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.     

A simulation study on the mode conversion process from slow Z-mode to LO mode by the tunneling effect and variations of beaming angle

For a particular angle of incidence wave, it is possible for a slow Z-mode wave incident on an inhomogeneous plasma slab to be converted into an LO mode wave. But for another wave normal angle of the incident wave, it has been considered impossible, since an evanescence region exists between two mode branches. In this case we expect that the mode conversion takes place through the tunneling effect. We investigate the effect of the spatial scale of the density gradient on the mode conversion efficiency in an inhomogeneous plasma where the mode conversion can occur only by the tunneling effect. We use the computer simulation solving Maxwell’s equations and the motion of a cold electron fluid. By considering the steepness of the density gradient, the simulation results show the efficient mode conversion could be expected even in the case that the mismatch of the refractive indexes prevents the close coupling of plasma waves. Also, we show for these cases the beaming angle does not correspond to Jones’ formula. This effect leads to the angles larger and smaller than the angle estimated by the formula. This type of mode conversion process becomes important in a case where the different plasmas form a discontinuity at their contact boundary.     

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。     

Impact of local and global factors and meteorological parameters in temporal variation of atmospheric potential gradient

In this research work, we have performed comparative diurnal variations of atmospheric Potential Gradient (PG) of fair-weather days by using the data of three stations installed in Northern, Pakistan for the year 2018. We investigated the impact of both local and global factors and meteorological parameters in the diurnal variation of atmospheric Potential Gradient on the annual and seasonal time scale. We observed two peaks, primary and secondary. This is because of the land-based measurements of annual and seasonal variations. The annual average curve of Potential Gradient of all three stations: Islamabad (CES), Muzaffarabad (MZF), and Balakot (BKT) demonstrated a notable deviation from the standard oceanic Carnegie curve. The atmospheric Potential Gradient variations are due to numerous meteorological factors e.g., air pollution, humidity, aerosol particles, fog, and temperature. Among three stations, the MZF station is located in highland (mountainous) and it demonstrated a higher atmospheric Potential Gradient. We further differentiate the results of our three stations with global results for authenticity and observed coherence between them. In addition, a positive correlation of fair-weather Potential Gradient is observed with temperature and a notable correlation between relative humidity and atmospheric Potential Gradient for all the three observatories.     

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.     

基于神经网络和人工势场的协同博弈路径规划

协同博弈路径规划是空战自主决策、机器人体育比赛等应用场景中的重要问题，其难点在于对环境对抗性反馈的实时自适应和多智能体的相互配合。提出一种基于神经网络和人工势场的协同博弈路径规划方法，使用反向传播（BP）神经网络自适应调整人工势场函数系数，并将人工势场作为神经网络输出端的特征提取。为解决真实样本质量和数量不足的问题，基于遗传算法仿真生成样本数据用于神经网络训练，并通过滚动时域的思路面向动态博弈优化样本性能。从样本数据中提炼出距离差与航向差以反映协同和博弈特性，利用神经网络的黑盒特性和学习能力解决协同博弈问题。应用于二对一反隐身超视距空战路径规划，比经典人工势场法有明显性能提升，且计算开销可接受，计算复杂度分析表明该方法可以较好扩展到多机对抗场景。     

基于微分进化算法的二自由度PID控制器参数优化

针对二自由度PID控制器参数多、调节困难等问题,提出了一种基于微分进化算法参数调节优化方法。证明了微分进化算法能够收敛到平稳点,并具有概率全局收敛性。研究了二自由度PID控制器参数与抑制特性、跟踪特性的关系,并将微分算法与遗传算法、粒子群算法的参数优化结果进行了比较。仿真验证表明,微分算法能够更好地实现二自由度PID控制器参数的调节,具有优异的跟踪特性和抑制特性。