Arnold置乱在零水印中的二次复用

为了提高水印的鲁棒性和减小对应版权信息的大小,本文提出了一种基于Arnold变换、DWT和SVD的零水印算法。该算法将载体图像进行DWT变换,对低频子带进行分块SVD;比较置乱后的最大奇异值的大小,若大于则对应位为1,否则为0;将结果与预处理过的水印做异或运算,得密钥矩阵。该矩阵为检测水印提供密钥。实验证明,本算法对图像压缩、滤波、模糊、噪声及脏图具有良好的鲁棒性。     

Reduced order model predictive control for constrained discrete‐time linear systems

A reduced order model predictive control (MPC) is discussed for constrained discrete‐time linear systems. By employing a decomposition method for finite‐horizon linear systems, an MPC law is obtained from a reduced order optimization problem. The decomposition enables us to construct pairs of initial state and control sequence which have large influence on system responses, and it also characterizes the standard LQ control. The MPC law is obtained based on a combination of the LQ control and dominant input sequences over the prediction horizon. The proposed MPC method is illustrated with numerical examples. Copyright © 2011 John Wiley & Sons, Ltd.     

Blind estimation of the PN sequence in lower SNR DS-SS signals with residual carrier

This paper presents a method of singular value decomposition (SVD) plus digital phase lock loop (DPLL) to solve the difficult problem of blind pseudo-noise (PN) sequence estimation in low signal to noise ratios (SNR) direct sequence spread spectrum (DS-SS, DS) signals with residual carrier. Of course, the method needs to know the parameters of DS signal, such as the period and code rate of PN sequence. Firstly, the received signal is sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. Then, an autocorrelation matrix is computed and accumulated by the signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of this autocorrelation matrix. Furthermore, a DPLL is used to deal with the estimated PN sequence with residual carrier, it estimates and tracks the residual carrier, removes the residual carrier in the end. Theory analysis and computer simulation results show that this method can effectively realize the PN sequence estimation from the input DS signals with residual carrier in lower SNR.     

提升细节捕捉能力的非下采样轮廓波变换

针对传统NSCT(非下采样轮廓波变换)算法中NSP(多尺度分解方法)对细节信息捕捉能力较差及利用其进行图像融合得到的融合图像出现细节丢失问题,提出改进的NSCT算法。不同于传统NSCT算法,该算法首先采用细节捕捉能力较强的非下采样形态学小波分解替代NSP分解,实现对源图像的多尺度分解,将源图像分解成水平高频、垂直高频、对角高频和低频4部分;然后利用NDFB(非下采样的方向性滤波器)对高频部分进行多方向分解得到一系列高频信息,实现改进型NSCT分解。实验结果表明,该算法的细节捕捉能力较传统算法好,在相同融合规则下其图像融合效果更好,各项融合指标值均有所提高,其中平均梯度提高了10%,且易于实现,可广泛用于多分辨率图像融合,是一种有效的融合图像算法。     

SVD域的图像高斯噪声强度估计

精确估计图像或视频中的噪声强度对于后续的信号处理是至关重要的先决条件。通过对含噪图像的奇异值特性的研究,提出一种精确的SVD域的图像噪声强度估计算法。该算法对噪声强度估计提出了创新的解决方法:1)利用奇异值的尾部数据进行噪声强度估计,这样达到尽可能地降低图像信息对噪声估计的干扰;2)对含噪图像加入已知强度的高斯白噪声,以计算噪声估计时需要设置的与图像内容相关的参数,因此该算法可以自适应图像的结构,能够广泛地适应各种类型的图片。实验结果表明SVD域噪声强度估计算法适用于各种图片类型,而且在极大的噪声强度范围内都能够稳定精确地估计噪声强度。     

自适应的有效非局部图像滤波

研究了基于预选择的非局部均值滤波方法,并指出已有方法在提取图像子块特征方面的不足,利用梯度域奇异值分解(SVD) 提出一种自适应的有效非局部滤波方法。该方法对基于预选择的非局部滤波方法的贡献主要有:1)是一种基于图像子块结构特征的鲁棒预选择方法;2)研究了相似集大小与滤波性能的关系;3)相似子块的自动选取;4)提出一种局部自适应的滤波参数选取方法,根据图像局部内容的不同确定滤波参数。此外,利用欧氏距离的对称性进一步提高运行速度。实验结果表明:本文方法滤波后的图像在主客观方面都优于原算法和其他快速方法,且运行速度较快,是一种有效的滤波方法。     

