基于偏最小二乘特征提取的支持向量机回归算法

为了提高SVM的建模质量,简化建模难度,提出了PLS-SVM组合回归建模方法.该方法通过PLS对样本数据进行降维、去噪以及消除共线性处理后,再进行SVM回归建模.不仅保持了SVM良好的模型性能,而且使SVM具备特征提取功能.实验结果表明,该方法是可行的,采用此法构建的SVM模型,泛化性能优于没有特征提取的SVM.     

A Squared-Chebyshev wavelet thresholding based 1D signal compression

In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression; but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches.     

加权模糊C均值聚类算法实现BDS三频组合观测值优选

在对BDS三频载波相位组合观测值进行误差分析的基础上,确定了优选载波相位线性组合系数的筛选标准。针对传统聚类算法在高维多频混合数据集分类中存在的不足,采用一种基于加权的模糊C均值聚类算法,通过对同一维度在不同簇上赋予不同的权重值,对传统遍历搜索法所获得的部分BDS三频载波相位组合观测值进行了优化分类选取,有效解决了传统全球导航卫星系统载波相位观测值选取方法效率低的问题,同时为多系统多频数据组合观测值系数的优化选取提供了一种新的思路。对分类结果进行分析,确定了各类组合观测量的适用范围,并结合实测数据,利用无几何层叠模糊度解算方法对优选组合进行了整周模糊度的解算,结果验证了该方法的可行性。     

视频编码标准H．264运动矢量搜索算法

高清视频的信息量巨大,传输比较困难。 H．264作为视频压缩的主流标准,可以有效去除视频的冗余信息,提高视频的网络传输能力。针对H．264中运动估计算法耗时严重的问题,提出一种新的X型搜索算法。 X型算法由粗到细地进行搜索,先以X型快速搜索确定运动矢量的可能范围,然后在可能范围内进行局部全搜索,最终确定运动矢量。试验结果表明,X型搜索能够在保证视频质量的情况下大幅度缩短编码时间,有效提高运动估计算法的效率,兼顾了速度与精度,为高清视频的实时传输提供了保障。     

关于算子方程Tx=y扰动分析的一些结果

在Banach空间中给出了一种相容算子方程解的误差估计 ,推广了矩阵扰动分析中的相应结果 .此外 ,利用Hilbert空间中算子M -P广义逆与算子的约化极小模之间的关系 ,给出了一些估计式 ,这些估计式对于分析不相容算子方程Tx =y的极小范数最小二乘解的扰动误差是有用的     

Fragment spatial distribution of prismatic casing under internal explosive loading

Non-cylindrical casings filled with explosives have undergone rapid development in warhead design and explosion control. The fragment spatial distribution of prismatic casings is more complex than that of traditional cylindrical casings. In this study, numerical and experimental investigations into the fragment spatial distribution of a prismatic casing were conducted. A new numerical method, which adds the Lagrangian marker points to the Eulerian grid, was proposed to track the multi-material interfaces and material dynamic fractures. Physical quantity mappings between the Lagrangian marker points and Eulerian grid were achieved by their topological relationship. Thereafter, the fragment spatial distributions of the prismatic casing with different fragment sizes, fragment shapes, and casing geometries were obtained using the numerical method. Moreover, fragment spatial distribution experiments were conducted on the prismatic casing with different fragment sizes and shapes, and the experimental data were compared with the numerical results. The effects of the fragment and casing geometry on the fragment spatial distributions were determined by analyzing the numerical results and experimental data. Finally, a formula including the casing geometry parameters was fitted to predict the fragment spatial distribution of the prismatic casing under internal explosive loading.     

A novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise,minimum mean square variance criterion and least mean square adaptive filter

Underwater acoustic signal processing is one of the research hotspots in underwater acoustics. Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing. Owing to the complexity of marine environment and the particularity of underwater acoustic channel, noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing. In order to solve the dilemma, we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), minimum mean square variance criterion (MMSVC) and least mean square adaptive filter (LMSAF). This noise reduction technique, named CEEMDAN-MMSVC-LMSAF, has three main advantages: (i) as an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD), CEEMDAN can better suppress mode mixing, and can avoid selecting the number of decomposition in variational mode decomposition (VMD); (ii) MMSVC can identify noisy intrinsic mode function (IMF), and can avoid selecting thresholds of different permutation entropies; (iii) for noise reduction of noisy IMFs, LMSAF overcomes the selection of decomposition number and basis function for wavelet noise reduction. Firstly, CEEMDAN decomposes the original signal into IMFs, which can be divided into noisy IMFs and real IMFs. Then, MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs. Finally, both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained. Compared with other noise reduction techniques, the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals, which has the better noise reduction effect and has practical application value. CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection, feature extraction, classification and recognition of underwater acoustic signals.     

基于正态分布曲线的分段式变步长LMS算法

针对传统最小均方误差(Least Mean Square, LMS)自适应滤波算法由于步长固定,在解决稳态误差与收敛性之间的关系时,始终处于矛盾状态的问题,在对传统的固定步长LMS自适应滤波算法分析的基础上,根据变步长LMS自适应滤波算法的步长调整原则,通过构造步长因子与误差信号的非线性函数,提出了一种基于正态分布曲线的分段式变步长LMS自适应滤波算法,并分析了参数取值对算法性能的影响。针对实际信号处理过程中参考信号难以选取的问题,提出了一种基于分裂阵的参考信号选取方法。理论和海试数据分析结果表明:该算法的收敛速度和稳态误差明显优于固定步长的LMS自适应滤波算法和基于Sigmoid函数的变步长LMS自适应滤波算法。     

炮兵雷达组网校射评估系统的设计与实现

针对多部雷达保障校正射击时各个雷达获得的数据精度不高、数据传输自动化程度低、炸点坐标缺乏数据融合等问题,采用VC＋＋和SQLServer为软件开发平台,设计并实现了炮兵雷达组网校射评估系统。讨论了系统的基本组成和软件结构。同时根据最小方差原则,按照各雷达的精度分配权值,设计了多雷达组网对炸点测量数据最优融合估计的数据融合算法。通过实弹射击保障应用表明,该系统提高了雷达对炸点数据测量的精度及炮兵指挥自动化能力。     

全球化时代的移民与国家安全：风险评估及其控制

移民具有经济、社会、政治等多重属性,其对国家安全的影响也涉及多个领域,且具有很强的不确定性。而地缘政治地位关键、领土存在国际争议、非劳动力要素供给缺乏弹性、族群认同超越国家认同和政府履行社会管理职能存在结构性缺陷等条件变量的作用,会显著增加移民的安全风险。这意味着控制移民安全风险的关键在于抑制或消除这些条件变量的作用。