Ionospheric TEC disturbances over China during the strong geomagnetic storm in September 2017

From September 7 to 8, 2017, a G4-level strong geomagnetic storm occurred, which seriously impacted on the Earth’s ionosphere. In this work, the global ionospheric maps released by Chinese Academy of Sciences are used to investigate the ionospheric responses over China and its adjacent regions during the strong storm. The prominent TEC enhancements, which mainly associated with the neutral wind and eastward prompt penetration electric field, are observed at equatorial ionization anomaly crests during the main phase of the storm on 8 September 2017. Compared with those on 8 September, the TEC enhancements move to lower-latitude regions during the recovery phase on 9 September. A moderate storm occurred well before the start of the strong storm causes similar middle-latitude TEC enhancements on 7 September. However, the weak TEC depletion is observed at middle and low latitude on 9–10 September, which could be associated with the prevailing westward disturbance electric field or storm-time neural composition changes. In addition, the storm-time RMS and STD values of the ionospheric TEC grids over China increase significantly due to the major geomagnetic storm. The maximum of the RMS reaches 12.0 TECU, while the maximum of the STD reaches 8.3 TECU at ~04UT on 8 September.     

Assessment of between-floor structural and topological properties on cognitive map development in multilevel built environments

The present study investigated cognitive map development in multilevel built environments. Three experiments were conducted in complex virtual buildings to examine the effects of five between-floor structural factors that may impede the accuracy of humans’ ability to build multilevel cognitive maps. Results from Experiments 1 and 2 (of three experiments) revealed that difficulties in developing multilevel cognitive maps are not solely caused by the z-axis offset, as is suggested in the literature, but are due to the factorial combination of a between-floor overlap and a z-axis offset. Results from Experiment 2 showed that this process becomes substantially more difficult when the reference directions between different floors have an angular offset from each other. Finally, results from Experiment 3 demonstrated that confusing between-floor heading shifts in aligned buildings did not make it reliably harder to build multilevel cognitive maps. The implications of these findings are discussed in terms of theories of mental representations in multilayered three-dimensional spaces, as well as for architectural design.     

Classification of solar wind structures and intense geomagnetic storm alarms with self-organizing maps

Intense geomagnetic storms (D_st < −100 nT) usually occur when a large interplanetary duskward-electric field (with Ey > 5 mV m⁻¹) lasts for more than 3 h. In this article, a self-organizing map (SOM) neural network is used to recognize different patterns in the temporal variation of hourly averaged Ey data and to predict intense storms. The input parameters of SOM are the hourly averaged Ey data over 3 h. The output layer of the SOM has a total of 400 neurons. The hourly Ey data are calculated from solar wind data, which are provided by NSSDC OMNIWeb and ACE spacecraft and contain information on 143 intense storms and a fair number of moderate storms, weak storms and quiet periods between September 3, 1966 and June 30, 2002. Our results show that SOM is able to classify solar wind structures and therefore to give timely intense storm alarms. In our SOM, 21 neurons out of 400 are identified to be closely associated with the intense storms and they successfully predict 134 intense storms out of the 143 ones selected. In particular, there are 14 neurons for which, if one or more of them are present, the occurrence probability of intense storms is about 90%. In addition, several of these 14 neurons can predict big magnetic storm (D_st  −180 nT). In summary, our method achieves high accuracy in predicting intense geomagnetic storms and could be applied in space environment prediction.     

Multiple model predictive control of perching maneuver based on guardian maps

Considering the strong nonlinearity of Unmanned Aerial Vehicles(UAVs) resulting from high Angle of Attack(AOA) and fast maneuvering, we present a multi-model predictive control strategy for UAV maneuvering, which has a small amount of online calculation. Firstly, we divide the maneuver envelope of UAV into several sub-regions on the basis of the gap metric theory. A novel algorithm is then developed to determine the ploytopic model for each sub-region.According to this, a Robust Model Predictive...     

The nighttime anomalies using Global IGS VTEC Maps

In this paper we show the VTEC variations at night, considering their geomagnetic, seasonal and solar activity dependences. The variations are analyzed in two time periods 10 p.m. (pre-midnight) and 2 a.m. (post-midnight); and for two different solar conditions; one during high solar activity (2000) and the other during low solar activity (2008). Spatial and temporal ionosphere variability is investigated from Global IGS VTEC maps applying Principal Component Analysis (PCA).     

