宽频带中频相位计的研制

比较全面地介绍了-宽带中频相位计的研制过程。论述了相位计的工作原理,给出了系统框图。对各个部件的设计做了介绍,并给出了相位计所达到的技术指标。还介绍了系统测试方法和检定方法,对系统的误差做了分析,并给出了理论计算误差与实测误差的对应关系,提出了今后的进一步设想。     

冷镜式露点仪测量精度影响因素分析

介绍了冷镜式露点仪的原理,分析了露点仪测量中的影响因素,针对开尔文效应推导了开尔文方程,通过计算的方法,验证了开尔文效应的影响大小。此外,还分析了乌拉尔效应和过冷水形成过程及原理,并提出了减小这些影响的措施和方法。     

计算机电磁泄漏测试及防护措施

论述了计算机电磁泄漏的危害及测试的意义,介绍了测试系统的组成,对某国产微型计算机进行了测试,并对其测试数据进行了分析,提出了行之有效的防护措施。     

月球探测器软着陆机构展开过程的运动学分析

软着陆机构是月球探测器实现软着陆的关键部件。针对一种四腿式软着陆机构方案,论述了该软着陆机构展开的工作原理,分析了机构的奇异性,推导了机构不发生奇异的条件,建立了展开机构的运动学方程组,采用Newton-Raphson法求解了该非线性运动学方程组,获得了主支撑杆运动参数的变化规律。     

高可靠性航空电子设备热分析中的有限体积法

阐述了热对系统可靠性的影响,对比了数值法求解温度场分布的优缺点.针对航空电子设备热分析中同时要求满足高的分析精度和快的计算速度的问题,提出用有限体积法求解温度场分布的计算方法.根据典型航空电子设备的结构特点,建立了温度场的数学模型,给出了边界条件.在此基础上,用有限体积法对待求解方程离散化,给出了离散化过程中网格划分及边界的处理方法.结合实例进行了温度场求解,计算结果与热测量结果进行了对比,得到了较高的分析精度,验证了方法的有效性.最后对计算误差进行了分析,并提出了进一步热设计的合理化建议.      

超量程位置误差基准桥接测量技术研究

以某类型专用检具为例,介绍了一种基于坐标测量机的利用基准桥接测量其位置误差的方法,对平行度和垂直度参数建立了数学模型并进行了计算推导,给出了实现测量的方法,提升了测量范围和测量能力,同时对测量结果不确定度进行了评定,证实了此方法的可行性。     

再入航天器滚速变化引起的落点误差分析

用转子动力学理论研究了再入航天器滚速变化引起的落点误差。通过分析一般攻角方程,给出了再入航天器的进动规律和配平攻角,据此,求出了不旋转航天器的落点误差;将轨迹分段后,推出了常值滚转速率航天器的平均落点误差;在滚速过零一周范围内,通过确定过零高度,分析了过零误差机理,并对该误差进行了计算;最后,分析了低空联锁共振现象,给出了发生联锁共振的条件和误差计算公式,并得出了几个重要结论。     

未来20年的太阳系探索（上）

1引言 1959年1月2日,苏联发射了月球-1月球探测器,月球-1成功飞到月球附近,开创了人类太阳系探索的新纪元。到目前为止,人类已经实现了飞越、环绕、着陆（包括硬着陆、软着陆与巡视车）、取样返回和载人探测（只在月球上实现了）等多种探测方式。50年的太阳系探索取得了许多突破性的成果,极大地加深了人类对太阳系的认识,同时也推动了空间技术的发展。现在,探索太阳系已经不是少数几个国家的行为,越来越多的国家和地区具有了独立探索太阳系的经济和技术基础,并制定了未来的探索计划。     

具有载荷扰动抑制的卫星姿态控制方法及验证

针对载荷转动对星体产生的呈周期变化的干扰,开展了干扰抑制方法研究。利用干扰的周期特性,基于李亚普诺夫原理设计了参数未知的周期干扰力矩的辨识算法,实现了卫星姿态的自适应控制。最后进行了物理仿真试验,利用真实的星载计算机和控制部件,在气浮台上进行了试验验证,结果表明,算法有效地抑制了载荷转动的干扰,卫星的姿态稳定度提高了50%,且算法简单,易于星上软件实现。     

新一代运载火箭姿态控制技术

开展了新一代运载火箭姿态控制设计技术研究,推导了工程适用的姿态动力学模型,基于牛顿-欧拉法建立了刚体质心和绕质心运动方程、液体晃动方程,基于有限元方法建立了火箭弹性振动方程,弹性运动方程基于空间模态,反映了横向、纵向、扭转与助推器局部模态对火箭的姿态与载荷的影响.基于空间模态的姿态动力学模型研究了姿控系统设计方法,给出了工程适用的姿控系统设计方法.开展了容错重构技术研究,分析了伺服机构故障对姿控系统的影响,为提高运载火箭的飞行可靠性和抗故障能力奠定了基础.对主动减载技术进行了研究,能够对高空风产生的气动载荷进行主动抑制,放宽了发射条件,对新一代捆绑运载火箭姿控系统的设计具有重要的借鉴意义.     

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.     

