船岸网络时延测量误差原因分析

简单介绍了测量船船岸通信网络组成,重点介绍了NTP（网络时间协议）和PTP（精密时间协议）这2种网络时间同步协议。针对改建后的船岸网络时延测量误差进行分析和研究,提出基于TWSTFT（卫星双向时间频率传输）技术模型的网络时延测量方法,测量结果误差小于3ms,满足测控任务实时性要求,为提高测控网的测控精度提供有益参考。     

面向深空通信的分布式系统Raptor码传输机制

为缓解深空探测器与地面站之间的超远距离和星体遮挡,而导致巨大的信号衰减以及链路频繁中断的问题,基于容迟/容断网络（DTN）协议框架,利用轨道器进行多跳中继组网传输,设计了多个探测器经过轨道器向地面传输场景下的分布式系统Raptor编码传输方案（DSRC）。提出了联合译码简化方案,理论分析推导了DSRC方案及改进方案的性能参数,并与现有分布式无速率纠删方案进行仿真比较,在译码冗余为5%时达到了0.01的译码失败率。     

安全可靠高性能的无线通信网络设计

作为支持物联网发展的基础网络层之一,无线网络已无处不在.然而,用户经常会遇到由于不当的设计带来的网络性能问题.这些问题可以归结到无线网络的固有特性以及电磁波的物理性质.然而,适当的无线网络设计可以减轻这些影响.本文将对这些设计考量进行介绍,还将对无线网络安全方面的问题加以讨论     

利用人工神经网络提前1h预报电离层TEC

提出了一种利用人工神经网络提前1h预报电离层TEC的简便方法. 考虑到实际工程应用要求, 没有使用其他空间天气参数, 而是选择电离层TEC观测数据本身作为输入参数. 输入参数为当前时刻TEC、一阶差分、相对差分和时间, 输出参数为预报时刻TEC. 利用文中介绍的GPS/TEC处理方法解算厦门站2004年电离层TEC观测数据, 对预报方法进行评估, 全年平均相对误差为9.3744%, 预报结果与观测值相关性达到了0.96678. 结果表明, 利用人工神经网络方法提前1h预报电离层TEC有很好的应用前景.      

复杂产品系统的风险管理实现研究

提出了一套跨研制单位持续项目风险管理机制.在其风险因素识别阶段.提出了以核对表和鱼刺图为基础的基于特征一因子法的风险因素识别法;在风险量化阶段,提出了先基于模糊综合评判进行单个风险量化、后基于改进PERT方法进行风险整体量化的实现方法:在风险应对阶段,提出了基于人工神经网络和框架知识表示及推理相结合的风险应对方法,建立了混合型专家系统.以实现风险应对智能化.最后,以具体实例说明了该项技术的可行性.     

基于小波矩特征和神经网络的灰度图像识别算法研究

小波矩结合了矩特征和小波特征,既反映了图像的全局性信息,又反映了图像的局域性信息,并且具有旋转、平移和缩放不变性。文章研究了基于小波矩的灰度图像特征提取算法,并将其与BP神经网络组合,利用神经网络的强学习能力和容错性,形成一个灰度图像识别系统。仿真实验表明,在图像失真较小的情况下识别率可达到100％,较之未提取特征的神经网络识别方法而言,网络收敛速度与识别精度都有较大的提高。     

Storm-time atmospheric density modeling using neural networks and its application in orbit propagation

Upper atmospheric densities during geomagnetic storms are usually poorly estimated due to a lack of clear understanding of coupling mechanisms between the thermosphere and magnetosphere. Consequently, the orbit determination and propagation for low-Earth-orbit objects during geomagnetic storms have large uncertainties. Artificial neural networks are often used to identify nonlinear systems in the absence of rigorous theory. In the present study, an attempt has been made to model the storm-time atmospheric density using neural networks. Considering the debate over the representative of geomagnetic storm effect, i.e. the geomagnetic indices ap and Dst, three neural network models (NNM) are developed with ap, Dst and a combination of ap and Dst respectively. The density data used for training the NNMs are derived from the measurements of the satellites CHAMP and GRACE. The NNMs are evaluated by looking at: (a) the mean residuals and the standard deviations with respect to the density data that are not used in training process, and (b) the accuracy of reconstructing the orbits of selected objects during storms employing each model. This empirical modeling technique and the comparisons with the models NRLMSIS-00 and Jacchia-Bowman 2008 reveal (1) the capability of neural networks to model the relationship between solar and geomagnetic activities, and density variations; and (2) the merits and demerits of ap and Dst when it comes to characterizing density variations during storms.     

空间碎片材料检测技术研究

为了对空间碎片材料的成分进行快速检测,分析了空间碎片的来源和构成,并对空间碎片主要成分的国内外检测技术手段进行了详细对比,采用了一种基于LIBS的网络化空间碎片检测方案。该方案具有分析速度快、测量精度和灵敏度高、可多元素同时测量、空间适应性好且无需样品预处理等特点,同时可实现检测流程全自动化和多线程化。     

Large-eddy simulation and linear acoustic modeling of entropy mode oscillations in a model combustor with coolant injection

Lean-burn combustor is particularly susceptible to combustion instability and the unsteady heat release is usually considered as the excitation of the self-maintained thermo-acoustic oscillations. The transverse coolant injection is widely used to reduce the temperature of burnt gas, but on the other hand, it will introduce temperature fluctuation inside the combustor. Therefore, it is necessary to consider the influence of the coolant injection on combustion instability, and evaluate its dynamic feature. In this paper, Large-Eddy Simulation (LES) of the self-excited pressure oscillations in a model combustor with coolant injection is carried out. The analysis of transient flow characteristics and the identification of the pressure modes confirm that one of the low frequency pressure oscillations is related to entropy fluctuations, which is known as rumble combustion instability. The LES results show that transient coolant injection is another excitation of temperature fluctuation other than unsteady combustion. The amplitude of the entropy mode oscillation increases with increasing coolant air mass whereas the change of its frequency is insignificant. According to the major feature of entropy wave oscillation caused by coolant injection, a compact coolant injection model is proposed and applied in the One Dimensional (1D) Acoustic Network Method (ANM). Key correlations used in the model match well with LES data in low frequency range. This means that the coolant injection model is a complex one reflecting the interaction of the fluctuating coolant mass, pressure and temperature. Finally, the combustion instability frequencies and modes predicted by acoustic network method are also in good agreement with LES results.     

地球观测网络成像任务可调度性预测方法

为了能够快速、合理地分配成像任务,充分发挥对地观测网络的观测效能,对成像任务可调度性预测问题进行了研究,提出一种由协同任务分配组件、任务调度组件、特征提取组件以及任务可调度性预测组件所构成的组件化求解架构。在成像卫星经典调度模型的基础上,提取成像任务特征,并采用变隐含层节点的反向传播（BP）神经网络集成技术求解成像任务可调度性问题。仿真结果表明,集成BP神经网络的平均预测准确度可以达到85%以上。