基于Subsampling抽样的厚尾AR(p)序列趋势变点的Ratio检验

文章考虑的是厚尾AR(p)序列趋势变点检验问题。首先,在已有研究的启发下,构造了一个Ratio统计量来检验趋势变点;其次,在原假设下证明统计量的极限分布是列维过程的泛函,在备择假设下得到统计量的一致性;其次,为了避免参数的估计,采用Subsampling方法获得更为准确的临界值,数值模拟结果显示,在大样本下基于Subsampling抽样方法的Ratio检验很好地控制了经验水平,经验势也达到了比较好的效果;最后,通过一组实证数据进一步阐明理论的有效性和可行性。

含有变点的厚尾单位根的subsampling检验

本文研究趋势项含有变点且新息为方差无穷厚尾过程的序列单位根检验问题,通过构造DF型检验,得到了其渐近分布。为避免估计统计量渐近分布中的尾指数,构造subsampling抽样方法来确定统计量渐近分布的百分位数,同时论证了subsampling抽样方法的一致性。最后,用Monte Carlo模拟证实本文所提出统计量以及subsampling抽样方法的可行性。

重尾过程的subsampling协整检验

由于重尾过程协整检验统计量的渐近分布含有不可估计的重尾指数α,本文通过构造subsampling抽样算法,在不估计重尾指数α的情况下,计算该检验统计量的临界值,并且证明该算法在理论上的合理性.最后,通过MonteCalo模拟证明该方法的有效性.

基于稳定分布的ARCH模型均值变点Subsampling检验

讨论了基于稳定分布的ARCH模型的均值变点检验问题,其中特征指数k∈(1,2)。基于残量平方累积和统计量,利用Subsampling抽样方法确定渐近分布的临界值,从而避免特征指数k的估计。结果显示:蒙特卡罗数值模拟结果和实证分析充分说明了Subsampling抽样方法的可行性和有效性。因此,基于Subsampling的残量平方累积和检验对于稳定分布的ARCH模型均值变点检验仍不失为一种有效的方法。

Subsampling Method for Robust Estimation of Regression Models

We propose a subsampling method for robust estimation of regression models which is built on classical methods such as the least squares method. It makes use of the non-robust nature of the underlying classical method to find a good sample from regression data contaminated with outliers, and then applies the classical method to the good sample to produce robust estimates of the regression model parameters. The subsampling method is a computational method rooted in the bootstrap methodology which trades analytical treatment for intensive computation;it finds the good sample through repeated fitting of the regression model to many random subsamples of the contaminated data instead of through an analytical treatment of the outliers. The subsampling method can be applied to all regression models for which non-robust classical methods are available. In the present paper, we focus on the basic formulation and robustness property of the subsampling method that are valid for all regression models. We also discuss variations of the method and apply it to three examples involving three different regression models.

ultiscale full-waveform inversion based on shot subsampling

Conventional full-waveform inversion is computationally intensive because it considers all shots in each iteration. To tackle this, we establish the number of shots needed and propose multiscale inversion in the frequency domain while using only the shots that are positively correlated with frequency. When using low-frequency data, the method considers only a small number of shots and raw data. More shots are used with increasing frequency. The random-in-group subsampling method is used to rotate the shots between iterations and avoid the loss of shot information. By reducing the number of shots in the inversion, we decrease the computational cost. There is no crosstalk between shots, no noise addition, and no observational limits. Numerical modeling suggests that the proposed method reduces the computing time, is more robust to noise, and produces better velocity models when using data with noise.

