基于核主成分分析的多输出模型确认方法

目前对于不确定性环境下多个相关的复杂计算模型进行确认的方法存在计算困难及稳定性较差的问题。针对这类复杂计算模型,提出了一种新的基于核主成分分析(KPCA)的多输出模型确认方法。该方法将核主成分分析与面积法的思想相结合,构造了一个新的易于计算且稳定性高的模型确认指标。所提方法通过核主成分分析将相关的输出变量转化为不相关的核主成分,再对每一核主成分进行模型与实验的对比,从而避免了传统多输出模型确认方法中需要求解多个输出的联合累积分布函数的困难。由于核主成分分析(PCA)方法能够有效提取分析对象的非线性成分,因此基于核主成分分析的多输出模型确认方法较基于主成分分析的模型确认方法更为稳定,这表现在相同的实验样本数据下核主成分分析的方法具有更低的出错率。另外核主成分分析通过核主成分提取,可以实现多输出模型的降维,从而降低多输出模型确认的复杂度。所提方法既可以用于一般的多输出模型的确认,也可以用于多确认点的输出模型的确认。最后通过数值算例和工程算例证明了该方法的正确性与有效性。     

一种基于双模式成像的遥感图像去运动模糊方法

对地观测载荷大多工作在颤振环境之中,这种颤振造成光轴偏移、像面抖动,由此产生的运动模糊对高分辨率成像与高精度探测产生了极为不利的影响。对空间相机像面去模糊的工作,大致分为两种方案：一种为硬件实时探测加硬件实时补偿,另一种为硬件实时探测加事后软件补偿。文章提出通过主成像相机的双成像模式获得低信噪比无抖动图像和正常信噪比模糊图像,利用图像处理手段从这一对图像中求得抖动信息（模糊核）,并以此为先验知识指导模糊图像的复原,抑制像面抖动。这种方法既不需要补偿器件,也不需要探测元件和探测支路,并在图像迭代处理过程中不断修正模糊核,在初始模糊核有一定误差的情况下,同样可望取得良好的像面去模糊效果,对于遥感图像像质提高具有潜在的应用价值。     

Experimental investigation of a gliding discharge plasma jet igniter

Relight of jet engines at high altitude is difficult due to the relatively low pressure and temperature of inlet air. The penetration of initial flame kernel affects the ignition probability in the turbine engine combustor greatly. In order to achieve successful ignition at high altitude, a deeper penetration of initial flame kernel should be generated. In this study, a Gliding Arc Plasma Jet Igniter(GAPJI) is designed to induce initial flame kernel with deeper penetration to achieve successful ...     

支持向量机理论的研究与进展

回顾了支持向量机理论的发展历程,介绍了支持向量机的标准训练算法及其分解算法、变形算法、几何算法以及多类分类算法,重点描述了最小二乘支持向量机算法,总结了支持向量机理论及其应用的现状与存在问题,对支持向量机的未来发展方向进行了展望。     

SINS/GNSS integrated navigation system based on maximum versoria filter

In the missile-borne Strapdown Inertial Navigation System/Global Navigation Satellite System (SINS/GNSS) integrated navigation system, due to the factors such as the high dynamics, the signal blocking by obstacles, the signal intefereces, etc., there always exist pulse interferences or measurement information interruptions in the satellite receiver, which make nonstationary measurement process. The traditional Kalman Filter (KF) can tackle the state estimation problem under Gaussian white noise, but its performance will be significantly reduced under non-Gaussian noises. In order to deal with the non-Gaussian conditions in the actual missile-borne SINS/GNSS integrated navigation systems, a Maximum Versoria Criterion Extended Kalman Filter (MVC-EKF) algorithm is proposed based on the MVC and the idea of M-estimation, which assigns a smaller weight to the anomalous measurements so as to suppress the influence of anomalous measurements on the state estimation while maintaining a relatively low calculation cost. Finally, the integrated navigation simulation experiments prove the effectiveness and robustness of the proposed algorithm.