Corrosion and aging of organic aviation coatings: A review

Organic anticorrosive aviation coatings are an effective guarantee for aviation structure,since aircraft corrosion can lead to great economic losses. Whether it is during ground parking or air cruises, organic aviation coatings are important barriers to the corrosion of aviation structure.With the vigorous development of the aviation industry, organic aviation coatings continue to meet the challenges of diverse, complex, and harsh service environments. This review analyzes and summarizes the res...     

Review of control and guidance technology on hypersonic vehicle

Air-breathing hypersonic vehicle has great military and potential economic value due to its characteristics: high velocity, long range, quick response. Therefore, the development of hypersonic vehicle and its guidance and control technology are reviewed in this paper. Firstly, the development and classification of hypersonic vehicles around the world are summarized, and the geometric configuration and mission profile of typical air-breathing hypersonic vehicle are given.Secondly, the control dif...     

基于多维特征的战场实体目标分群方法

目标分群问题也称为群形成过程,是态势估计中的一个重点和难点问题,属于信息融合中的高层融合范畴。现有的目标分群方法大多是将空间位置相近的目标聚为一类,极少有分群方法考虑到进行战场态势分析所需的位置、速度、航向、属性和类型等多维特征。为此,首先根据海战场态势分析需求,基于目标的特征属性定义了多维度欧式距离;然后综合利用分类与密度聚类两种分析方法,提出了一种战场实体目标分群方法,以综合实现战场实体目标的实时分群处理;最后,将该算法应用于一个军用场景的数据集上进行仿真实验。仿真结果表明了该方法在海战场环境中实现目标分群处理的可行性和有效性。     

我国人口受教育程度分类模型

模糊聚类分析是研究将事物进行分类的科学,其目的是利用一定的数学方法将研究的样品或变量归并为若干类,使得每一类中的所有个体之间具有密切的关系,而不同类之间个体关系相对比较疏远。本文用模糊聚类方法将我国人口受教育程度分成了四类的模式,按该模式可以对其他省市进行了预测分类。     

基于小波阈值降噪和经验模态分解的高光谱图像分类算法

为了充分降低高光谱图像中的噪声以获得高精度的分类结果,本文结合小波阈值降噪(WTD)和经验模态分解(EMD)的优点,提出了一种基于小波阈值降噪-经验模态分解的高精度支持向量机(SVM)高光谱图像分类算法(WTD-EMD-SVM)。首先对高光谱图像进行小波阈值降噪,除去高光谱数据中的高频噪声;然后再对高光谱图像进行EMD,获得含有高光谱数据本质特征的内固模态函数(IMF)和含有低频噪声的残差;最后采用内固模态函数重构高光谱图像,并对高光谱图像进行SVM分类。将其应用到AVIRIS数据92AV3C,仿真结果表明该算法不仅提高了高光谱图像分类精度,同时可减少支持向量数目,以提高高光谱图像分类速度。     

基于对称Holder系数的雷达辐射源信号特征分析

文章基于Holder系数,提出了一种改进的雷达辐射源信号脉内特征提取方法,称之为对称Holder系数法。给出了对称Holder系数的定义,描述了基于对称Holder系数的特征提取算法。可以看出对称Holder系数法包含了相像系数法,后者只是前者的一种特例,且该方法相对于Holder系数法,其复杂度将减少一半。文中选择了6种典型的雷达辐射源信号进行特征提取和分类识别实验。实验结果表明,对称Holder系数在取不同类型时均获得了很好的正确识别率,并且当对称Holder系数所选取的类型不为相像系数时,可以获得更高正确识别率。     

三种类型的质子耀斑

根据SMM卫星观测资料,分析了GRL,PE和GRL/PE三种类型质子耀斑在硬X射线辐射特征上的差异。结果表明,存在的差异与粒子加速区(或作用区)分别处于不同的日面高度有关。      

增材制造（3D打印）分类及研究进展

增材制造(3D打印)近年来被国内外广泛研究和应用,但是目前尚无关于增材制造的系统、清晰和准确的分类。根据文献调研和现场调研,将增材制造技术分别按照制造材料种类、形态、热源、工艺组合等方法来进行划分,即增材制造技术可分为四大类16个小类,并且分别介绍各类增材制造技术原理、特点及其研究应用现状。最后指出目前增材制造材料单一与效率低等不足及向多元化、高效化、稳定化和包容化等发展的趋势。     

An unsupervised classification method of flight states for hypersonic targets based on hyperspectral features

In response to the challenges of aerospace defense caused by the rapid development of hypersonic targets in recent years, the research on the unsupervised classification of flight states for hypersonic targets is carried out in this paper, which is based on the Hyperspectral Features (HFs) of hypersonic targets covered with plasma sheath during high-speed flight. First, a new concept of the super node is defined to improve classification accuracy by alleviating the intraclass variability of HFs. Then, the frequency domain information of the curve of HFs is utilized to reduce the feature redundancy according to the prior theoretical knowledge that the fluctuation characteristics of HFs of the same flight states are similar. Finally, an unsupervised classification method based on the Density Peak Clustering (DPC) for HFs is designed to class flight states after eliminating the impact of intraclass variability and feature dimension redundancy. The proposal is compared with the traditional classification algorithms on simulated hyperspectral data sets of typical flight states of the hypersonic vehicle and an actual-observation hyperspectral data set. The results indicate that the performance of our proposal has competitive advantages in terms of Overall Accuracy (OA), Average Accuracy (AA) and Kappa coefficient.     

Supervised learning with probability interpretation in airfoil transition judgment

Transition prediction has always been a frontier issue in the field of aerodynamics. A supervised learning model with probability interpretation for transition judgment based on experimental data was developed in this paper. It solved the shortcomings of the point detection method in the experiment, that which was often only one transition point could be obtained, and comparison of multi-point data was necessary. First, the Variable-Interval Time Average (VITA) method was used to transform the fluctuating pressure signal measured on the airfoil surface into a sequence of states which was described by Markov chain model. Second, a feature vector consisting of one-step transition matrix and its stationary distribution was extracted. Then, the Hidden Markov Model (HMM) was used to pre-classify the feature vectors marked using the traditional Root Mean Square (RMS) criteria. Finally, a classification model with probability interpretation was established, and the cross-validation method was used for model validation. The research results show that the developed model is effective and reliable, and it has strong Reynolds number generalization ability. The developed model was theoretically analyzed in depth, and the effect of parameters on the model was studied in detail. Compared with the traditional RMS criterion, a reasonable transition zone can be obtained using the developed classification model. In addition, the developed model does not require comparison of multi-point data. The developed supervised learning model provides new ideas for the transition detection in flight experiments and other experiments.