Responses of diff erent biodiversity indices to subsampling efforts in lotic macroinvertebrate assemblages

As a less time-consuming procedure, subsampling technology has been widely used in biological monitoring and assessment programs. It is clear that subsampling counts af fect the value of traditional biodiversity indices, but its ef fect on taxonomic distinctness(TD) indices is less well studied. Here, we examined the responses of traditional(species richness, Shannon-Wiener diversity) and TD(average taxonomic distinctness: Δ +, and variation in taxonomic distinctness: Λ +) indices to subsample counts using a random subsampling procedure from 50 to 400 individuals, based on macroinvertebrate datasets from three dif ferent river systems in China. At regional scale, taxa richness asymptotically increased with ?xed-count size; ≥250–300 individuals to express 95% information of the raw data. In contrast, TD indices were less sensitive to the subsampling procedure. At local scale, TD indices were more stable and had less deviation than species richness and Shannon-Wiener index, even at low subsample counts, with ≥100 individuals needed to estimate 95% of the information of the actual Δ + and Λ + in the three river basins. We also found that abundance had a certain ef fect on diversity indices during the subsampling procedure, with dif ferent subsampling counts for species richness and TD indices varying by regions. Therefore, we suggest that TD indices are suitable for biodiversity assessment and environment monitoring. Meanwhile, pilot analyses are necessary when to determine the appropriate subsample counts for bioassessment in a new region or habitat type.

Novel block-matching algorithms by subsampling both search candidates and pixels

A new faster block-matching algorithm （BMA） by using both search candidate and pixd sulzsamplings is proposed. Firstly a pixd-subsampling approach used in adjustable partial distortion search （APDS） is adjusted to visit about half points of all search candidates by subsampling them, using a spiral-scanning path with one skip. Two sdected candidates that have minimal and second minimal block distortion measures are obtained. Then a fine-tune step is taken around them to find the best one. Some analyses are given to approve the rationality of the approach of this paper. Experimental results show that, as compared to APDS, the proposed algorithm can enhance the block-matching speed by about 30% while maintaining its MSE performance very close to that of it. And it performs much better than many other BMAs such as TSS, NTSS, UCDBS and NPDS.

Genetic algorithm for λ-optimal translation sequence of rough communication

In rough communication, because each agent has a different language and can not provide precise communication to each other, the concept translated among multi-agents will loss some information, and this results in a less or rougher concept. With different translation sequences the amount of the missed knowledge is varied. The λ-optimal translation sequence of rough communication, which concerns both every agent and the last agent taking part in rough communication to get information as much as he （or she） can, is given. In order to get the λ-optimal translation sequence, a genetic algorithm is used. Analysis and simulation of the algorithm demonstrate the effectiveness of the approach.

<i>ε</i>-Optimality in Multivalued Optimization

In this paper we apply the directional derivative technique to characterize D-multifunction, quasi D-multifunction and use them to obtain ε-optimality for set valued vector optimization problem with multivalued maps. We introduce the notions of local and partial-ε-minimum (weak) point and study ε-optimality, ε-Lagrangian multiplier theorem and ε-duality results.

基于NSST与稀疏先验的遥感图像去模糊方法

针对遥感图像的模糊问题,设计一种基于非下采样剪切波变换与稀疏先验的图像复原算法.首先,利用遥感图像在非下采样剪切波分解下的高频图像的稀疏特性设置先验条件构造图像复原模型;其次,采用交替方向乘子法求解模型;再次,采用软阈值方法对高频图像进行约束处理,在低频图像进行导向滤波处理,以最大可能保留图像的细节信息;最后,将高频图像与低频图像进行重构,对重构后的图像采用卷积神经网络进行深度去噪,最终复原出清晰的图像.将该去模糊算法与H-PNP,GSR,L2TV算法进行实验对比.实验结果表明,该算法能有效去除遥感图像中的模糊和噪声,保留图像的边缘细节,客观评价指标均高于其他3种对比实验算法.

