基于FDG和GA的故障诊断技术在电子对抗装备中的应用

探讨了模糊有向图（Fuzzy Directed Graph,FDG）和遗传算法（Genetic Algorithm,GA）相结合的故障诊断技术及其在电子对抗装备故障诊断中的具体应用。根据电子对抗装备组件的实际状态,应用模糊理论得出各组件发生故障的可能性,建立电子对抗装备的模糊有向图,利用遗传算法搜索出模糊有向图中可能的故障传播路径。实践表明,该技术应用在电子对抗装备的故障诊断中是行之有效的,取得了较好的诊断结果。     

IMPROVEMENT OF PHASE GRADIENT AUTOFOCUS ALGORITHM

ＩＭＰＲＯＶＥＭＥＮＴＯＦＰＨＡＳＥＧＲＡＤＩＥＮＴＡＵＴＯＦＯＣＵＳＡＬＧＯＲＩＴＨＭＬｉＬｉｗｅｉ（李立伟），ＭａｏＳｈｉｙｉ（毛士艺），ＡｓｉｆＲａｚａ，ＬｉｕＹｉｎｇ（刘莹）（ＤｅｐａｒｔｍｅｎｔｏｆＥｌｅｃｔｒｏｎｉｃＥｎｇｉｎｅｅｒｉｎｇ...     

平面波导器件封装工艺软件的研究与开发

平面波导器件的耦合封装是制约其发展的一个瓶颈,通过研究平面波导器件的封装工艺特点,分析了封装过程对控制系统的要求.基于Visual Basic.Net开发了封装工艺软件,研究了手动调整、视觉引导粗对准、自动化对准以及点胶固化四个模块的设计方法,并对编程过程中的关键技术进行了讨论.     

Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Satellite data, taken from the National Oceanic and Atmospheric Administration (NOAA) have been proposed and used for the detection and the cartography of vegetation cover in North Africa. The data used were acquired at the Analysis and Application of Radiation Laboratory (LAAR) from the Advanced Very High Resolution Radiometer (AVHRR) sensor of 1 km spatial resolution. The Spectral Angle Mapper Algorithm (SAM) is used for the classification of many studies using high resolution satellite data. In the present paper, we propose to apply the SAM algorithm to the moderate resolution of the NOAA AVHRR sensor data for classifying the vegetation cover. This study allows also exploiting other classification methods for the low resolution. First, the normalized difference vegetation index (NDVI) is extracted from two channels 1 and 2 of the AVHRR sensor. In order to obtain an initial density representation of vegetal formation distribution, a methodology, based on the combination between the threshold method and the decision tree, is used. This combination is carried out due to the lack of accurate data related to the thresholds that delimit each class. In a second time, and based on spectral behavior, a vegetation cover map is developed using SAM algorithm. Finally, with the use of low resolution satellite images (NOAA AVHRR) and with only two channels, it is possible to identify the most dominant species in North Africa such as: forests of the Liege oaks, other forests, cereal’s cultivation, steppes and bar soil.     

基于Kriging的多目标遗传算法

为了提高多目标优化问题的求解效率,提出了一种新的处理约束多目标优化问题的基于Kriging的多目标遗传算法(MOKGA)。MOKGA采用物理规划法将多目标优化转化为单目标优化,然后构建目标函数的考虑约束的EI(Expected Improvement)模型,并采用遗传算法进行求解。六峰值驼背函数和一个导弹多目标多学科设计优化问题用于MOKGA算法性能的测试。结果表明,与理论解相比,MOKGA算法有很好的优化结果;与NSGA II相比,MOKGA有很快的收敛性。     

航空电子企业中数据挖掘技术的应用

以Oracle数据库为源头在微软SQL Server Analysis Services平台上通过建立数据挖掘模型并在航空电子企业中得到实践与应用。     

本地标准时间频率的产生与保持

近年来，地方协调时UTC(BIRM)守时能力稳步提升，截至目前，UTC(BIRM)与UTC的相对频率偏差优于3E-14，时间偏差优于20ns。本文概括介绍了守时系统的组成，本地频率驾驭与主备同步技术，以及TA(BIRM)算法研究等。     

A multi-criteria fusion feature selection algorithm for fault diagnosis of helicopter planetary gear train

Planetary gear train is a prominent component of helicopter transmission system and its health is of great significance for the flight safety of the helicopter. During health condition monitoring, the selection of a fault sensitive feature subset is meaningful for fault diagnosis of helicopter planetary gear train. According to actual situation, this paper proposed a multi-criteria fusion feature selection algorithm (MCFFSA) to identify an optimal feature subset from the high-dimensional original feature space. In MCFFSA, a fault feature set of multiple domains, including time domain, frequency domain and wavelet domain, is first extracted from the raw vibration dataset. Four targeted criteria are then fused by multi-objective evolutionary algorithm based on decomposition (MOEA/D) to find Proto-efficient subsets, wherein two criteria for measuring diagnostic performance are assessed by sparse Bayesian extreme learning machine (SBELM). Further, F-measure is adopted to identify the optimal feature subset, which was employed for subsequent fault diagnosis. The effectiveness of MCFFSA is validated through six fault recognition datasets from a real helicopter transmission platform. The experimental results illustrate the superiority of combination of MOEA/D and SBELM in MCFFSA, and comparative analysis demonstrates that the optimal feature subset provided by MCFFSA can achieve a better diagnosis performance than other algorithms.     

Optimal mission planning of the reconfiguration process of satellite constellations through orbital maneuvers: A novel technical framework

In this paper, a general new methodology is presented for the orbital reconfiguration of satellite constellations on the basis of Lambert targeting theorem. In view of the cost and risk reduction, it is very important to consider the problem of satellite constellation reconfiguration with the two constraints of overall mission cost minimization and the desired final configuration. Hence, the dependent non-simultaneous deployment approach is proposed to minimize overall fuel cost. Despite the fact that the satellites deploy in a non-simultaneous manner, supplementary phasing maneuvers on the target orbital pattern to achieve the desired orbital configuration are avoided. Moreover, a novel idea is presented to optimize the flight of satellites, which plays an important role in complying with the constraint of overall fuel cost minimization as much as possible. In order to achieve the global optimal solution of the satellite constellation reconfiguration problem, the efficient hybrid Particle Swarm Optimization/Genetic Algorithm (PSO/GA) technique, is implemented. Finally, to indicate the superiority of the presented method, a comparison to the simultaneous maneuver viewpoint is made on a number of representative cases. The obtained results imply significant reduction of reconfiguration costs by employing the proposed method.