单晶高温合金叶片用熔模壳型的研制

介绍了一种加矿化剂的单晶壳型。它具有高强度、壁厚薄而均匀的特点，已应用该壳型成功浇注了单晶叶片。     

运动单站干涉仪相位差直接定位方法

针对传统干涉仪测向定位在低信噪比(SNR)下性能降低、多频段系统复杂等缺点,本文直接利用运动单站干涉仪模糊相位差(PD)实现对地面静止辐射源定位.为解决该定位问题,结合干涉仪相位差的几何特性,提出了一种多角度距离参数化高斯和滤波(MB-RP-GMF)定位解算方法,利用相位差误差检测获得多个初始方位角,通过距离参数化得到一组滤波初始值,采用扩展卡尔曼滤波(EKF)进行定位计算.仿真结果表明,该定位方法在低信噪比下优于仅测向(BO)定位,且仅采用一组干涉仪便可对多个频段辐射源进行定位.     

多功能信号发生器的设计与制作

仪器由AT89C55单片机进行控制,通过键盘、LCD显示模块进行人机对话,完成波形选择、频段选择,频率、幅度输出控制。由MAX038产生正弦波、方波、三角波,其波形选择由单片机进行控制;频段选择由五档组成,可通过旋扭开关完成;频率由单片机输出数据经D/A转换后控制MAX038;幅度由单片机输出数据经D/A转换后改变放大器负反馈深度来实现输出信号幅度的变化。     

Disturbance observer based model predictive control for accurate atmospheric entry of spacecraft

Facing the complex aerodynamic environment of Mars atmosphere, a composite atmospheric entry trajectory tracking strategy is investigated in this paper. External disturbances, initial states uncertainties and aerodynamic parameters uncertainties are the main problems. The composite strategy is designed to solve these problems and improve the accuracy of Mars atmospheric entry. This strategy includes a model predictive control for optimized trajectory tracking performance, as well as a disturbance observer based feedforward compensation for external disturbances and uncertainties attenuation. 500-run Monte Carlo simulations show that the proposed composite control scheme achieves more precise Mars atmospheric entry (3.8?km parachute deployment point distribution error) than the baseline control scheme (8.4?km) and integral control scheme (5.8?km).     

多通道智能场强计的研制

介绍87C51单片机在研制多通道场强计中的应用情况以及软件和硬件的研究设计。     

电机瞬态转速测试

介绍了一种利用单片机和光电转速传感器来测量电机瞬态转速及电机转速变化过程的方法。该方法简单、反应时间快、准确度高。     

一种新型测距仪的研制

介绍了一种在中，小范围内，采用激光定位，超声测距的新型测量仪器，该仪器以单片微处理器为核心，对不同的测量距离，采用不同频率的超声波，结合信号处理方法，从而实现０．６～１４ｍ范围内高精度实时测量目标距离，其测量相对误差优于１％。     

可编程数字锁相环的研制

介绍一种可编程数字锁相环。它由单片机和数字锁相环构成。其中锁相环的工作状态和参数由单片机处理和控制。这样能较好地解决稳态误差与稳态建立时间之间的矛盾，基本上实现了在保证稳态误差小的同时，可大幅度地缩短稳态建立时间的目的，使原来的数字锁相环的性能更优。     

一种研究圆柱度目标函数单谷性的新方法

提出了一种研究圆柱目标函数单谷性的新方法，圆柱度目标函数单谷性的问题目前研究得还少，而弄清楚圆柱度目标函数是否是单谷函数对于用优化原理正确地计算其极值是非常重要的。     

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.