消防科学数字图书馆建设模式探析

消防科学数字图书馆建设模式研究是消防科学数字图书馆的基础性工作。从消防科学数字图书馆建设模式研究的目的意义、指导原则和研究内容出发,论述了应如何建立消防科学数字图书馆的建设模式。     

武警院校教育转型后图书馆建设浅见

武警院校教育正由以学历教育为主体向以任职教育为主体转型。图书馆要主动适应院校教育转型需要,构建科学合理的馆藏文献信息资源体系,改进和深化服务工作,建设一支高素质的馆员队伍,实现图书馆建设的可持续发展。     

地方高校图书馆主动服务对军校图书馆的启示——对全国15所重点高校图书馆网站的调查与分析

从图书馆主动服务的理念入手,探讨利用网络开展主动服务的可能性,并结合对全国15所重点高校图书馆网站和实践开展的多种主动服务方式进行调查统计,分析总结其特点,由此得出对军校图书馆开展主动服务的启示。     

基于DRFM的机载PD雷达干扰研究

针对传统干扰技术对PD雷达干扰的不足,噪声干扰对干扰机功率要求高,欺骗干扰产生的干扰数目单一,基于当前对抗先进体制雷达的手段——DRFM,对PD雷达干扰进行研究。通过将截获的雷达信号存储、处理,按程序设定的转发间隔重复读出当前采样信号并转发,产生了多个距离欺骗假目标;通过对每次转发干扰进行频率调制,产生了包含距离速度欺骗的一系列假目标;并经过Matable仿真验证了在PD雷达窄带滤波器中,出现多个距离或速度欺骗假目标。并通过仿真验证了干扰的有效性。     

WinCC在数字化油库监控系统中的应用

为适应油库数字化建设需要,解决油库监控中区域分散、设备点多、监控面广导致的数据采集和统一管理较为困难、系统实时性差、可靠性低等问题,设计了基于WinCC的数字化油库监控系统。该设计应用OPC技术实现与控制器的数据交互,利用用户归档和变量归档,有效实现数据库的建立、查询和修改,克服了软件编程中与现场设备通信复杂和一般组态软件数据管理较差的缺陷,大大缩短了系统开发周期,使系统操作方便,界面友好美观,维护简单快捷,可广泛应用于油库监控。     

一种基于TMS320C6678的JPEG编码算法并行实现方法

针对数字视频总线在装备应用中的实时性需求,在分析JPEG编码原理和多核DSP任务并行的基础上,提出了一种基于TMS320C6678的JPEG编码算法并行实现方法,该方法实现了任务级并行流水和核间高效通信。实验结果表明,该方法是可行、有效的,并且基于TMS320C6678的JPEG编码系统的实时处理能力有很大提升。     

BDS/GPS双模自适应抗干扰接收机关键技术研究与设计

针对现代导航战对卫星导航接收机干扰对抗能力的要求,研究了BDS/GPS双模自适应抗干扰接收机的关键技术。在分析传统空时零陷技术的基础上,引入基于数字波束形成的自适应抗干扰技术,并使用惯性模块和历书进行卫星信号初始来波方向估计,显著增强了接收机的抗干扰能力。同时,提出了BDS/GPS双模多阵元天线耦合测量与校正技术,有效解决了双模天线设计难题。仿真测试表明:接收机使用数字波束形成技术后,总体抗干扰能力可达85dB,比单一使用空时零陷技术增加了8dB。     

考虑不确定参数及外部扰动数字液压缸非线性鲁棒位置跟踪控制

在考虑不确定参数及外部扰动的情况下为数字液压缸位置跟踪系统设计了一个非线性控制器。首先,建立了数字液压缸位置跟踪系统的非线性模型;然后,构造了一个李雅普诺夫函数及非线性控制器,推导了系统鲁棒稳定的充分条件,由此将非线性控制器的设计问题转化成为线性矩阵不等式的求解问题;最后,进行了仿真及实验验证。结果表明:所设计的控制器对于参数不确定性及外部扰动具有良好的鲁棒稳定性及更好的动态特性。     

Undergraduate disarmament and nonproliferation education: gaps,opportunities, and new approaches

ABSTRACT

Despite living in a nuclear-weapon state, young Americans are generally ill-informed about weapons of mass destruction (WMD) and their means of control. The result is both widespread apathy toward nonproliferation and disarmament decision making among the general public and a looming personnel crisis within government sectors that enact policy in these domains. Considering that 67 percent of high school graduates in the United States go on to pursue a bachelor’s degree, exposing more undergraduates to nonproliferation and disarmament issues could contribute to addressing both of these challenges. The present study analyzes how these issues are already being taught at select US colleges and universities and explores ways to introduce them to more students that align with current priorities in higher education, such as interdisciplinary learning, digital humanities, and data-science learning. It also proposes concrete steps that the WMD policy community can take to help institutions of higher education integrate these topics more broadly into their curricula. The anticipated result is greater support for education in this important issue area across different stakeholders in academia, as well as increased engagement with these critical issues among a more diverse population of young people.     

Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm

To solve the problem of realizing autonomous aerial combat decision-making for unmanned combat aerial vehicles (UCAVs) rapidly and accurately in an uncertain environment, this paper proposes a decision-making method based on an improved deep reinforcement learning (DRL) algorithm: the multi-step double deep Q-network (MS-DDQN) algorithm. First, a six-degree-of-freedom UCAV model based on an aircraft control system is established on a simulation platform, and the situation assessment functions of the UCAV and its target are established by considering their angles, altitudes, environments, missile attack performances, and UCAV performance. By controlling the flight path angle, roll angle, and flight velocity, 27 common basic actions are designed. On this basis, aiming to overcome the defects of traditional DRL in terms of training speed and convergence speed, the improved MS-DDQN method is introduced to incorporate the final return value into the previous steps. Finally, the pre-training learning model is used as the starting point for the second learning model to simulate the UCAV aerial combat decision-making process based on the basic training method, which helps to shorten the training time and improve the learning efficiency. The improved DRL algorithm significantly accelerates the training speed and estimates the target value more accurately during training, and it can be applied to aerial combat decision-making.