Data-driven prediction of plate velocities and plate deformation of explosive reactive armor

Explosive reactive armor (ERA) is currently being actively developed as a protective system for mobile devices against ballistic threats such as kinetic energy penetrators and shaped-charge jets. Considering mobility, the aim is to design a protection system with a minimal amount of required mass. The efficiency of an ERA is sensitive to the impact position and the timing of the detonation. Therefore, different designs have to be tested for several impact scenarios to identify the best design. Since analytical models are not predicting the behavior of the ERA accurately enough and experiments, as well as numerical simulations, are too time-consuming, a data-driven model to estimate the displacements and deformation of plates of an ERA system is proposed here. The ground truth for the artificial neural network (ANN) is numerical simulation results that are validated with experiments. The ANN approximates the plate positions for different materials, plate sizes, and detonation point positions with sufficient accuracy in real-time. In a future investigation, the results from the model can be used to estimate the interaction of the ERA with a given threat. Then, a measure for the effectiveness of an ERA can be calculated. Finally, an optimal ERA can be designed and analyzed for any possible impact scenario in negligible time.     

A pixel-level local contrast measure for infrared small target detection

Infrared (IR) small target detection is one of the key technologies of infrared search and track (IRST) systems. Existing methods have some limitations in detection performance, especially when the target size is irregular or the background is complex. In this paper, we propose a pixel-level local contrast measure (PLLCM), which can subdivide small targets and backgrounds at pixel level simultaneously. With pixel-level segmentation, the difference between the target and the background becomes more obvious, which helps to improve the detection performance. First, we design a multiscale sliding window to quickly extract candidate target pixels. Then, a local window based on random walker (RW) is designed for pixel-level target segmentation. After that, PLLCM incorporating probability weights and scale constraints is proposed to accurately measure local contrast and suppress various types of background interference. Finally, an adaptive threshold operation is applied to separate the target from the PLLCM enhanced map. Experimental results show that the proposed method has a higher detection rate and a lower false alarm rate than the baseline algorithms, while achieving a high speed.     

基于不同分裂的修正局部Crank-Nicolson方法对一维热传导方程的应用

本文采用不同分裂的修正局部Crank-Nicolson方法讨论了一维热传导方程的初边值问题。该方法不仅具有原来计算量少,无条件稳定的优点,而且推广了该方法对复杂的数学物理方程应用的可能性。理论上分析了无条件稳定性、相容性及收敛性,最后进行数值试验,验证了它的可行性。     

以优质的信息服务打造武警高校名牌图书馆

武警院校图书馆要以提供优质的信息服务为着眼点,不断创新服务理念、服务方法和服务手段,并通过各项措施保障优质信息服务,达到创建武警高校名牌图书馆的目的。     

伦理教育维度下工程院校校园网络文化构建

在工程院校网络文化构建中,充分发挥伦理教育功能是素质教育的重要内容。工程伦理教育作为人文教育与科技教育交叉结合的新学科,要求高校尤其是工程院校在网络文化构建中,确立科学的教育理念、正确的舆论导向,多学科交叉配合,培育学生树立社会责任感,正确的价值观、利益观和强烈的伦理道德意识。     

基于RBF神经网络的导弹自动驾驶仪故障诊断

系统地分析了某型导弹自动驾驶仪的结构功能,在此基础上提出了采用RBF(径向基函数)神经网络对自动驾驶仪进行故障诊断的方法;提出了用Gauss(高斯)函数对获取的数据进行归一化处理的方法,再用处理后的数据训练RBF神经网络。实验结果表明,训练后的神经网络能快速、准确地诊断自动驾驶仪的故障,为自动驾驶仪的故障诊断提出了一种新的方法。     

基于Zigbee无线传感器网络的押运感知节点的研究与设计

利用无线传感器网络及Zigbee协议标准,对押运信息感知监测系统进行了分析;提出了基于Zigbee无线传感器网络与空间定位技术、计算机技术、数据通信技术结合的押运信息感知监测系统架构;设计了押运感知监测网络中感知节点的软硬件,并且对网络感知节点软硬件功能进行了测试。     

基于灰色神经网络武器装备研制费用预测模型

提出了基于灰色系统理论与神经网络的武器装备研制费用组合预测模型,该模型首先采用灰色GM(0,N)模型对研制费用进行预测,利用LMBP神经网络对预测误差进行了模拟与修正,实例验证该方法具有较高的预测精度.     

基于概率神经网络的编队协同防空威胁评估

为了有效地进行防空作战部署,确保重点目标不受或少受损失,以防空作战实际需要为出发点,针对目前多平台协同防空作战中目标威胁评估问题普遍存在的不足,从舰艇编队的整体角度出发,运用概率神经网络的方法,建立了编队协同防空目标威胁评估问题的数学模型,给指挥员提供了正确决策。通过实例验证,证明了该模型的有效性和正确性。     

网络文学在大学生思想政治教育中的功能及其影响研究述要

随着网络的发展和普及,网络文学已深入人们的生活,大学生深受影响。网络文学作为思想政治教育的有效载体之一,它的影响给大学生思想政治教育工作注入了新的活力,同时也带来了巨大挑战。为了更直观地了解＂网络文学与大学生思想政治教育的交叉研究＂在国内的总体研究概况,笔者将对网络文学的研究成果做全面系统的梳理,提出进一步的思考,旨在为高校思想政治教育提供借鉴和参考。