装备研制项目的模糊挣值管理方法

由于装备研制项目广泛存在着技术风险,其预算成本难以用确定数描述,而现有挣值管理技术通常用确定数描述项目费用基线,忽视了项目的技术风险因素。为此,将技术风险引入挣值管理(EVM)方法,提出了一种装备研制项目的模糊挣值管理(FEVM)改进方法。该方法充分考虑了装备研制中的技术风险问题,利用技术成熟度(TRL)对项目计划成本进行估算,在此基础上,利用模糊数学理论,给出了一种基于可能度的高风险项目的成本和进度的模糊评价标准,并对项目的完工成本(ECC)和完工时间(ECT)进行了模糊预测。最后,通过案例对文中的方法进行了阐述。该方法针对装备研制项目技术风险大的特点,进一步拓展了挣值管理的适用性,对于决策者控制风险、保证项目的费用和进度具有一定指导意义和参考价值。     

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。     

基于编辑距离相似度的文本校验技术研究与应用

树形结构的文本配置在分布式的测控数据处理软件中使用广泛,它的正确性对数据处理而言至关重要。为了实现树形结构的文本配置自动检查和纠错,通过引入LD(Levenshtein Distance)编辑距离算法,把字符串的编辑操作推广到多叉树之间。在此基础上定义了多叉树之间的编辑距离,建立了衡量多叉树之间相似度的方法,设计了基于模糊匹配的文本配置自动校对流程,解决了精确匹配时由字符的多义性导致的查全率失真和误判的问题。根据实验结果,查全率和查准率分别达到了87.5%和100%,有效提高了基于树形结构的文本配置自动校验时的可靠性。     

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.     

构建靶场安全控制辅助决策系统探讨

日益繁重和复杂的靶场试验对靶场安全控制能力要求不断提高,保护航区周围重要目标的安全成为靶场地面安全控制系统的重要使命.针对这一现实需求提出了安全控制决策自动化问题.在分析影响安全控制决策依据的安全控制信息的基础上,构建基于一体化体系结构的靶场安全控制辅助决策系统,建立专家系统模型;探讨了基于模糊推理的导弹工作状态判定问题的推理规则,设计了安全控制辅助决策系统各模块功能;总结了系统须解决的关键技术问题及实现的技术途径.为靶场安全控制辅助决策系统建设提供了科学依据.     

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.     

一类模糊时滞系统的鲁棒二次稳定和H∞控制

研究了一类连续不确定模糊时滞系统的鲁棒二次稳定问题。首先给出不确定时滞系统的自治系统稳定的充分条件,在此基础上设计状态反馈控制器,给出全局二次可稳定的充分条件。相比已有的理论成果,更进一步考虑了系统的日。性能指标,使系统在满足二次可稳定的同时也满足H∞性能指标,并将所得的条件转化为线性矩阵不等式以便借助Matlab完成求解。最后的算例验证了方法的可行性。     

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。