基于特征序列的时域STBC-OFDM盲识别算法

为有效解决STBC-OFDM信号盲识别过程中存在的低信噪比适应能力弱等问题，在OFDM块大小已知的前提下，提出了一种在时域上构造特征序列的识别算法。该算法推导了空时分组码接收信号的时域特性，以及四阶特征向量，并构造特征序列，通过检测特征序列达到识别4种STBC-OFDM信号的目的。推导和仿真结果表明:所提算法无需信道、噪声、调制方式和OFDM块起始位置等先验信息，在较低信噪比条件下也具有良好的识别性能，且对频偏、多普勒频移和时延的鲁棒性能好，计算量较低，具有较高的实用价值。      

基于能量等效原理的颤振机理及颤振导数识别

桥梁和风是相互作用的流固耦合系统,风对桥梁的作用效应可以分为阻尼效应和刚度效应。首先基于能量等效原理将Scanlan线性颤振自激力分为纯阻尼效应项H_1~*、A_2~*,纯刚度效应项A_3~*、H_4~*和既有刚度效应又有阻尼效应的双重效应项A_1~*、H_2~*、H_3~*、A_4~*。将颤振自激力进行积分运算分别求出其阻尼效应项的做功时程和刚度效应项的无功时程并从功能角度对经典耦合颤振驱动机理进行了研究,最后通过将耦合颤振微分方程转化为功能方程形式,提出了一种基于自激力瞬时做功的颤振导数识别方法并证明了该方法的可靠性。     

NASA-STD-5020航天器用螺纹紧固系统要求标准简介

介绍NASA-STD-5020《航天器用螺纹紧固系统要求》标准的适用范围、主要内容,包括总体要求、设计要求及考虑因素、螺纹紧固系统分析准则、质量保证等,对标准的实施作了说明。     

基于遗传算法与评估模型的飞行载荷实测研究

为了得到飞行载荷实测所需的最优载荷方程,建立了可表征载荷方程数学意义和物理意义的评估模型,与遗传算法(GA)结合,以载荷方程评估驱动载荷方程建立,形成一种新的载荷方程建立方法——EMGA。通过建立某机翼根部剪力方程,比较了穷尽搜索(ES)法、传统GA和EMGA所建载荷方程的评估模型参数。最后将3种方法建立的方程用于飞行载荷实测。结果发现:和其他两种方法比较,本文提出的EMGA可得到最优的载荷方程;EMGA测得的飞行载荷分散性小,最优载荷方程的测量结果可作为最终的飞行载荷。     

变体翼梢小翼的减阻机理数值模拟

总结了对翼梢小翼减阻效果影响最大的几何参数,在此基础上采用数值模拟方法研究了这些几何参数的最佳变化范围,为变体翼梢小翼设计提供理论依据.并从气动性能、气动载荷分布和翼尖涡的角度探讨了变体翼梢小翼相对传统翼梢小翼的优缺点.结果表明:在飞机的起飞阶段,变体翼梢小翼的减阻效率比传统翼梢小翼高2.2%,同时将翼尖涡强度降低了15%,有利于提高飞机的燃油效率和机场空域安全;但也会增大机翼的翼根弯矩,因此必须权衡变体翼梢小翼带来的气动收益与结构强度不利因素.     

Intelligent modeling and identification of aircraft nonlinear flight

In this paper, a new approach has been proposed to identify and model the dynamics of a highly maneuverable fighter aircraft through artificial neural networks（ANNs）. In general, aircraft flight dynamics is considered as a nonlinear and coupled system whose modeling through ANNs, unlike classical approaches, does not require any aerodynamic or propulsion information and a few flight test data seem sufficient. In this study, for identification and modeling of the aircraft dynamics, two known structures of internal and external recurrent neural networks（RNNs） and a proposed structure called hybrid combined recurrent neural network have been used and compared.In order to improve the training process, an appropriate evolutionary method has been applied to simultaneously train and optimize the parameters of ANNs. In this research, it has been shown that six ANNs each with three inputs and one output, trained by flight test data, can model the dynamic behavior of the highly maneuverable aircraft with acceptable accuracy and without any priori knowledge about the system.     

Weighted marginal fisher analysis with spatially smooth for aircraft recognition

Due to limitations to extract invariant features for recognition when the aircraft presents various poses and lacks enough samples for training, a novel algorithm called Weighted Marginal Fisher Analysis with Spatially Smooth （WMFA-SS） for extracting invariant features in aircraft rec- ognition is proposed. According to the Graph Embedding （GE） framework, Heat Kernel function is firstly introduced to characterize the interclass separability when choosing the weights of penalty graph. Furthermore, Laplacian penalty is applied to constraining the coefficients to be spatially smooth in this algorithm. Laplacian penalty is able to incorporate the prior information that neigh- boring pixels are correlated. Besides, using a Laplacian penalty can also avoid the singularity of Laplacian matrix of intrinsic graph. Once compact representations of the images are obtained, it can be considered as invariant features and then be performed in classification to recognize different patterns of aircraft. Real aircraft recognition experiments show the superiority of our proposed WMFA-SS in comparison to other GE algorithms and the current aircraft recognition algorithm; the accuracy rate of our proposed method is 90.00% for dataset BH-AIR1.0 and 99.25% for dataset BH-AIR2.0.     

Multi-view space object recognition and pose estimation based on kernel regression

The application of high-performance imaging sensors in space-based space surveillance systems makes it possible to recognize space objects and estimate their poses using vision-based methods. In this paper, we proposed a kernel regression-based method for joint multi-view space object recognition and pose estimation. We built a new simulated satellite image dataset named BUAA-SID 1.5 to test our method using different image representations. We evaluated our method for recognition-only tasks, pose estimation-only tasks, and joint recognition and pose estimation tasks. Experimental results show that our method outperforms the state-of-the-arts in space object recognition, and can recognize space objects and estimate their poses effectively and robustly against noise and lighting conditions.     

薄膜充气梁失稳载荷研究

针对薄膜充气梁在弯曲载荷下的失稳载荷问题,进行了理论及试验研究,得到充气模型失稳载荷的理论值和试验值,两者对比显示试验值高于理论值1.56倍以上,为在使用过程中充分利用充气模型的承载能力提供了有效的数据。