过程中力学性能、凝胶性能和界面性能的变化.研究结果表明:定应变作用下NEPE推进剂在贮存老化过程中最大抗拉强度降低,最大延伸率变化较小,其老化失效主要表现为强度的失效;定应变下NEPE推进剂的凝胶百分数和粘附功随老化时间的延长而降低,NEPE推进剂粘合剂基体的降解断裂和界面的"脱湿"是其主要的老化机理;定应变下NEPE推进剂的力学性能与细观性能的相关性研究表明,最大抗拉强度与凝胶百分数和粘附功存在相关关系,计算了其关系式,建立了由细观性能评估推进剂宏观力学性能的方法;选择最大抗拉强度下降30%时失效,20%定应变下NEPE推进剂的贮存寿命为8.3年.     

长贮过程中发动机HTPB推进剂老化机理分析

为揭示固体推进剂在长期贮存过程中的老化机理,解剖了自然贮存19年贴壁浇注的端羟基聚丁二烯橡胶推进剂装药发动机,沿着药柱径向不同位置进行了取样,并采用扫描电镜、能量色散X射线光谱仪、红外吸收光谱、交联密度等多种手段对这些样品进行了测试分析。研究发现：贴壁浇注发动机药柱不同位置推进剂的老化程度不尽相同,越靠近非金属壳体位置,推进剂的老化程度越严重。进一步深入分析表明：除了丁羟胶中双键的氧化断链和端羟基的缩合等影响推进剂老化,铝粉氧化产生的铝离子会催化丁羟胶双键与氯气或水发生加成反应生成■双键和仲醇,而铝离子迁移和分布的不均匀会引起药柱老化的不均匀性。研究结果对推进剂配方设计和防老化措施的研究具有重要意义。     

Neural network modeling to evaluate the dynamic flow stress of high strength armor steels under high strain rate compression

An artificial neural network (ANN) constitutive model is developed for high strength armor steel tempered at 500 °C, 600 °C and 650 °C based on high strain rate data generated from split Hopkinson pressure bar (SHPB) experiments. A new neural network configuration consisting of both training and validation is effectively employed to predict flow stress. Tempering temperature, strain rate and strain are considered as inputs, whereas flow stress is taken as output of the neural network. A comparative study on Johnson–Cook (J–C) model and neural network model is performed. It was observed that the developed neural network model could predict flow stress under various strain rates and tempering temperatures. The experimental stress–strain data obtained from high strain rate compression tests using SHPB, over a range of tempering temperatures (500–650 °C), strains (0.05–0.2) and strain rates (1000–5500/s) are employed to formulate J–C model to predict the high strain rate deformation behavior of high strength armor steels. The J-C model and the back-propagation ANN model were developed to predict the high strain rate deformation behavior of high strength armor steel and their predictability is evaluated in terms of correlation coefficient (R) and average absolute relative error (AARE). R and AARE for the J–C model are found to be 0.7461 and 27.624%, respectively, while R and AARE for the ANN model are 0.9995 and 2.58%, respectively. It was observed that the predictions by ANN model are in consistence with the experimental data for all tempering temperatures.