面向地物混杂背景的偏振光谱图像融合方法

针对偏振光谱图像融合方法在地物混杂背景遥感探测中多尺度变换融合图像存在边缘轮廓细节模糊、对比度不佳的问题,提出一种基于非下采样轮廓波变换的稀疏表示与引导滤波器相结合的图像融合方法,以改善融合图像的质量和视觉效果。首先,该方法通过非下采样轮廓波变换对光谱图像和偏振图像进行多尺度多方向分解,进而将图像分解成不同子带内的特征信息。其次,低频子带采用稀疏表示融合,从而降低融合图像中物体对比度损失。此外,采用引导滤波器融合高频子带,以增强图像轮廓细节信息。最后,对低频与高频融合系数进行非下采样轮廓波逆变换,最终得出融合图像。分析表明融合图像对比度相对于原始光谱图像与偏振度图像分别提升了54.5%和15.4%,更容易区分混杂背景下阴影中的物体。基于此方法对偏振光谱成像仪所采集的不同波长下的光谱与偏振图像进行融合,并实现真彩还原。真彩还原图像证明此融合方法在保留混杂背景下的环境信息的同时实现了物体和背景的有效区分,有效提高了偏振光谱遥感探测成像的图像质量,有助于提升偏振光谱遥感探测成像中图像信息的完整性和真实性,扩大其在复杂环境遥感探测和图像识别中的应用范围。

基于多通道的彩色图像多重水印算法

针对多重水印技术如何确定最优的嵌入区域,以实现多个水印之间的权衡问题,提出一种基于多通道嵌入的彩色图像多重水印算法。当嵌入水印时,选择在彩色图像R、G、B 3个通道嵌入不同的水印图像,目的是嵌入更多的水印信息。通过提升小波变换(LWT)和非下采样剪切波变换(NSST)提取宿主图像每个通道中的方向性特征,确定水印的嵌入位置,以提高对常规攻击和几何攻击的鲁棒性。通过将加密的水印有效信息嵌入到具有良好稳定性的奇异值矩阵中,保证水印的提取效果。实验结果表明,嵌入水印后的图像峰值信噪比(PSNR)在43 dB以上,所提算法具备良好的不可见性,对嵌入水印后的图像进行高强度的旋转、滤波、剪切等攻击,提取水印的归一化系数(NC)值在0.95以上,其中,滤波攻击对应的NC值可以达到0.99以上,水印信息提取完整、清晰可辨,表现出较强的鲁棒性。同时,该算法可以1次嵌入3个水印图像,具有更高的嵌入容量。因此,与现有的多重水印算法相比,该算法的不可见性、鲁棒性和嵌入容量均有较大的提升。

多光谱图像融合的IC器件表面缺陷检测

针对单可见光或单红外条件下的IC器件表面缺陷对比度不足,缺陷检测精度低的问题,提出多光谱图像融合的IC器件表面缺陷检测方法。针对IC器件可见光与红外图像配准中存在尺度不一致和对比度反转问题,引入拉普拉斯金字塔和特征描述符重组策略改进ORB(Oriented FAST and Rotated BRIEF)图像配准算法。在图像配准的基础上,提出NSST_VP图像融合方法,以非下采样剪切波变换(Non-Subsample Shearlet Transform, NSST)得到红外图像和已配准可见光图像的低频和高频子带,对低频子带采用视觉显著图(Visual Significance Map, VSM)加权融合规则,高频子带则采用自适应脉冲耦合神经网络(PA-Pulse Coupled Neural Network, PA-PCNN)决策融合规则,进而通过NSST逆变换得到高质量多光谱融合图像。最后,将融合图像输入YOLOv8s模型进行检测。实验结果表明,改进ORB的图像配准平均精度为87.8%,比ORB图像配准精度提高了62%,NSST_VP图像融合算法在主观视觉效果和客观评价指标上均有所提高。在缺陷检测实验中,NSST_VP融合方法的均值平均精度(mean Average Precision, mAP)达到83.15%,比单可见光、单红外缺陷图像检测的mAP分别提高了22.97%,28.31%,比双树复小波变换融合、曲线变换融合、非下采样轮廓波变换融合方法的mAP分别提高了13.14%,15.01%,20.35%。

基于多层显著性模型的SAR图像舰船目标检测

针对合成孔径雷达图像舰船目标检测问题,提出了一种结合选择机制与轮廓信息的多层显著性目标检测方法。首先,利用非下采样剪切波和频谱残差法进行全局显著性区域提取。其次,提出了一种基于动态恒虚警率的活动轮廓显著性模型,逐步滤除候选区域的虚警,提取目标轮廓,从而实现目标的精确检测。所提方法能够由粗到细地快速捕获目标区域,从而实现高效、高分辨率合成孔径雷达图像舰船检测。最后,在真实SAR数据集进行了测试,与其他经典的舰船检测方法相比,所提算法不仅有效地抑制了海杂波的影响,而且在检测精度上有较大提高。

