型钢混凝土桁架转换层的监控与分析

对贵阳市某大楼型钢混凝土桁架转换层进行了施工期间的监控,并利用有限元分析软件ANSYS建立了带有型钢混凝土桁架转换层的结构有限元分析模型.针对工程中连接、构造和施工影响等相关因素进行有限元模型的对比分析,明确各因素对桁架受力的影响.结合施工阶段的监控数据,分析此工程中桁架受力状态的成因,为准确判断工程在正常投入使用后的...     

新疆泽普县农村中小学布局调整评价分析及建议

新疆泽普县农村中小学布局调整按照＂县办中学、乡办小学,中心村办幼儿园＂的总体要求,初步实现了学校规模办学及城乡教育一体化。本文对新疆泽普县农村中小学布局调整的整体状况进行了实地调研,并在此基础上对布局调整后取得的成效及存在的问题进行了具体的评价分析,提出了改进新疆泽普县农村中小学布局调整的建议。     

针对AES密码第1轮线性碰撞的代数攻击

将旁路攻击中线性碰撞思想与代数攻击方法相结合,使AES加密操作第1轮中的碰撞转换为线性方程组,构造了SAT求解器求解方程组,利用穷举攻击筛选真实的密钥。实验结果表明,该方法可降低密钥搜索空间,最终获得AES128位真实密钥,攻击效果优于碰撞攻击。     

弹药集装单元储存静力学有限元分析

以简单的六面体结构为基础,以“铝板-聚氨酯泡沫材料-铝板”复合板材为弹药集装单元的主体材料,结合弹药集装单元的承载能力要求,建立弹药集装单元的基本模型。通过其在储存状态下的静力学有限元分析,了解3种不同堆垛情况下,弹药集装单元的应力与应变情况,为进一步研究弹药集装单元结构强度提供理论依据。     

加权主成分TOPSIS法的防空导弹型号论证决策

针对防空导弹型号论证决策问题,讨论了影响型号论证的主要因素,建立了基于加权主成分TOPSIS法的防空导弹型号论证决策模型,实现了对新研制型号方案的综合评价和分析.在此基础上,通过实例验证了方法的实用性与有效性.论文的研究成果为防空导弹型号论证提供了辅助决策方法和智力支持.     

探空火箭级间段的CAD系统

本文在CADKEY绘图软件包的支持下,开发了加筋圆柱结构CAD系统—Hcadx.此系统通过菜单引导用户的操作,可用经验公式或解析法作轴压稳定分析,用罚函数法优化参数,可以在不退出Hcadx的情况下启动CADKEY,自动绘制设计草图。此草图经过适当地编辑、修改、便可成为工程中能接受的工作图纸。     

无奇点三维攻击时间控制滑模导引律

为研究导弹在视场角约束下的三维攻击时间控制问题,提出一种三维非线性滑模导引律。利用攻击时间误差设计滑模面,推导出三维形式的俯仰和偏航加速度指令;通过对所设计制导律进行简单修正,解决了零初始前置角引起的奇点问题;从数学上证明了该导引律的稳定性和收敛性,讨论了导引律相关参数的取值范围,分析了与纯比例导引法、解耦三维导引律之间的关系。仿真结果表明,所设计导引律能够在视场角约束条件下有效实现攻击时间增大或减小的控制;俯仰和偏航平面的耦合程度越强,该导引律控制能耗小的优势越为明显。     

动初级高速六相直线感应电机工作特性分析

为了准确刻画对象模型,采用一阶等效电路瞬态涡流分析法对动态边端效应影响下动初级高速六相直线感应电机的参数时变规律进行了定量计算,并通过感应涡流损耗推导得到考虑动态边端效应时电机的等效电路。基于此,通过电磁推力计算及效率评估对动态边端效应影响下电机的工作特性进行了详细分析。利用不同运行速度条件下的有限元仿真及动态实验验证了理论分析和推导的正确性。     

MF-CFI: A fused evaluation index for camouflage patterns based on human visual perception

The evaluation index of camouflage patterns is important in the field of military application. It is the goal that researchers have always pursued to make the computable evaluation indicators more in line with the human visual mechanism. In order to make the evaluation method more computationally intelligent, a Multi-Feature Camouflage Fused Index (MF-CFI) is proposed based on the comparison of grayscale, color and texture features between the target and the background. In order to verify the effectiveness of the proposed index, eye movement experiments are conducted to compare the proposed index with existing indexes including Universal Image Quality Index (UIQI), Camouflage Similarity Index (CSI) and Structural Similarity (SSIM). Twenty-four different simulated targets are designed in a grassland background, 28 observers participate in the experiment and record the eye movement data during the observation process. The results show that the highest Pearson correlation coefficient is observed between MF-CFI and the eye movement data, both in the designed digital camouflage patterns and large-spot camouflage patterns. Since MF-CFI is more in line with the detection law of camouflage targets in human visual perception, the proposed index can be used for the comparison and parameter optimization of camouflage design algorithms.     

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.