用于静电放电EMP实验的三模冗余技术

通过对TMR(Trip le Modu lar Redundancy)容错技术的分析和研究,介绍了一种基于FPGA芯片的TMR整体“硬化”技术,并分别对采用了TMR技术的电机容错控制系统和未采用TMR技术的一般电机控制系统进行了ESD EMP的辐照效应实验,得出TMR技术可有效增强控制系统抗ESD EMP能力的结论。     

DEA方法在防空作战方案优选中的应用

针对现代防空作战对决策速度和准确性的要求,运用DEA方法研究了防空作战方案优选问题。根据防空作战方案构成的主要要素,建立了防空作战方案的C2R评估模型,从“技术有效”和“规模有效”两个方面对备选方案的有效性进行综合分析,得出各方案评估结果,为指挥员优选方案提供了科学准确的依据。与传统的方法相比,该方法不仅具有内在的客观性,避免了方案中投入和产出指标间的相互制约,而且易于实现计算机处理,有效保障指挥员在防空作战中快速准确选定作战方案。     

精密坐标镗床CNC系统研究

介绍了作者研制的通用ＣＮＣ系统,该系统以ＡＳＴ３８６作为控制器,采用ＰＷＭ驱动直流伺服电机,并配以软、硬件接口;文中对该系统的主要组成部分作了分析,在此基础上应用传统的ＰＩＤ控制方法设计数字控制器;最后通过实验验证了该系统的输出响应和控制器的性能。     

目标自动跟踪在对抗系统中的仿真

研究了在OpenGVS仿真平台上实现目标自动跟踪系统仿真的方法。根据目标自动跟踪系统的基本原理建立了其系统的仿真数学模型,然后运用MFC,OpenGVS在HLA搭建的对抗仿真环境中实现了半实物仿真。     

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles(UAVs).In this paper,aiming to address the control problem of maneuvering target tracking and obstacle avoidance,an online path planning approach for UAV is developed based on deep reinforcement learning.Through end-to-end learning powered by neural networks,the proposed approach can achieve the perception of the environment and continuous motion output control.This proposed approach includes:(1)A deep deterministic policy gradient(DDPG)-based control framework to provide learning and autonomous decision-making capa-bility for UAVs;(2)An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning;and(3)An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV's control model built based on MN-DDPG.The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV's flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.     

军事装备在突发事件应急响应中的应用

军事装备应用于突发事件应急响应并非不可行或大材小用,我国在自然灾害、事故灾难和海上联合救援等领域已积累了较为丰富的应用经验,国外一些国家同样有着相关应用实例。本文认为,此类应用符合我国军队宗旨与现行规制,能够满足风险社会下的应急能力（资源）整合需要,装备的优势在响应过程中也能发挥特殊作用。但相关应用并非是指一般突发事件,而是重大或特殊的突发事件。根据我国现有规制与军事装备的优势,这类装备至少可应用于巨灾式自然灾害、重大公共卫生事件、高危害性事故灾难和规模性撤侨等突发事件中。为进一步促进军事装备的此类应用,需要完善应用的相关规制,使得应用更为规范和顺畅;同时加强响应中多主体的协调,提升应用效率;以及促进军民融合的深度发展,实现双向促进。     

Bidirectional parallel multi-branch convolution feature pyramid network for target detection in aerial images of swarm UAVs

In this paper, based on a bidirectional parallel multi-branch feature pyramid network (BPMFPN), a novel one-stage object detector called BPMFPN Det is proposed for real-time detection of ground multi-scale targets by swarm unmanned aerial vehicles (UAVs). First, the bidirectional parallel multi-branch convolution modules are used to construct the feature pyramid to enhance the feature expression abilities of different scale feature layers. Next, the feature pyramid is integrated into the single-stage object detection framework to ensure real-time performance. In order to validate the effectiveness of the proposed algorithm, experiments are conducted on four datasets. For the PASCAL VOC dataset, the proposed algorithm achieves the mean average precision (mAP) of 85.4 on the VOC 2007 test set. With regard to the detection in optical remote sensing (DIOR) dataset, the proposed algorithm achieves 73.9 mAP. For vehicle detection in aerial imagery (VEDAI) dataset, the detection accuracy of small land vehicle (slv) targets reaches 97.4 mAP. For unmanned aerial vehicle detection and tracking (UAVDT) dataset, the proposed BPMFPN Det achieves the mAP of 48.75. Compared with the previous state-of-the-art methods, the results obtained by the proposed algorithm are more competitive. The experimental results demonstrate that the proposed algorithm can effectively solve the problem of real-time detection of ground multi-scale targets in aerial images of swarm UAVs.     

不同采集设备时间对准研究及误差分析

在数据采集和测试领域,经常出现对不同采集设备的记录参数进行对比分析的情况.为保证对比分析的正确性,需要对来自不同采集设备的数据进行时间上的对准.针对采集设备缺少时间信息的情况,提出一种利用关键参数的关键变化节点实现不同采集设备时间对准的方法.选取记录在不同采集设备中的同一个参数设为关键参数,分别查找该参数在两采集设备中发生同一状态变化的采集时刻位置,该时刻位置作为关键时间节点.利用该关键节点和采集设备的采集频率,实现不同采集设备起始时刻的对准.最后利用数值内插,实现不同采集设备在任意时刻的时间对准.通过均值和方差工具对该方法的时间对准精度进行分析,结果表明时间对准精度取决于采集设备的采集频率,当不同采集设备采集频率差异较大时对准精度取决于较低的采集频率.     

一种基于生物免疫原理的入侵检测新模型

入侵检测系统可以从生物免疫系统的很多特点中得到启发,文中利用生物免疫原理设计了一个新的入侵检测框架模型,该框架在传统信息传输网基础上构建了免疫淋巴网,用来监控和管理传统传输网的行为。模型中还应用了阴性选择、克隆选择等免疫算法,使得该模型对于入侵检测问题有较好的敌我识别功能。     

磷碳键的构建

含有磷碳键的有机磷化合物广泛应用于农药、医药、塑料添加剂等领域,构建磷碳键是有机磷化学研究的基本问题之一．概要的评述了Michaelis-Arbuzov反应、Becker反应、过渡金属催化反应和含磷氢键化合物对不饱和烯炔的加成等几种形成磷碳键的化学反应．