Observations of non-migrating tides and ionospheric perturbations of O(D) airglow by ISUAL instrument

In this paper, we present the spatial variations of O(¹D) airglow observed by the ISUAL (Imager of Sprites and Upper Atmospheric Lightning) instrument on board the FORMOSAT-2 satellite. With a CCD camera and a 630 nm filter, ISUAL can measure global atmospheric emissions lying between the heights of 80 and 300 km. In days of 3–6 September 2008 and 25–27 February 2009, ISUAL has measured the emissions of O(¹D) airglow with results showing strong longitudinal peak-3 and peak-4 structures. The Lomb-Scargle analyses for these two cases show periods of longitudes of 120° and 90° supporting the DE2 and DE3 non-migrating tides. The 630 nm emissions are enhanced in equatorial regions and are lying along the equator. Over Africa its intensity can sometimes increase up to 80% relative to other longitudes. The perturbation is so strong that non-migrating tides are erased. A case of bimodal distribution with strong emissions at latitudes in equator and mid-latitude in geographic coordinates was observed.     

Tropospheric wet refractivity tomography using multiplicative algebraic reconstruction technique

Algebraic reconstruction techniques (ART) have been successfully used to reconstruct the total electron content (TEC) of the ionosphere and in recent years be tentatively used in tropospheric wet refractivity and water vapor tomography in the ground-based GNSS technology. The previous research on ART used in tropospheric water vapor tomography focused on the convergence and relaxation parameters for various algebraic reconstruction techniques and rarely discussed the impact of Gaussian constraints and initial field on the iteration results. The existing accuracy evaluation parameters calculated from slant wet delay can only evaluate the resultant precision of the voxels penetrated by slant paths and cannot evaluate that of the voxels not penetrated by any slant path. The paper proposes two new statistical parameters Bias and RMS, calculated from wet refractivity of the total voxels, to improve the deficiencies of existing evaluation parameters and then discusses the effect of the Gaussian constraints and initial field on the convergence and tomography results in multiplicative algebraic reconstruction technique (MART) to reconstruct the 4D tropospheric wet refractivity field using simulation method.     

Traveling ionospheric disturbances observed at South African midlatitudes during the 29–31 October 2003 geomagnetically disturbed period

This paper presents traveling ionospheric disturbances (TIDs) observations from GPS measurements over the South African region during the geomagnetically disturbed period of 29–31 October 2003. Two receiver arrays, which were along two distinct longitudinal sectors of about 18°-20° and 27°-28° were used in order to investigate the amplitude, periods and virtual propagation characteristics of the storm induced ionospheric disturbances. The study revealed a large sudden TEC increase on 28 October 2003, the day before the first of the two major storms studied here, that was recorded simultaneously by all the receivers used. This pre-storm enhancement was linked to an X-class solar flare, auroral/magnetospheric activities and vertical plasma drift, based on the behaviour of the geomagnetic storm and auroral indices as well as strong equatorial electrojet. Diurnal trends of the TEC and foF2 measurements revealed that the geomagnetic storm caused a negative ionospheric storm; these parameters were depleted between 29 and 31 October 2003. Large scale traveling ionospheric disturbances were observed on the days of the geomagnetic storms (29 and 31 October 2003), using line-of-sight vertical TEC (vTEC) measurements from individual satellites. Amplitude and dominant periods of these structures varied between 0.08–2.16 TECU, and 1.07–2.13 h respectively. The wave structures were observed to propagate towards the equator with velocities between 587.04 and 1635.09 m/s.     

基于贝叶斯模型平均的航空发动机可靠性分析

针对不同失效模式的特点,利用航空发动机状态监测信息,分别采用Gamma过程、Wiener过程和Weibull分布建立可靠性分析模型.采用贝叶斯模型平均（BMA）方法,分析不同失效模式对可靠性影响,对航空发动机可靠性进行集成分析.通过算例跟踪某在翼航空发动机可靠性水平,基于贝叶斯模型平均的航空发动机可靠性模型的计算误差率低于单一可靠性分析模型,计算结果误差率低于1%.说明多模型集成可靠性技术在复杂系统运行可靠性分析中的有效性和准确性.      

