基于复杂网络理论的指挥信息网络拓扑模型研究

从网络科学的观点出发对作战指挥机构进行研究,建立了指挥关系网络和通信网络,以及在两者基础上的指挥信息网络拓扑模型,分析了指挥信息网络的度分布特性,提出了描述指挥信息网络的特征参量,进行了针对指挥信息网络的体系破击仿真实验,得出了与战争实际相符的结论,为利用复杂网络知识研究信息化条件下的体系对抗进行了积极地尝试和探索。     

RBF神经网络的C4ISR系统效能评估

针对当前效能评估方法存在人为因素干扰的问题,并考虑到RBF网络具有收敛速度快,无局部极小优点等优点,建立了基于RBF网络的C4ISR系统效能评估模型.模型采用K-均值聚类算法确定隐层节点数据中心和扩展常数;用最小二乘法进行输出层权值的修正;为防止病态,用目标总函数动态地确定隐节点的个数.最后,将得到的结果和BP网络输出...     

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。     

融合神经网络与超像素的候选区域优化算法

为解决目标检测中候选区域召回率低的问题,提出融合神经网络与超像素的目标候选区域算法。该算法利用神经网络提取更能清楚表达目标边界的特征,并使用聚类、相似性等策略,计算每个滑动窗口所含有的边缘信息量;将待测图像使用简单线性迭代聚类算法分割成若干个超像素,并利用超像素的空间位置、完整性、相邻超像素间的对比度信息,计算各个超像素的显著性得分及每个滑动窗口的显著性得分;根据每个滑动窗口的边缘信息及显著性得分筛选滑动窗口。在PASCAL VOC 2007测试集上进行对比实验,其实验结果表明:所述算法能够快速产生定位质量高的候选区域。     

Damage modeling of ballistic penetration and impact behavior of concrete panel under low and high velocities

This work presents a numerical simulation of ballistic penetration and high velocity impact behavior of plain and reinforced concrete panels. This paper is divided into two parts. The first part consists of numerical modeling of reinforced concrete panel penetrated with a spherical projectile using concrete damage plasticity (CDP) model, while the second part focuses on the comparison of CDP model and Johnson-Holmquist-2 (JH-2) damage model and their ability to describe the behavior of concrete panel under impact loads. The first and second concrete panels have dimensions of 1500 mm × 1500 mm × 150 mm and 675 mm × 675 mm × 200 mm, respectively, and are meshed using 8-node hexahedron solid elements. The impact object used in the first part is a spherical projectile of 150 mm diameter, while in the second part steel projectile of a length of 152 mm is modeled as rigid element. Failure and scabbing characteristics are studied in the first part. In the second part, the com-parison results are presented as damage contours, kinetic energy of projectile and internal energy of the concrete. The results revealed a severe fracture of the panel and high kinetic energy of the projectile using CDP model comparing to the JH-2 model. In addition, the internal energy of concrete using CDP model was found to be less comparing to the JH-2 model.     

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.     

改善Al对B4C润湿性的研究

依据金属Al的特性及其与B4C相互作用的关系,在理论上分析Al对B4C的润湿性,分析理论润湿性与实际润湿性差异的原因,在此基础上,通过实验寻求改善Al对B4C润湿性的方法和工艺,发现升高温度可以改善润湿性,温度由1 050℃升至1 100℃时,润湿角θ由52°降至23°;在Al中添加小量Mg后,真空1×10-3Pa,温度1 100℃×3 h时Al对B4C润湿角由原来的23°降至9°.     

信息化条件下装备保障组织变革的新逻辑

分析了装备保障组织作为一个开放的复杂系统,面对信息化所带来的巨大变化,其结构变革的必然性以及变革所要遵循的基本原则,指出了信息化时代装备保障组织所应具有的基本特征。     

光电干扰武器系统C^3I可执行建模研究

基于UML的C3I系统模型的设计结果是对系统的静态表述,而C3I系统的本质是动态的。着色Petri网(Colored Petri Net,CPN)具有强大的描述能力及严密的数学基础和多种分析手段,并且它是可以仿真运行的。提出了从UML产品映射到可用于逻辑、行为和性能方面体系结构评价的CPN可执行模型的方法,先对光电干扰武器系统C3I进行UML建模的一般描述,再详细地阐述了光电干扰武器系统C3I从UML产品映射到CPN建立可执行模型的过程。通过对可执行模型CPN的仿真可对光电干扰武器系统C3I进行完整准确地评价。     

基于SEA的舰载C3I系统防空作战效能评估模型

现代海战中,空中目标对舰艇的威胁越来越大,舰艇的防空作战能力已成为衡量其综合作战能力的主要指标。而C3I系统作为舰艇的指挥中枢,其性能直接决定着舰艇防空作战的成败。根据现代海战中空中目标的武器作战使用方式,并针对SEA方法的要求,提出了评估C3I系统在防空作战时系统效能的三个主要性能量度(MOP),通过分析C3I系统在防空作战时的特点,建立了评估C3I系统防空作战效能的SEA方法模型。并利用该模型比较了不同的C3I系统的作战效能,对结果进行了简要分析,证明了SEA方法模型对舰载防空C3I系统进行效能评估的可行性。