大学英语教学情境式设问应用策略初探

将情境式设问与授课内容导入交互结合的模式融入精读课堂教学,能有效提高教学效果和效率。本文以《21世纪大学英语》(读写教程第三册)Unit3,Text A,The Sense of Wonder为例,具体分析了情境式设问与课文内容导入相结合的课堂设计方案和应用策略。     

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。     

UG在高速加工中的应用

高速加工(HSM)具有许多优点，被广泛应用在航空产品的数控加工中。但是使用高速加工技术，不仅要有适合高速加工的设备，选择适合进行高速加工的刀具，更为重要的是选择合理的编程策略。任何编程策略的不合理都会造成严重的后果。所以数控编程一直是高速加工的重点和难点。以UG软件为研究对象，作者提出了实现高速加工的数控编程的策略，并使用高速铣雕设备对生成的NG代码进行了加工，加工结果验证了NG代码是正确的、合理的。说明了采用合理的加工策略，使用UG软件可以实现高速加工的编程。     

基于共享数据关系的线程调度策略

对基于CMT线程调度策略的研究,将线程间的共享数据作为启发指标,提出一种基于对共享存储使用情况分析的线程调度策略。通过共享数据的情况获得线程之间的耦合关系,根据耦合关系构造无向图,采用迭代算法融合图中节点,最终完成线程到处理核的分配。仿真实验表明,将共享数据作为启发指标可以有效减少资源的争用,通过实验分析,随着矩阵阶的增大,依据数据共享关系进行线程组合对性能提升的优势更加明显。     

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。     

面向对象开发方法的单元测试研究

简要介绍了面向对象开发方法的单元测试面临的特殊性问题并进行分析，探讨了面向对象单元测试采取的策略和测试用例的设计方法，最后对面向对象单元测试的充分性问题进行了讨论。     

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。     

Heading control strategy assessment for coaxial compound helicopters

The coaxial compound helicopter has two possible strategies for heading control: collective differential and rudder deflection. A flight dynamics model is developed to assess the effect of different heading control strategies. This includes the trim characteristics, steady flight performance,controllability, and manoeuvrability. The trim study demonstrates that heading control strategies are less influential on trim results, and the steady flight performance is also not significantly affected by the heading control strategy adopted. The controllability analysis shows although heading bandwidth and phase delay results at various speeds with different heading control strategies are all satisfied, the control derivative of the collective differential decreases as speed increases, and its heading aggressive agility is degraded into Level 3 in high-speed flight. In addition, using collective differential would lead to severe heading-rolling coupling as forward speed increases. On the contrary, the control derivative and aggressive agility of the rudder deflection is improved with forward speed, and there is no evidence of heading-rolling coupling. Finally, the transient turn MissionTask-Element(MTE) is utilized to investigate the heading manoeuvre characteristics in different heading control strategies, which indicates that the collective differential would add the amplitude of control input and the power consumption during this MTE.     

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.