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.     

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。     

基于自准直反射法的光学中心偏检测系统研究

提出了一种基于自准直反射法测量光学中心偏的检测方法,分析了其测量光学中心偏的基本原理,在此基础上设计了基于自准直反射法的光学镜组中心偏检测系统。系统采用CV-A1型CCD探测器组件,以轴向晃动≤1″、径向跳动≤1μm的高精度基准轴工作台配合PIV研华工控机进行数据采集和图像处理,系统测量精度为:角度≤2,″线度≤0.01 mm。实际应用表明,该系统适用于1.06μm激光探测系统光学镜组的在线装校及系统中心偏检测,也可用于其它光学系统中心偏的测量。     

近程防空武器网络化跟踪系统

研究多探测器航迹滤波与互联新技术与新方法,把数据融合的基本理论和计算机网络技术应用于近程防空武器的跟踪系统中,得到了近程防空武器网络化跟踪系统的框架.应用数据融合和计算机网络技术可以提高近程防空武器的使用效率.     

基于小波网络的非线性系统辨识研究

小波网络为非线性系统辨识研究提供了一种有效的方法,但目前用于小波网络学习的进化算法易陷入局部极小等缺陷.结合生物免疫系统的概念和理论,在非线性系统辨识中引入基于免疫算法的小波网络.该算法中抗体通过浓度相互作用的机制来促进或抑制抗体的生成,借此保持抗体的多样性,并产生了高亲和力的抗体对种群进行不断的更新,提高了算法的全局搜索能力和收敛速度.最后,把基于免疫算法的小波网络用于一个非线性系统辨识的标准实例中,仿真结果验证了该算法的有效性.     

大学英语文化教学初探

大学英语教学在贯彻提高大学生英语知识水平的同时,必须把外语教学与文化教学紧密地结合起来。语言是文化的载体,又是文化的重要组成部分,文化对语言的产生和发展起着重要作用。大学英语教学要有目的、有计划地实施文化教学,通过掌握英语国家的历史背景、文化内涵来指导外语学习,培养学生对西方文化的了解,为今后顺利使用外语和胜任跨文化交际打好基础。     

涉外型院校实施体育“双语教学”的可行性研究

在社会飞速发展的时代,经济的进步促使知识的日新月异,导致教学模式的不断创新。伴随着我国加入WTO,双语教学应运而生.在体育教学中运用双语教学,是体育教学模式的大胆尝试和教学手段的创新.涉外型院校具有实施体育双语教学得天独厚的条件,双语教学的实施具有重要意义,并有可行性。     

任务空间概念建模语言初探

通过研究军事专家与系统分析人员的沟通问题,借鉴可视化建模、分层建模和多视图建模的思想,提出了任务空间概念建模语言(MSCML),对MSCML语言的各个建模元素进行了简要的说明.     

基于时滞分割法的具有leakage项时滞的离散型神经网络状态估计

摘要：研究一类具有leakage时滞的离散型神经网络的状态估计问题．通过构造新的Lyapunov泛函得到保证估计误差全局渐近稳定的充分条件,并通过求解一个线性矩阵不等式（LMI）得到状态估计器的增益矩阵．采用一种新的时滞分割方法将变时滞区间分割为多个子区间,使该结果在获得更小的保守性同时也降低了计算的复杂度．     

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．