A new inversion method of plasma density distribution of plasmasphere in the geomagnetic equatorial plane from IMAGE data

The plasma density distribution of plasmasphere in the geomagnetic equatorial plane can help us study the magnetosphere like plasmasphere, ionosphere and their kinetics. In this paper, we introduce a new inversion method, GE-ART, to calculate the plasma density distribution in the geomagnetic equatorial plane from the Extreme Ultraviolet (EUV) data of IMAGE satellite under the assumption that the plasma density is constant along each geomagnetic field line. The new GE-ART algorithm was derived from the traditional Algebraic Reconstruction Techniques (ART) in Computed Tomography (CT) which was different from the several existing methods. In this new method, each value of the EUV image data was back-projected evenly to the geomagnetic field lines intersected by this EUV sight. A 3-D inversion matrix was produced by the contributions of all the voxels contained in the plasmasphere covered by the EUV sensor. That is, we considered that each value of the EUV image data was relative to the plasma densities of all the voxels passed through by the corresponding EUV radiation, which is the biggest difference to all the existing inversion methods. Finally, the GE-ART algorithm was evaluated by the real EUV data from the IMAGE satellite.     

XP项目迭代工作量及相关度量

在使用极限编程(XP,Extreme Programming)方法的软件开发项目中,我们可以使用用户story点来度量项目的规模,这些体现了用户需求的story是以一定的效率被逐次迭代完成的,迭代结束时形成一个完整的软件产品。本文提供一种基于用户story的迭代开发阶段工作量(IE)及任务效率(TE)等指标的计算方法。用一组数据对该方法进行了模拟验证。     

特大展弦比机翼的特性与设计

大展弦比机翼有明显的升/阻(L/D)优势。但随着机翼展弦比的增大,在机翼结构重量控制条件下,机翼成柔性机翼,飞行中机翼的弹性变形量随速压(1/2ρV2)增大而增大,出现了严重的气动/结构耦合的问题。本文阐述了耦合问题的成因和解决途径,为特大展弦比机翼飞机(长航时无人机)的设计提供参考。     

基于先验知识及其定量评估的自适应杂波抑制研究

 为了改善训练样本数受限的非均匀杂波环境中的系统检测性能,研究了基于先验知识及其定量评估的自适应杂波抑制算法。提出了使用经真实杂波信息白化后的先验杂波协方差矩阵与单位矩阵之差的谱范数,来定量评估杂波先验知识的准确程度,并给出了真实杂波协方差矩阵未知时的矩阵谱范数估计方法。结合先验知识定量评估结果,获得了具有先验知识约束时的杂波协方差矩阵最大似然估计方法。分别基于多脉冲相参雷达以及空时自适应雷达进行了杂波建模,在此基础之上分析了算法性能。仿真结果证实了该算法优于使用样本协方差矩阵及先验杂波信息形成杂波抑制权值的性能。     

基于极限学习机的修正当前统计模型跟踪算法

针对当前统计(Current Statistical,CS)模型由于先验知识的缺乏和其结构特点导致的跟踪精度降低的问题,通过使用极限学习机(Extreme Learning Machine,ELM)并根据目标历史状态信息,实时估计并修正CS模型的加速度估计值,提出了基于ELM的修正CS模型跟踪算法.ELM的训练结果表明...     

基于小生境遗传算法的分布式OS-CFAR检测系统优化与性能分析

 利用小生境遗传算法,对不同检测窗长度和检测信噪比的三传感器分布式 OS-CFAR检测系统进行了优化设计,给出了一组针对不同检测环境与融合方式的搜索结果。分析表明,对于非一致环境下分布式 OS-CFAR检测系统,小生境遗传算法是一种良好的优化算法。利用搜索结果,研究了不同融合方式下环境变化对分布式 OS-CFAR检测系统的性能影响,结果表明,“或”融合对检测环境的非一致变化具有较强的鲁棒性,而“3选2”融合和“与”融合对检测环境的变化比较敏感。     

环境因素对三号航空有机玻璃应力-溶剂银纹的影响

观测了三号航空有机玻璃 ( YB-3)在不同溶剂作用下的应力-溶剂银纹临界应力σc和银纹形貌。研究了吸水、紫外光辐照等单一环境因素和多个因素循环作用对 σc和银纹形貌的影响,并对影响机理进行了讨论     

载人航天中的微重力燃烧研究

简要阐述了载人航天中防火安全的重要性，列举了美国型号研制中的几次着火事故。介绍了载人航天中防火安全问题的处理原则。回顾了国外开展微重力环境中燃烧研究的发展。对微重力条件下可燃性、液滴燃烧、层流气体喷流扩散火焰以及沿固体面火焰的传播研究予以介绍。讨论了开展微重力燃烧研究的内容及途径。     

Cooperative formation control of multiple aerial vehicles based on guidance route in a complex task environment

In recent years, formation control of multi-agent has been a significant research subject in the field of cooperative control. However, previous works have mainly concentrated on formation control for simple point-mass model and linear model. In contrast, this paper presents a novel cooperative algorithm for multiple air vehicles formation control, which aims to devise a control strategy based on guidance route to achieve precisely coordinated formation control for a group of fixed-wing aircraft in a complex task environment. The proposed method introduces the leader-follower structure for effective organization of the multi-agent coordination. Moreover, the Partial Integrated Formation and Control (PIFC) is adopted to design the control law for Guidance-Route based Formation Control (GRFC). Additionally, the proposed approach designs two guidance-route generation strategies for two special situations to demonstrate the effectiveness of GRFC in complex task environments. Theoretical analysis reveals that the proposed control protocol for guidance command can ensure the overall stability and tracking accuracy of the system. Numerical simulations are performed to illustrate the theoretical results, and verify that the proposed approach can achieve coordinated formation control precisely in a complex task environment.     

Modeling M(3000)F2 based on extreme learning machine

This paper presents an novel extreme learning machine (ELM)-based prediction model for the ionospheric propagation factor M(3000)F2 at Darwin station (12.4°S, 131.5°E; −44.5°dip) in Australia. The proposed ELM model is trained with hourly daily values of M(3000)F2 from the period 1998–2014 except 2001 and 2009. The hourly daily values of 2001 (high solar activity) and 2009 (low solar activity) are used for validating the prediction accuracy. The proposed ELM for modeling M(3000)F2 can achieve faster training process and similar testing accuracy compared with backward propagation neural network (BPNN). In addition, the performance of the ELM is verified by comparing the predicted values of M(3000)F2 with observed values and the international reference ionosphere (IRI −2016) model predicted values. Based on the error differences (the root mean square error (RMSE) and the M(3000)F2 percentage improvement values M(3000)F2_IMP(%)), the result demonstrates the effectiveness of the ELM model compared with the IRI-2016 model at hourly, daily, monthly, and yearly in high (2001) and low (2009) solar activity years. The ELM also shows good agreement with observations compared with the IRI during disturbed magnetic activity.