基于等效陀螺的捷联惯导快速两位置对准仿真

研究优化惯导系统的可观性能,提高捷联惯导两位置对准的速度,有助于提高导航的准确性和快速性,将水平速度误差和等效陀螺误差作为系统观测量,提出采用静基座下快速两位置对准的新方法.在常规对准方法的基础上,建立了引入陀螺信息的观测方程,用奇异值分解方法分析了新系统可观测性,推出了第一位置可观测量的最优估计及估计误差.根据状态快速收敛的特性,提出采用PCWS方法,对加速度计和陀螺进行了有效的标定,并进行了仿真,结果证明方法有效提高加快了两位置对准的速度,能充分利用陀螺信息,有效提高了惯导系统的可观测性,缩短对准时间,具有重要的参考价值.     

An analytical and numerical study of the two-dimensional Bratu equation

Bratu's problem, which is the nonlinear eigenvalue equationu+ exp(u)=0 withu=0 on the walls of the unit square and as the eigenvalue, is used to develop several themes on applications of Chebyshev pseudospectral methods. The first is the importance ofsymmetry: because of invariance under the C₄ rotation group and parity in bothx andy, one can slash the size of the basis set by a factor of eight and reduce the CPU time by three orders of magnitude. Second, the pseudospectral method is ananalytical as well as a numerical tool: the simple approximation3.2A exp(–0.64A), whereA is the maximum value ofu(x, y), is derived via collocation with but a single interpolation point, but is quantitatively accurate for small and moderateA. Third, the Newton-Kantorovich/Chebyshev pseudospectral algorithm is so efficient that it is possible to compute good numerical solutions—five decimal places—on amicrocomputer inbasic. Fourth, asymptotic estimates of the Chebyshev coefficients can be very misleading: the coefficients for moderately or strongly nonlinear solutions to Bratu's equations fall off exponentially rather than algebraically withv untilv is so large that one has already obtained several decimal places of accuracy. The corner singularities, which dominate the behavior of the Chebyshev coefficients in thelimit v, are so weak as to be irrelevant, and replacing Bratu's problem by a more complicated and realistic equation would merely exaggerate the unimportance of the corner branch points even more.     

基于前景理论的风-蓄联合系统中长期电量分解模型的研究

为解决风-蓄联合系统同时参与中长期市场与日前市场的组合交易问题,提出一种考虑发电商心理特征的方法,并给出基于前景理论的风-蓄联合系统中长期电量分解模型。该模型通过构建发电商收益的价值函数衡量发电商对收益偏差的感受,运用概率权重函数描述发电商对不同场景下中长期电量分配方案的偏好,建立以前景效用值最大化为目标的风-蓄联合系统中长期电量分解模型。算例分析验证了所建模型的合理性和有效性,为发电商制定合理可行的盈利目标,提升发电企业在电力市场环境下的盈利水平提供理论依据和参考。     

GIL机械故障诊断与预警技术研究

为了有效诊断气体绝缘金属封闭输电线路(GIL)的机械故障,搭建了110 kV GIL试验平台并设计了3种典型机械故障,通过互补集合经验模态分解(CEEMD)模糊熵值与鲸鱼优化极限学习机(WOA-ELM)模型联合方法对GIL机械故障模式进行识别与诊断。首先,利用CEEMD方法对振动信号进行分解,引入正负白噪声组对信号进行处理,得到含有故障信息的模态分量(IMF)。其次,利用模糊熵计算模态分量特征值,得到能表征故障特征的模糊熵值。最后,结合WOA-ELM模型对特征向量集进行模式识别,根据聚类结果与自适应阈值对GIL设备机械故障进行诊断和预警。结果表明,利用CEEMD与模糊熵对GIL振动信号特征进行分析,可以有效避免模态混叠和冗余噪声分量的干扰,得到能够表征故障特征的特征值;利用WOA-ELM模型可以有效实现GIL设备机械故障诊断与预警。