一种定量建立主题数据库的算法

本文利用企业模型中的企业活动与实体之间的关系给出了实体组之间的相关系数定义，在此基础上，给出了实体组的分组算法，从而应可以根据主要数据库分划标准把实体划分成实体大组，形成主题数据库。     

高精度稠粘流体压力传感器

一种高精度流体压力传感器,这种传感器除适用于测量普通气体、液体的压力外,特别适用于稠粘流体的压力测量。文中着重阐述了这种传感器的工作原理、结构特点及性能指标,并简要介绍了设计中的一些考虑,指出了这种传感器的发展潜力和应用前景。     

低速壁压信息洞壁干扰修正方法两个重要的新改进

本文介绍了对美国洛克希德公司Hackett,J.E.等人研究的壁压信息洞壁干扰修正方法两个重要的新改进。建立了一个简便方法,消除了原方法必须要在模型下游测到壁压的渐近分布,才能进行准确修正的苛刻条件;建立了一个测压试验洞壁干扰的改进修正方法,使测压与测力试验的修正方法一致。文中给出了两组模型的验证结果。本文的两个新改进,消除了壁压信息法应用中的突出难关,不仅拓宽了应用,并大为提高了测力、测压试验洞壁干扰修正的准确性。     

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。     

综合运输中理想运力分配模型

本文对综合运输中各种运输方式的理想运力进行了理论上的探讨,希望能引起交通运输的主管部门的重视,并在实践中积极地实践,从而来检验理论的正确性.     

调控收入差别,缩小收入差距

全面建设小康社会是党的十六大确立的奋斗目标.全面建设小康社会要求我们重视收入分配,当前收入分配的改革重点应放在缩小收入差距上,通过收入分配的市场公平和社会公平来促进全面建设小康社会.     

正态分位点的矩阵算法浅析

假设标准正态的t分位点记为z(t),本文将根据z(t)的Taylor展开式中各个系数之间的矩阵递推关系,给出一种计算z(t)的近似值的方法,并给出具体的程序运算来验证本文的结论.     

颈动脉内血流动力学特征受向前加速度影响的数值模拟

人们在日常或某些特殊条件下常会面临方向和大小发生迅速变化的加速度环境,已有研究发现加速度环境会影响心血管系统生理、病理特征。利用计算机模拟方法详细研究了向前加速度环境下人体颈动脉内的血流动力学参数变化规律。结果表明:加速度会对颈动脉内压力、压力梯度、壁面剪切应力等与血管生理、病理现象密切相关的血流动力学参数产生显著影响。研究结果为加速度环境下颈动脉生理、病理研究提供了一定的理论依据,也为航空航天领域加速度环境下人员防护提供参考。      

The adjusted optical properties for Galileo/BeiDou-2/QZS-1 satellites and initial results on BeiDou-3e and QZS-2 satellites

Solar Radiation Pressure (SRP) is the dominant non-gravitational perturbation for GNSS (Global Navigation Satellite System) satellites. In the absence of precise surface models, the Empirical CODE Orbit Models (ECOM, ECOM2) are widely used in GNSS satellite orbit determination. Based on previous studies, the use of an a priori box-wing model enhances the ECOM model, especially if the spacecraft is a stretched body satellite. However, so far not all the GNSS system providers have published their metadata. To ensure a precise use of the a priori box-wing model, we estimate the optical parameters of all the Galileo, BeiDou-2, and QZS-1 (Quasi Zenith Satellite System) satellites based on the physical processes from SRP to acceleration. Validation using orbit prediction proves that the adjusted parameters of Galileo and QZS-1 satellites exhibit almost the same performance as the corresponding published and “best guess” values. Whereas, the estimated parameters of BeiDou-2 satellites demonstrate an improvement of more than 60% over the initial “guess” values. The resulting optical parameters of all the satellites are introduced into an a priori box-wing model, which is jointly used with ECOM and ECOM2 model in the orbit determination. Results show that the pure ECOM2 model exhibits better performance than the pure ECOM model for Galileo, BeiDou-2 GEO and QZS-1 orbits. Combined with the a priori box-wing model the ECOM model (ECOM+BW) results in the best Galileo, BeiDou-2 GEO and QZS-1 orbits. The standard deviation (STD) of satellite laser ranging residuals reduce by about 20% and 5% with respect to the pure ECOM2 model for Galileo and BeiDou-2 GEO orbits, while the reductions are about 40% and 60% for QZS-1 orbits in yaw-steering and orbit-normal mode respectively. BeiDou-2 IGSO and MEO satellite orbits do not benefit much from the a priori box-wing model. In summary, we suggest setting up a unified SRP model of ECOM+BW for Galileo, QZS-1, and BeiDou-2 orbits based on the adjusted metadata. In addition, we estimate the optical parameters of BeiDou-3e and QZS-2 satellites using a limited number of tracking stations. Results regarding the unified SRP model indicate the same advantages, the STD of satellite laser ranging residuals reduces by about 30% and 20% for QZS-2 and BeiDou-3e orbits respectively over orbit products without a priori model. The estimation procedure is effective and easy to apply to the new emerging satellites in the future.     

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.