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.     

Visual SLAM in dynamic environments based on object detection

A great number of visual simultaneous localization and mapping (VSLAM) systems need to assume static features in the environment. However, moving objects can vastly impair the performance of a VSLAM system which relies on the static-world assumption. To cope with this challenging topic, a real-time and robust VSLAM system based on ORB-SLAM2 for dynamic environments was proposed. To reduce the influence of dynamic content, we incorporate the deep-learning-based object detection method in the visual odometry, then the dynamic object probability model is added to raise the efficiency of object detection deep neural network and enhance the real-time performance of our system. Experiment with both on the TUM and KITTI benchmark dataset, as well as in a real-world environment, the results clarify that our method can significantly reduce the tracking error or drift, enhance the robustness, accuracy and stability of the VSLAM system in dynamic scenes.     

装备表面现代维修保养技术研究

结合装备表面智能维修保养技术方面的研究成果,介绍了装甲装备故障智能检测技术、自适应、自修复功能材料,装甲装备器材全自动保养设备等先进技术,侧重装备维修保养阐述了如何提高信息化程度,推进军队武器装备维修保养技术的现代化建设。     

网络中心作战体系效能评估方法初探

网络中心作战体系是信息化战争要求下的一个具体作战体系,其效能评估是网络中心战研究领域的重要问题,是解决网络中心作战体系建设中方案论证、性能效能指标、经济因素等诸多问题的有效方法。首先提出了网络中心作战体系效能评估的基本概念;然后阐述了网络中心作战体系效能评估方法问题,分析介绍了几种较为成熟的评估方法的特点、适用性和不足;最后提出一种基于探索性建模与分析方法的网络中心作战体系效能评估框架,作为研究网络中心作战体系效能评估的参考评估框架。     

Property distribution and configurations of sovereign states: A rational economic model

A nation's wealth is both an object of conquest to covetous aggressors and a resource to its owners for self defense. To maintain autonomy every country must mount a defense which either makes its capture (1) more expensive than any aggressor can afford, or (2) more expensive than it is worth to aggressors. Whether this condition can be satisfied for all countries simultaneously depends as shown in this paper on relative efficacy of military offense versus defense, the aggregate of wealth among nations and its distribution, and the benefits a conqueror may obtain from conquest, including the duration of these benefits. The paper shows how these factors fit together to determine the sustainability and stability of the international distribution of property as embodied in the configuration of sovereign states.     

战时装备调配保障决策模型构建研究

分析了战时装备调配保障决策的特点,运用模糊聚类分析法对装备调配保障对象进行了分组和优先级排序,以此为基础,运用目标规划法构建了战时装备调配保障决策模型,最后,通过实例验证了模型的可行性和有效性．     

内编队地球重力场测量性能数值分析

内编队系统通过实现内卫星纯引力轨道环境和内卫星精密定轨,完成高精度地球重力场测量,实现了不依赖于加速度计的重力卫星实施新途径。针对内编队重力场测量性能难以解析分析的情况,基于MATLAB并行程序设计进行了重力场测量数值模拟,获得了内编队重力场测量的有效阶数及其精度。在内编队轨道高度为300km、内卫星干扰力为1.0×10-10m/s2、外卫星定轨精度为3cm、内外卫星相对状态测量精度为1mm的条件下,计算得到内编队测量重力场的有效阶数为72,相应的大地水准面累积误差为44cm,重力异常累积误差为4.5mGal,由此可知内编队测量重力场的有效阶数主要分布在低阶部分。     

基于组合赋权雷达图实现电网电能质量综合评估

将改进的组合赋权雷达图方法用于电网电能质量综合评估。对主观、客观赋权法组合,基于组合权重与原权重之间的偏差尽可能小的优化思想,求取雷达图中各项指标的组合权值。利用各指标对应的扇形区域的对角线作为指标轴绘制雷达图,不仅最大限度体现各指标的独立权重,也反映了各指标之间的相互影响。利用雷达图的面积和周长两个变量完成对电能质量的综合评估。最后通过应用实例,验证了论文方法的合理性和在电能质量综合评估中的有效性。     

基于联合相似测度的SAR图像边缘点特征配准方法

针对SAR图像配准过程中几何变换影响特征匹配稳健性和适应性的问题,提出了一种在特征匹配过程中直接解算几何变换模型的边缘点特征配准方法。利用SAR图像边缘点的梯度和方向特征,基于像素迁移思想,定义了图像匹配的联合相似度——联合特征均方和(SSJF),并建立了SAR图像边缘点集相似性匹配准则;基于方向模板提出了改进的ROEWA算子;利用改进的遗传算法(GA)来进行相似度的全局优化搜索,获取配准模型参数;利用多幅SAR图像的配准试验,对本文方法的性能进行了验证。     

基于可组合服务的陆军作战仿真模型集成

陆军仿真模型体系的复杂性决定了其建模与集成的复杂度,本文基于模型组件化技术,设计了模型组件库、对象库,通过逻辑组合实现了基本库和作战单元库,并以可组合的模型服务实现不同分辨率、异构模型的聚合集成目标,使得陆军作战仿真模型具备模块化、可复用、可扩展和可控制的应用特点和战技术性能。