基于视频数据的机场跑道外来物检测

提出了一种基于视频数据的机场跑道外来物(FOD,Foreign Object Debris)检测算法,该算法包括几何校正、背景差分、杂波抑制和伪装消除等4个步骤.其中,几何校正排除了摄像头轻微抖动造成的图像差异;背景差分利用每个像素对应的颜色向量信息,建立背景模型并进行定时更新;杂波抑制和伪装消除利用差分图像和原始图像信息,建立马尔科夫随机场和概率统计模型,在降低虚警的同时提高了检测率.将本算法应用于两组不同光照条件下获取的视频图像,检测不同形状和颜色的FOD目标;采用本算法对机场实测数据进行测试,验证了算法的有效性.     

空间高阶差分近似对三维ADI-FDTD数值色散的影响

研究采用空间高阶差分近似的三维交替方向隐式时域有限差分法(ADI-FDTD,Alternating-Direction Implicit Finite-Difference Time-Domain)数值色散问题,首先推导了采用空间高阶差分近似的数值色散迭代公式,分别对最小相速方向和最大相速方向进行二阶、四阶、六阶、十阶空间差分数值色散误差的数值计算和比较,并分析数值色散对空间差分阶数的依赖关系,最后发现在均匀网格中采用四阶空间差分能得到较好的效果.     

DDN状态监控系统的设计实现

设计实现了一个用于解决DDN（Digital Data Net）网络管理不足之处的新系统——DDN状态监控系统．详细论述了该系统的设寸思路和软件结构,采用了数据库、网络轮询等一系列有效的技术手段来解决DDN网络管理的不足,指出了该系统的技术创新点。     

利用神经元拓展正则极端学习机预测时间序列

为实现对于时间序列预测数据的准确预测,提出一种神经元拓展正则极端学习机(NERELM,Neuron-Expanding Regularized Extreme Learning Machine),并研究了其在时间序列预测中的应用.NERELM根据结构风险最小化原理权衡经验风险与结构风险,以逐次拓展隐层神经元的方式自动确定最佳的网络结构,以避免传统神经网络训练过程中需人为确定网络结构的弊端.应用于时间序列的仿真结果表明:NERELM可有效实现对于RELM最佳网络结构的自动确定,具有预测精度高与计算速度快的优点.      

基于XML的EtherCAT工业以太网协议解析技术

重点研究了基于可扩展标记语言(XML,eXtensible Markup Language)的EtherCAT工业以太网协议解析器技术和实现方法.介绍了EtherCAT协议报文格式和特点,分析了以太网数据侦听器和协议解析器的结构,针对通用以太网解析器在工业以太网领域应用的局限性,提出使用XML语言描述协议报文的方法,在此基础上开发出一种开放式、可重构的EtherCAT协议解析器.实验结果表明基于XML的协议报文描述方法及解析器能够有效地描述和解析EtherCAT协议报文,使用者可以根据解析对象格式使用XML语言自定义、修改和扩展协议解析规则,获得清晰、直观的解析结果.      

LEVEL DECISION-MAKING FOR AERO-ENGINE MODULES BASED ON INCREMENTAL KNOWLEDGE LEARNING

The maintenance of an aero-engine usually includes three levels, and the maintenance cost and period greatly differ depending on the different maintenance levels. To plan a reasonable maintenance budget program, airlines would like to predict the maintenance level of aero-engine before repairing in terms of performance parame- ters, which can provide more economic benefits. The maintenance level decision rules are mined using the histori- cal maintenance data of a civil aero-engine based on the rough set theory, and a variety of possible models of upda- ting rules produced by newly increased maintenance cases added to the historical maintenance case database are in- vestigated by the means of incremental machine learning. The continuously updated rules can provide reasonable guidance suggestions for engineers and decision support for planning a maintenance budget program before repai- ring. The results of an example show that the decision rules become more typical and robust, and they are more accurate to predict the maintenance level of an aero-engine module as the maintenance data increase, which illus- trates the feasibility of the represented method.