校罗差时磁偏差设定为零的机理分析

在飞机地面通常检查时,磁差给定器磁偏差值往往设定为当地磁差,才能得到正确的航向。校正磁罗差时,只有将磁差给定器设定为000.0,不然罗差计算会大大溢出指标。文章通过磁罗差校正的原理和校正方法的论述,进一步分析了校罗差时将磁偏给定器设定为000.0的工作机理。     

公差原则中相关要求的计算问题

关于公差原则中的相关要求的计算问题 ,本文提出了两种方法 :计算法和图解法。运用这两种方法可以简化此类问题的计算过程 ,准确求得计算结果。     

蜂窝夹层板撞击实验数据中的野值判别

基于超高速撞击物理实验数据对蜂窝夹层板撞击极限方程进行修正是获得高可信度新方程的一种常用方法,为了提高物理实验数据的可靠性,以国外131个碳纤维复合材料(CFRP)面板的蜂窝夹层板实验数据为对象,进行野值判别方法研究,发现该批数据中存在1个野值。将该野值剔除并基于剩余的130个数据重新进行方程修正后,新方程的总体预测率和安全预测率分别达到82.3%和93.1%,其绝对误差平方和、相对误差平方和分别为0.010、0.506,相对于剔除野值前的修正方程有所改善,表明实验数据野值判别方法可行、有效。为考核方法的适用性,对铝合金面板的蜂窝夹层板的实验数据也进行野值判别分析,结果显示该方法可合理识别野值。     

温度传感器辐射修正校准的准确牲研究

为了确定温度传感器辐射修正校准的准确性，需要对温度传感器辐射修正校准结果进行对比及不确定度分析。本文介绍了在热校准风洞中进行温度传感器辐射修正校准的结果，并做了对比研究和不确定度分析。     

攻角试飞测试基准的误差修正

阐述了攻角测量误差的成因和攻角试飞校准的必要性。选定机头空速管攻角传感器为攻角试飞的测试基准，分析其攻角探测特性，并运用细长旋成体理论重点计算了机体绕流对攻角测量的影响量。探讨了攻角试飞测试基准的误差修正问题。     

光线示波器振子的选用

为了准确地测量记录各种被测信号,应当正确地选用振子的固有频率和阻尼,适当地选取被测信号的大小,保持信号源内阻Ri与振子的内阻rg相匹配,同时要正确地调整光点位置,以减小圆弧误差.     

准分子激光矫正屈光不正的原理及其实现

研究了准分子激光治疗屈光不正的原理及其实现方法，介绍了准分子激光治疗屈光不正的原理及其种类。在初始角膜模型假设的基础上，提出了能够用于近视、远视和散光等屈光手术的数学模型，计算出切削量和切削的层数，并给出了相应的算法框图。就准分子激光角膜切削机理和“飞点扫描”等关键技术进行了研究。研究成果直接用于准分子激光眼科治疗机，并给出了准分子激光眼科治疗机的原理框图。实验和临床数据表明。取得了满意的手术效果。     

壁面摩擦对风洞侧壁干扰的影响

本文研究了摩擦对风洞侧壁干扰的影响。在以Barnwell为代表开展的一系列侧壁干扰研究中,所有工作都未计及摩擦的影响。我们认为,Barnwell等人的这一处理理论上有缺陷。对此,本文用Karman-Pohlhausen方法对侧壁边界层作了分析,指出摩擦对侧壁干扰有不同程度的影响,具体取决于Pohlhausen参数B的大小;B越小,影响越大。Barnwell略去摩擦项的结果只对应于分离边界层情况,而事实上Barnwell处理的是附着边界层。通过这一分析,本文建立了有摩擦影响的侧壁干扰修正的新形式;考虑到多种因素的影响,初步建议参数B按中间值取为B=3～4。最后,就修正参数δ~*的确定作了一定讨论。     

考虑多因素的可修系统任务可靠性分配方法

在实际工程中,系统常常是由串联、并联、旁联和表决等模型混合而成的复杂可修系统,目前此类系统的可靠性分配方法多采用等分分配等方法,得到的分配结果往往误差较大。本文提出了一种考虑维修、故障逻辑等多因素影响的可修系统任务可靠性分配方法。该方法以系统故障率为待分配指标,首先对包含维修影响的故障率进行转换,然后利用考虑故障逻辑的评分分配法进行分配,最后通过备件系数进行修正,获得分配结果。新方法能够为在实际工程中可修系统的任务可靠性分配提供一种简便易行的方法。      

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.