基于结构聚集度的海面目标特征描述及匹配方法

在对海面目标进行光电图像跟踪时,由于跟踪技术的局限性,跟瞄点往往会出现对目标的"失跟"现象,为了判断这一情况,通常需要对前后帧跟瞄区域的目标图像特征进行对比判别。但红外图像基本不存在颜色及纹理特征,而仅使用形状特征对于持续运动中的复杂目标图像又难以取得良好的效果。为解决这一问题,提出了一种基于目标结构聚集度的特征描述及匹配方法。首先对目标图像进行均匀矩形划分,在此基础上建立基于矩形块填充比的邻域矩阵作为图像特征,计算相邻帧图像该特征的欧氏距离差,并加入面积差参数进行特征匹配。对多种不同情况下的实测及模拟目标进行了计算机仿真实验,结果表明这种特征能够有效判断"失跟"情况,并且对于光学及红外目标旋转具有较好的不变性。     

群众路线:中国共产党的根本路线

群众路线是我们党创造性地运用马克思主义的群众观点创立的根本路线,是我们党的优良传统和政治优势.新形势下坚持群众路线,是巩固党的执政地位的必然要求,是推进中国特色社会主义宏伟事业的强大动力,是构建社会主义和谐社会的重要因素,是党的执政能力建设和作风建设的核心所在.当前要结合人民群众的新特点,探索坚持群众路线的新规律;着眼人民群众的新诉求,拓展坚持群众路线的新载体;汲取人民群众的新创见,丰富坚持群众路线的新形式,不断开创坚持群众路线的新局面.     

混沌序列相关特性研究及在扩频通信中的应用

分别用Logistic映射和Chebyshev映射产生出混沌序列,在Matlab下进行相关性分析,再以Chebyshev映射产生的混沌序列代替传统的伪随机序列作为扩频通信系统的扩频码,在Simulink下进行扩频通信系统的仿真,综合分析了基于混沌序列的扩频系统的抗噪性能,仿真结果验证了基于混沌序列的扩频系统的抗干扰性能和混沌序列作为码的应用价值.     

多目标环境下基于分布式融合思想的误差估计方法

针对多雷达多目标跟踪过程中分布未知的系统误差估计问题,提出了基于"分布式融合思想"的误差估计方法。给出相应误差估计方法的计算公式,利用改进截断奇异值方法来减轻矩阵病态性的影响,提高误差估计的稳健性。设置了两种不同的系统误差仿真场景,对"分布式"误差估计方法在两种情形下的估计性能进行了仔细对比分析。结合"分布式"误差估计方法与"集中式估计"方法所体现出的优缺点,提出了一种将两种方法结合起来的系统误差估计算法,算法通过合理选择阈值门限η,能够在多雷达多目标且系统误差分布未知的复杂环境下对两种误差估计算法自适应地进行切换,从而充分发挥两种误差估计算法各自的优点,给出更好的误差估计结果。     

运动激光辐照圆柱薄壳温度场的数值模拟

对圆柱薄壳在四种不同运动模式激光源辐照下的温度场分布进行了分析研究。运用傅立叶级数展开法推导了在不同运动模式激光源作用下圆柱薄壳温度场的解析表达式,并对该解析表达式进行了编程计算,其计算结果与有限元解吻合良好。详细分析了旋转角速度与平动速度对圆柱壳温升分布的影响,发现在往复运动加热模式下光斑区域的温升变化最为剧烈。计算结果为导弹的抗激光辐射提供了理论依据。     

探空火箭级间段的CAD系统

本文在CADKEY绘图软件包的支持下,开发了加筋圆柱结构CAD系统—Hcadx.此系统通过菜单引导用户的操作,可用经验公式或解析法作轴压稳定分析,用罚函数法优化参数,可以在不退出Hcadx的情况下启动CADKEY,自动绘制设计草图。此草图经过适当地编辑、修改、便可成为工程中能接受的工作图纸。     

基于SysML的车辆信息系统设计

针对目前车辆信息系统缺乏有效设计方法的现状,结合新时期车辆信息系统的特点,提出了一种基于SysML的车辆信息系统设计方法。该方法明确了车辆信息系统设计过程的阶段和产物,并通过标准建模语言SysML的运用,用模型来支持系统的设计和有效性验证。该方法在智能巡航系统的设计中得到了运用。实践结果表明,该方法不仅能够自顶向下完整地设计系统、利用可视化的语言准确清晰地描述系统,而且能够在系统设计层面对系统功能逻辑和数据流进行动态仿真和迭代验证。该方法能够较好地指导新时期车辆信息系统的设计过程,满足信息系统的设计要求。     

MF-CFI: A fused evaluation index for camouflage patterns based on human visual perception

The evaluation index of camouflage patterns is important in the field of military application. It is the goal that researchers have always pursued to make the computable evaluation indicators more in line with the human visual mechanism. In order to make the evaluation method more computationally intelligent, a Multi-Feature Camouflage Fused Index (MF-CFI) is proposed based on the comparison of grayscale, color and texture features between the target and the background. In order to verify the effectiveness of the proposed index, eye movement experiments are conducted to compare the proposed index with existing indexes including Universal Image Quality Index (UIQI), Camouflage Similarity Index (CSI) and Structural Similarity (SSIM). Twenty-four different simulated targets are designed in a grassland background, 28 observers participate in the experiment and record the eye movement data during the observation process. The results show that the highest Pearson correlation coefficient is observed between MF-CFI and the eye movement data, both in the designed digital camouflage patterns and large-spot camouflage patterns. Since MF-CFI is more in line with the detection law of camouflage targets in human visual perception, the proposed index can be used for the comparison and parameter optimization of camouflage design algorithms.     

多武器平台目标域协同射击控制模型研究

目标域协同射击控制模型是在单平台火控系统空域窗射击控制、射击门控制等基础上,针对多武器平台对点目标或面目标协同射击的需求,进行了目标域和射击效能的定义,提出了空间/时间复合三维射击门的概念,设计了三维射击门模型,分别建立了多武器平台对点目标射击控制模型和多武器平台对面目标射击控制模型,通过对多武器平台协同射击效能的计算分析,证明了目标域协同射击控制模型可显著提高多武器平台协同射击对目标的火力打击效能.     

Real-time prediction of projectile penetration to laminates by training machine learning models withfinite element solver as the trainer

Studies on ballistic penetration to laminates is complicated, but important for design effective protection of structures. Experimental means of study is expensive and can often be dangerous. Numerical simu-lation has been an excellent supplement, but the computation is time-consuming. Main aim of this thesis was to develop and test an effective tool for real-time prediction of projectile penetrations to laminates by training a neural network and a decision tree regression model. A large number of finite element models were developed;the residual velocities of projectiles fromfinite element simulations were used as the target data and processed to produce sufficient number of training samples. Study focused on steel 4340tpolyurea laminates with various configurations. Four different 3D shapes of the projectiles were modeled and used in the training. The trained neural network and decision tree model was tested using independently generated test samples using finite element models. The predicted projectile velocity values using the trained machine learning models are then compared with thefinite element simulation to verify the effectiveness of the models. Additionally, both models were trained using a published experimental data of projectile impacts to predict residual velocity of projectiles for the unseen samples. Performance of both the models was evaluated and compared. Models trained with Finite element simulation data samples were found capable to give more accurate predication, compared to the models trained with experimental data, becausefinite element modeling can generate much larger training set, and thus finite element solvers can serve as an excellent teacher. This study also showed that neural network model performs better with small experimental dataset compared to decision tree regression model.