火箭发动机基于神经网络非线性辨识的故障检测

应用神经网络方法，提出了一种液体火箭发动机故障实时检测算法。神经网络采用非线性辨识技术贴近发动机的工作过程，并输出包合发动机故障信息的辨识误差信号。若辨识误差变大超过一定阈值，检测逻辑就预报发动机故障。在发动机启动阶段离线训练神经网络，在发动机稳态过程可以采用离线或在线学习算法。实验研究表明神经网络可以成功地应用于大型泵压式液体火箭发动机的故障检测。     

Forecasting magnetopause crossing locations by using Neural Networks

Given the highly complex and nonlinear nature of Near Earth Space processes, mathematical modeling of these processes is usually difficult or impossible. In such cases, modeling methods involving Artificial Intelligence may be employed. We demonstrate that data driven models, such as the Neural Network based approach, shows promise in its ability to forecast or predict the behavior of these processes. In this paper, modeling studies for forecasting magnetopause crossing locations are summarized and a Neural Network algorithm is presented to describe the nonlinear time-dependent response of the subsolar region of the magnetopause to varying solar wind conditions. In our approach the past history of the solar wind has, for the first time to the best knowledge of the authors, been included in forecasting the subsolar region of the magnetopause. It is proposed that the data driven approach is a valid approach to understanding and modeling the physical phenomena of Near Earth Space. The only basic requirement for the data driven approach is the availability of representative data for the phenomena. The objective of this paper is to demonstrate that by using WIND and GEOTAIL satellite data a Neural Network based model can be adapted to the modeling of the Earth’s magnetopause.     

A MLP neural network as an investigator of TEC time series to detect seismo-ionospheric anomalies

Anomaly detection is extremely important for earthquake parameters estimation. In this paper, an application of Artificial Neural Networks (ANNs) in the earthquake precursor’s domain has been developed. This study is concerned with investigating the Total Electron Content (TEC) time series by using a Multi-Layer Perceptron (MLP) neural network to detect seismo-ionospheric anomalous variations induced by the powerful Tohoku earthquake of March 11, 2011.     

基于ANN的故障诊断专家系统的应用研究

介绍一种基于ANN的设备故障智能诊断专家系统模型,探讨了该模型的基本结构和相应的推理机制及故障诊断策略,提出将设备故障分为低层故障和高层故障的思想以及相应的分层诊断、分块进行的诊断策略,并以MK9-5卷接机组为诊断对象,研究了该模型及诊断方法在设备故障智能诊断中的具体应用.     

广义回归神经网络在卫星钟差短期预报中的应用

近年来,神经网络(Neural Network,简称NN)在非线性系统的预测方面取得了广泛的应用。考虑到卫星钟差包含了复杂的非线性因素,所以将一种新型神经网络-广义回归神经网络(Generalized Regression Neural Network,GRNN)应用于钟差预报中。采用“滑动窗”方式构建样本数据以提高数据利用率,为提高网络的泛化能力,利用K重交叉验证法(K-fold Cross-Validation)对网络进行训练学习,并根据最小均方根误差(Root Mean Square Error,RMSE)确定最优平滑因子。利用国际GNSS服务(International GNSS Service,IGS)公布的精密GPS卫星钟差数据进行预报实验,并与传统二次多项式模型对比分析。结果表明：GRNN模型在24h的预报跨度内的误差可达ns级,并较多项式模型有更好的稳定性;对于线性钟差,GRNN模型要逊于多项式模型,而对于非线性钟差,GRNN模型则明显优于多项式模型,初步验证了GRNN用于钟差预报的可行性、有效性以及实用性。     

神经网络在下颌运动轨迹图测量中的应用

介绍了采用小型磁石测量下颌运动轨迹图Electrognathography(EGN)的方法。给出利用磁偶极子模型代替小磁石模型的理论及实际依据。利用神经网络解决由磁石产生的磁感应强度推定其空间参数的反演问题 ,并且采用了分割网络及修正网络的方法来提高磁石运动测量的推定准确度。     

Machine vision monitoring of plant health.

Techniques and algorithms to detect and diagnose disorders in plants grown in a controlled environment have been developed. A video camera senses features of plants which are indicative of disorders. Images are calibrated for size and color variations by using calibration templates. Different image segmentation techniques for separating object from background, have been implemented. Plant size and color properties have been investigated, temporal, spectral and spatial variation of leaves were extracted from the segmented images. Neural network and statistical classifiers were used to determine plant condition.     

风云二号星上转发器功率放大器的非线性分析与模拟

针对风云二号气象卫星星上转发器放大器的非线性特性及其带来的交调问题进行了分析。对实测给出的放大器单载波功率转移曲线的非线性数据,采用多项式分析和人工神经网络(ANN)模拟的方法进行了拟合和分析,并对所得结果作了比较。实验表明,文中提出的用ANN 概念和方法进行非线性分析与模拟,对解决非线性问题具有一定的理论意义和实际应用价值。     

Using neural networks to determine the optimum solar input for the prediction of ionospheric parameters

Neural networks (NNs) are proving to be ideal tools for modeling the behaviour of the ionosphere. The NNs are trained using a database of archived data describing the relationship between the output parameter and an input space. The input space is designed from knowledge of those variables that affect the behaviour of the output parameter. For ionospheric parameters this input space would always include a solar variable due to the strong influence that the sun has on ionospheric behaviour.