履带车辆动力性计算的数学模型

以发动机动力特性计算式为基础,推导出履带车辆动力性参数计算的数学模型,为今后履带车辆设计,以及动力性能的工程计算分析提供了数学方法,并通过计算机编程对某型车辆进行了计算,结果表明该方法具有较高的准确度和工程实用性。     

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。     

基于影响因素分析的装甲车辆驾驶员作业可靠性评价模型

从影响因素分析人手,在对装甲车辆人-机一环境系统物理模型综合分析的基础上,运用灰色关联度法建立了基于驾驶员作业可靠性影响因素分析的定性评价模型,并结合某装甲部队的驾驶训练考核进行了应用。结果表明：考核成绩与模型计算结果有着比较吻合的变化趋势,验证了模型的有效性。所述研究方法根据不同装备、不同岗位特点,可对评价指标体系调整后进行移植和拓展使用。     

基于转速控制的电传动车辆动力分配策略

为解决电传动车辆行驶过程中的动力分配问题,在车辆整体方案设计的基础上,对驾驶员操作指令进行合理的解析,并提出了一种基于＂转速控制＂方式的动力分配控制策略,通过对左、右侧驱动电机进行转速控制给定,实现车辆在各种复杂工况下的正常行驶,最后进行了系统建模和仿真。结果表明：基于该动力分配控制策略,车辆可实现稳定直驶、任意半径转向、中心转向以及电气制动等工况要求,并且具有良好的动力性能。     

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。     

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.     

An improved adaptive differential evolution algorithm for single unmanned aerial vehicle multitasking

Single unmanned aerial vehicle (UAV) multitasking plays an important role in multiple UAVs cooperative control, which is as well as the most complicated and hardest part. This paper establishes a three-dimensional topographical map, and an improved adaptive differential evolution (IADE) algorithm is proposed for single UAV multitasking. As an optimized problem, the efficiency of using standard differential evolution to obtain the global optimal solution is very low to avoid this problem. Therefore, the algorithm adopts the mutation factor and crossover factor into dynamic adaptive functions, which makes the crossover factor and variation factor can be adjusted with the number of population iteration and individual fitness value, letting the algorithm exploration and development more reasonable. The experimental results implicate that the IADE algorithm has better performance, higher convergence and efficiency to solve the multitasking problem compared with other algorithms.     

预报风强度对火箭最大气动载荷精度影响及建模分析

针对不同预报风强度的精度以及其对应的最大气动载荷预报值精度的研究文献极少。以某地区实况风为基准,分别对不同预报风强度的精度以及其对应的最大气动载荷预报值精度特征进行了分析,并利用多元线性回归方法建立了订正模型,结果表明在预报日数第1～11天,预报风强度越大,预报风精度及其对应的最大气动载荷预报值精度越高;利用多元线性回归方法可提高最大气动载荷预报值精度,且预报风强度越小,最大气动载荷预报精度提高越明显。这些发现在火箭发射前的飞行保障及安全决策方面具有参考价值。