ATC3000航空地图制作软件及其关键技术分析

针对ATC3000空管自动化系统的运行要求以及飞行管制区现场实际工作的需要,介绍了ATC3000航空地图制作软件的关键技术。文中分析了该软件的结构以及在Windows系统中使用Delphi开发的具体实现技术。     

应用保护映射理论的高超声速飞行器自适应控制律设计

针对高超声速飞行器包线范围广、参数变化大的控制需求,应用保护映射理论提出一种高超声速飞行器的自适应控制律设计方法。首先建立整个飞行包线内的线性变参数(LPV)模型,在参数变化边界点设计一个初始的控制结构和参数,然后基于保护映射理论分析初始控制结构使闭环系统稳定的参数范围,通过迭代自动获取整个包线内满足性能指标的控制参数,进而通过多项式拟合设计出高超声速飞行器自适应控制律。所提出的方法能够根据初始控制结构自动寻找一系列满足性能要求的控制器参数,并确定这些控制参数满足闭环系统稳定的设计范围。仿真结果表明,所设计的自适应控制律能够确保高超声速飞行器大包线的设计要求,实现闭环系统的鲁棒稳定。     

基于综合设计的涡轴发动机热力循环方案研究

为了对比研究不同热力循环参数的涡轴发动机方案,建立集总体性能设计、尺寸流路设计、部件初步气动设计和重量估算的总体/部件为一体的综合设计模型,利用部件效率/气动负荷耦合设计和涡轮冷气量计算模型,实现发动机总体/部件的耦合设计。结果表明:在现有的设计技术水平下,低压比方案、高涡轮进口总温方案以及低压比和高涡轮进口总温的组合方案各具优势;高热力参数方案的设计必须以技术的进步为前提;未来涡轴发动机的总体设计将会沿着高热力循环参数和低热力循环参数两种方向发展。     

基于支持向量机和粒子群算法的压气机特性计算

改进和实现了支持向量机用于函数回归估计的算法,并将支持向量机和粒子群优化算法运用于压气机特性计算,以寻找压气机进口换算空气流量和效率随增压比、转子转速、压气机进气角度变化的非线性函数关系,将进气角度作为一个影响因子列入考虑范围,改进了以往只考虑压气机特性随转子转速和增压比变化的计算方法。用支持向量机回归估计压气机特性随各因子变化的非线性函数,用粒子群优化算法对非线性函数逼近度进行全局优化,计算结果表明,设计的算法能准确地回归估计出描述压气机特性的非线性函数,算法是有效的。      

重访机制驱动的多无人机协同动目标搜索方法

为实现多无人机高效捕获灰色任务区域内的移动目标,考虑传感器探测概率与虚警概率,提出了重访机制驱动的协同搜索规划（RMD-CSP）方法,以降低目标遗漏与误判概率。考虑无人机飞行性能约束,以最大化任务执行效能为目标建立多无人机协同搜索模型。根据目标先验信息初始化环境搜索信息图（包括目标概率分布图、环境不确定度图与环境搜索状态图）,利用无人机实时探测信息,基于贝叶斯准则持续更新搜索信息图。定制基于环境不确定度更新的重访机制,通过增加长时间未被重访区域的环境不确定度,引导无人机搜索该区域,降低移动目标的遗漏概率;定制基于目标函数权重更新的重访机制,引导无人机快速重访发现新的疑似目标的区域,对疑似目标进行再次确认,减少由于传感器虚警概率造成的目标误判概率。采用滚动时域规划架构,将搜索规划问题分解为一系列短时域规划问题,提升了求解效率。在典型任务想定下,通过数值仿真试验验证了所提方法的有效性。仿真结果表明,RMD-CSP能够在秒级时间内生成每个时域的搜索航迹,相比于光栅式搜索方法与标准的概率启发式搜索方法,能够引导无人机捕获更多的移动目标,同时减少误判次数,有效提升了多无人机协同搜索的任务效能。