Optimal decorrelated score subsampling for generalized linear models with massive data

In this paper, we consider the unified optimal subsampling estimation and inference on the lowdimensional parameter of main interest in the presence of the nuisance parameter for low/high-dimensionalgeneralized linear models (GLMs) with massive data. We first present a general subsampling decorrelated scorefunction to reduce the influence of the less accurate nuisance parameter estimation with the slow convergencerate. The consistency and asymptotic normality of the resultant subsample estimator from a general decorrelatedscore subsampling algorithm are established, and two optimal subsampling probabilities are derived under theA- and L-optimality criteria to downsize the data volume and reduce the computational burden. The proposedoptimal subsampling probabilities provably improve the asymptotic efficiency of the subsampling schemes in thelow-dimensional GLMs and perform better than the uniform subsampling scheme in the high-dimensional GLMs.A two-step algorithm is further proposed to implement, and the asymptotic properties of the correspondingestimators are also given. Simulations show satisfactory performance of the proposed estimators, and twoapplications to census income and Fashion-MNIST datasets also demonstrate its practical applicability.

基于稳健距离的大数据Logistic回归最优子抽样

大数据统计分析在有限的计算资源下面临一些挑战性问题,用子数据代替全数据进行统计分析成为一种选择。文章基于最小协方差行列式的稳健距离,为大数据Logistic回归模型提出了一种更高效的子数据选择算法。通过大量的数值模拟,在不同的标准下比较了所提算法与其他已有算法的性能。结果表明,所提算法具有较高的估计效率和计算效率,与全数据相比,计算时间显著减少。与其他算法相比,所提算法得到的子数据信息矩阵行列式的值更大。同时,当协变量之间存在高度相关性时,所提算法具有稳健性。最后,通过对实际数据集的分析,说明了所提算法的预测误差更小。

结合局部熵与梯度能量的双通道医学图像融合

多模态医学图像融合已成为有效结合正常组织结构和异常改变信息、提高医学诊断效率的强有力辅助技术.针对空域融合技术在处理图像细节丢失、光谱退化等方面的缺陷,提出一种在联合双边滤波器(JBF)域内实现图像结构与细节信息增强的双通道频域多模态医学图像融合方法.该方法将源图像分解为结构和能量2个通道,分别处理图像纹理细节信息和边缘强度信息.在结构通道中,通过改进梯度能量得到局部梯度能量算子,进一步提升融合图像对小尺度细节信息的表达能力和对噪声的鲁棒性;在能量通道中,利用非下采样轮廓波变换提高模型的多方向多尺度特性,并提出一种局部熵细节增强算子和脉冲耦合神经网络结合的高频子带处理框架,达到增强能量通道中结构信息和细节信息的效果.在Atlas公开数据集上,与基于MST、稀疏表示、PCNN以及JBF的6种具有代表性的频域融合方法进行对比及消融实验的结果表明,所提方法的融合图像与源图像相似度提升35.0%,空间频率提升16.2%,边缘保持度提升12.5%,对比度提升11.2%;并在视觉方面有较好的效果,明显优于对比方法.

基于改进PM扩散方程的NSST域声呐图像融合

针对声呐图像边缘特征信息模糊丢失等问题,为使图像尽可能包含更多边缘和纹理特征等信息,提出一种基于改进PM扩散方程非下采样剪切波变换声呐图像融合改进算法。采用非线性扩散滤波对NSST多尺度分解进行改进,将多幅源图像分解为高频系数和低频系数,并且结合局部能量及PCNN方法对高低频系数进行融合,经过逆变换重构为融合图像。经过实验验证与其他算法相比,所提方法能够较好地保留源图像中的边缘特征信息,在含噪声声呐图像上表现较为明显。