略论张恨水小说中的女性形象

张恨水是中国现代通俗文学大师,他塑造了众多生动的人物形象,几乎每一部小说中至少都有一个突出而鲜明的女性形象。本文主要通过对张恨水小说中女性形象类型,塑造女性形象的艺术手法,女性形象身上寄托了张恨水的人生思想等三个方面来分析张恨水小说中的女性形象,探究张恨水笔下的女性形象所蕴藏的深层文化内涵,反观张恨水的人生价值立场和人文关怀。     

一种基于知识因素表示理论的装备信息分类描述方法

当前信息分类的形式化描述是信息集成中一个研究重点和难点,基于知识的因素表示理论能够描述概念和概念之间的关系,并通过概念之间的关系描述概念的语义,该方法具有知识描述的统一框架和严格的数学理论基础.通过对装备信息分类方法研究,提出了装备全系统全寿命信息体系结构模型和基于知识因素表示理论的装备信息分类描述方法,并结合装备基础...     

面向大尺寸滑动窗口应用的并行计算模型

大尺寸滑动窗口的应用在数据输入速度与处理速度之间存在较大差距.为了缩短差距,提出了一种并行计算模型,使用尽可能少的存储资源与尽可能简单的存储器读写控制逻辑实现了尽可能高的数据重用性与并行性.该模型将不同滑动窗口之间的并行处理与单个窗口内不同数据之间的并行处理结合起来:对于不同窗口,按列进行分组并映射到多个处理单元上并行...     

改进类电磁算法在武器目标分配问题中的应用

通过基于级数的粒子编码变换方法,将武器目标分配问题的约束条件进行了化简.对原始类电磁算法,在种群初始化、局部搜索、合力计算以及粒子移动等各步骤对其进行改造,使之适应武器目标分配问题的整数解空间.最后通过数值实验验证了该改造后算法解决武器目标分配问题的有效性.     

美军作战实验室建设研究

简要回顾了美军作战实验发展的历史,从军种到联合详细阐述了美军作战实验室完整的体系,并简要介绍了各作战实验室所完成的典型实验,研究了美军作战实验室主要的实验方法和技术手段,剖析了美军作战实验室的运行机制,探讨了美军作战实验室建设带给我们的启示。     

基于功能树的导弹打击系统目标瞄准点选择方法

针对导弹打击系统目标的问题,建立了基于目标功能损伤度的瞄准点选择方法;在对毁伤树方法进行改进的基础上,提出了描述目标结构与工作流程的目标功能树模型,构造出体现目标整体功能下降的优化目标函数,采用模拟退火算法搜索最优解。通过算例对不同的瞄准点选择方案进行比较,发现考虑目标的功能结构特点时,得到的瞄准点明显优于其它方案。     

坦克外壳对内部电磁波的影响及其振荡干扰波型的数值计算

在坦克的金属立方体盒子模型下,分析了坦克外壳对内部电磁波的影响,并对Lc型电路激发的电磁波在坦克内部形成的振荡波型进行了计算,分析了该类电磁振荡波对坦克内部其他电子元件产生电磁干扰的作用机理,对一种最低频率的谐振波型进行了仿真模拟,提供了对坦克内部电磁波电场分量及磁场分量的一种分析计算方法,为研究坦克内部电子设备的电磁干扰规律提供了思路,对提高坦克的电磁兼容性能具有一定参考意义。     

数据挖掘故障诊断专家系统

为了及时准确地对防空导弹飞行试验故障进行诊断,克服传统专家系统获取专家知识的瓶颈,立足于防空导弹飞行试验历史数据,利用数据挖掘的方法获得飞行试验故障模式判定决策树,进而得出导弹故障判定规则,形成故障诊断专家知识库,构建防空导弹故障诊断专家系统.这使数据挖掘和专家系统两者的优点得到充分发挥.     

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.