双混频时差测量系统设计中的理论和技术问题

从分析双混频时差(DMTD)测量系统的测量误差着手,对系统设计和使用中常磁到的一些理论和技术问题进行了探讨,这涉及到公共源的影响、移相器的作用、自校和测量中的其它问题。     

新型环氧树脂潜伏性促进剂的合成、表征及性能研究

合成了新型环氧树脂潜伏性促进剂三(乙酰乙酸十八酯)铝,采用DSC,FT-IR,H-NMR、元素分析分别对其热分解机理、化学结构、物质纯度进行了表征.研究了以环氧ZH92-21∶双酚S∶三(乙酰乙酸十八酯)铝=l00∶3∶0.5为配比的树脂体系的固化制度、潜伏性和力学性能,树脂体系的表观粘度在室温储藏7个月后基本没有变化,相分离是其具有优异潜伏性的主要原因.     

三氢化铝的合成及性能研究

采用LiAlH4还原AlCl3的方法制备了AlH3,产品的产率大于90%。采用不同的稳定剂对合成的样品进行了稳定化处理,将经处理的样品通过在70℃进行放气量测试比较了热稳定性,发现经稀酸处理的AlH3样品稳定性最好。研究了AlH的安全性能,并通过DSC法研究了AlH与常用推进剂组分的相容性。     

基于模糊机会约束规划的机会阵雷达方向图综合

针对机会阵雷达方向图综合的不确定问题,提出了一种基于模糊机会约束规划的方向图综合规划模型。该模型基于可信性理论,综合考虑天线单元分布的随机性以及激励状态的不确定性,将参与方向图综合时激励打开的天线数目看做一个梯形模糊变量,用以描述综合时的复杂不确定环境。随后,将规划模型转化为清晰等价形式,再结合遗传算法和灰关联综合评价法则设计了一种混合智能优化算法,用于求解该模型。以一维任意非均匀线阵为例,对主瓣宽度和最大副瓣电平进行了优化。仿真表明:优化后结果的可信性高于置信水平,验证了该算法的有效性。     

波形合成的方向处理

波形合成中方向问题的核心是相位计算。本文作了分析     

弹性飞行器敏感元件位置设置与参数优化综合

本文研究了弹性飞行器敏感元件位置的设置问题，分析了敏感元件位置对弹性飞行器稳定性的影响，提出了几类飞行器自动驾驶仪敏感元件可能合适位置的选择准则。从飞行器弹性振动闭环系统方程出发研究了弹性飞行器参数优化综合的某些方法。通过算例得到控制系统返馈系数，陀螺位置，舵面位置优化综合的数值模拟结果。这些结果与该飞行器实际采用的值符合较好。     

介质材料对频率选择表面传输特性的影响

通过理论计算和实验验证分析了介质材料对频率选择表面传输特性的影响规律,讨论了蒙皮的厚度、介电常数,芯层厚度、介电常数四个因素对FSS的通带位置、宽度、损耗的影响。结果表明,蒙皮和芯层厚度增厚会使通带位置向低频移动,厚度减小通带位置向高频移动;蒙皮和芯层介电增大会使通带位置向低频移动,介电减小通带位置向高频移动。并测试了同样结构的FSS平板传输特性,理论分析和实验结果一致性较好。     

酚醛树脂炭的合成及力学性能研究

为了表征多孔炭泡沫材料基体的结构与力学性能,采用碱催化法制备热固性酚醛树脂,经固化、高温炭化处理合成多孔炭泡沫材料的基体——酚醛树脂炭。研究酚醛树脂炭的微观结构、压缩强度及压缩断裂韧性。结果表明:酚醛树脂炭结构以树脂炭基体、微孔和微裂纹为主,其碳元素以sp3杂化的非晶炭形式存在;压缩强度为8.58 MPa,压缩断裂特征为脆性断裂模式;酚醛树脂炭的压缩断裂韧性较差,断裂过程中吸收的总能量为0.135 MJ/m3。     

NiAl合金的热爆合成及其反应机制

本文研究了NiAl合金的热爆合成,其反应过程为液相控制,产物为β-NiAl,密度较高。本文给出了不同条件下合成的起爆时间及主要影响因素。     

Using TensorFlow-based Neural Network to estimate GNSS single frequency ionospheric delay (IONONet)

In the last 20?years, and in particular in the last decade, the availability of propagation data for GNSS has increased substantially. In this sense, the ionosphere has been sounded with a large number of receivers that provide an enormous amount of ionospheric data. Moreover, the maturity of the models has also been increased in the same period of time. As an example, IGS has ionospheric maps from GNSS data back to 1998, which would allow for the correlation of these data with other quantities relevant for the user and space weather (such as Solar Flux and Kp). These large datasets would account for almost half a billion points to be analyzed. With the advent and explosion of Big Data algorithms to analyze large databases and find correlations with different kinds of data, and the availability of open source code libraries (for example, the TensorFlow libraries from Google that are used in this paper), the possibility of merging these two worlds has been widely opened. In this paper, a proof of concept for a single frequency correction algorithm based in GNSS GIM vTEC and Fully Connected Neural Networks is provided. Different Neural Network architectures have been tested, including shallow (one hidden layer) and deep (up to five hidden layers) Neural Network models. The error in training data of such models ranges from 50% to 1% depending on the architecture used. Moreover, it is shown that by adjusting a Neural Network with data from 2005 to 2009 but tested with data from 2016 to 2017, Neural Network models could be suitable for the forecast of vTEC for single frequency users. The results indicate that this kind of model can be used in combination with the Galileo Signal-in-Space (SiS) NeQuick G parameters. This combination provides a broadcast model with equivalent performances to NeQuick G and better than GPS ICA for the years 2016 and 2017, showing a 3D position Root Mean Squared (RMS) error of approximately 2?m.