基于模型重构的变形叶片配准定位技术

叶片毛坯的精确定位是叶片数控加工的核心问题,为了提高叶片数控加工质量,保证叶片各处余量分配均匀,本文以叶片进排气边的自适应数控加工中的检测定位为研究对象,提出了一种基于模型重构的配准定位方法.该方法利用偏置模型的叶片测量方式提高了测量精度,对基于重构的测量模型进行变形与定位分析,得出较优的定位参数.通过仿真试验和对比分析,论证了本文方法的正确性.     

基于模型大扭角叶片的三维矢量测量技术

针对接触式坐标测量机测量大扭角叶片截面存在余弦误差的问题，提出了基于模型的矢量测量方法。本文对此方法的测量精度及测量效率等方面进行论述，并通过实例验证了基于模型的矢量测量方法在精度与效率等方面优势，实现了此测量方法的工程化应用。     

A robust star identification algorithm with star shortlisting

A star tracker provides the most accurate attitude solution in terms of arc seconds compared to the other existing attitude sensors. When no prior attitude information is available, it operates in “Lost-In-Space (LIS)” mode. Star pattern recognition, also known as star identification algorithm, forms the most crucial part of a star tracker in the LIS mode. Recognition reliability and speed are the two most important parameters of a star pattern recognition technique. In this paper, a novel star identification algorithm with star ID shortlisting is proposed. Firstly, the star IDs are shortlisted based on worst-case patch mismatch, and later stars are identified in the image by an initial match confirmed with a running sequential angular match technique. The proposed idea is tested on 16,200 simulated star images having magnitude uncertainty, noise stars, positional deviation, and varying size of the field of view. The proposed idea is also benchmarked with the state-of-the-art star pattern recognition techniques. Finally, the real-time performance of the proposed technique is tested on the 3104 real star images captured by a star tracker SST-20S currently mounted on a satellite. The proposed technique can achieve an identification accuracy of 98% and takes only 8.2?ms for identification on real images. Simulation and real-time results depict that the proposed technique is highly robust and achieves a high speed of identification suitable for actual space applications.     

黎曼几何在计算机视觉中的应用研究进展

在计算机视觉领域，许多任务相关数据具有非欧结构，近年来基于黎曼几何的数据表征与应用研究受到了越来越多的关注。如何充分利用数据的几何结构，来提高目标识别、目标跟踪及目标检测算法的性能，是其中的一些研究热点。本文主要从三个方面介绍黎曼流形在计算机视觉中的应用研究进展。首先，从数学基本概念出发，阐述黎曼流形与图像的关系以及视觉应用的可行性，并介绍计算机视觉中具有重要应用的几种黎曼流形。其次，对黎曼流形在计算机视觉中若干常见应用进行了概述，重点介绍了与深度学习相结合的相关进展。最后，对引入黎曼流形的机器学习方法的未来发展进行了分析和讨论。     

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.     

基于星座图恢复的PSK信号调制方式盲识别

针对非协作通信条件下PSK信号识别问题,提出了一种基于星座图恢复的MPSK/MDPSK信号调制方式盲识别系统.该系统不需要信号任何先验信息,星座图恢复所需的参数如载波频率、符号速率、位定时信息均从接收序列中估计得到,同时,在系统中采用相位差分的方法以消除由载波估计偏差引起的星座图扩散.提出并定义信号隶属度函数,该函数利用星座图盲聚类得到的聚类中心的数目计算隶属度来实现MPSK和MDPSK信号调制方式识别.仿真表明,在AWGN信道条件下,当SNR＞11dB时,对所有信号的识别率大于90%,对于BPSK、4PSK信号在SNR＞6dB时识别率达到100%,证明了该算法的有效性.     

风机叶片静载荷和模态测试技术

对近期国内外在风机叶片静载荷测试和模态测试方面的研究成果、测试标准和测试机构进行了评述.     

社会网络和团队认知对团队知识转移的影响

文章整合社会网络理论和知识转移等理论,分析团队中的社会网络结构对团队认知、团队知识转移的影响,结果表明社会网络结构有助于团队认知的形成和团队知识转移。因此,应该充分利用团队内部的社会网络关系,增强团队成员的团队认知,促进团队的知识转移,提升企业绩效。     

基于仿射不变矩特征的舰船图像识别方法

获取舰船图像会受到视角影响,产生仿射变换。文章研究了矩特征的平移、旋转、尺度不变性和仿射变换的性质,进一步构造了仿射不变矩。首先,提取舰船图像的封闭轮廓;然后,计算仿射不变矩作为识别的特征库。通过计算待识别图像仿射不变矩与特征库特征的相关系数进行舰船类型识别,通过仿真数据证明仿射不变矩具有良好的识别性,最大相对误差小于...