噪声为单位根过程的非参数函数变点的小波检测

利用小波方法和极限定理对噪声为单位根过程的非参数函数的跳跃点进行检测. 首先, 利用极限定理得到噪声的小波系数的极限分布.然后构造检验统计量, 在原假设成立的条件下得到任意尺度上检验的临界值, 证明了检验的一致性, 并给出小波系数的阈值; 在备择假设成立的条件下,给出变点个数、变点位置的相合估计与收敛速度. 最后利用模拟研究与实例分析说明了方法的有效性和实用性, 并与“UNI”方法以及“GOF”方法作比较, 说明对于噪声为单位根过程的非参数函数变点的检测问题, 本文所提的方法更加有效.     

随机系数自回归模型变均值点在线监测与应用

对随机系数自回归模型的变均值点进行在线监测时, 如果变均值点的位置远离开始监测点, 则平均地说, 需要较长的运行时间方能检测到该变均值点. 为此, 笔者引进一个窗宽参数, 提出了一种改进的在线监测方法. 给出了监测统计量在原假设下的极限分布, 并证明了此方法的一致性. 模拟结果显示新方法明显优于已有的方法. 最后将该方法应用于两组股票价格均值点的监测问题中, 说明了方法的有效性.     

基于窗口统计量的水下分布式目标检测算法

针对水声传感器网络对水下目标检测时面临的节点数目、布放位置随机、检测性能时变、缺乏入侵目标先验模型的问题,将对点目标的假设检测推广到对最优海域窗口的假设检测,提出了一种基于最优窗口统计量的融合检测规则,近似推导出了算法系统级的检测性能,并给出了仿真对比实验.结果表明:在满足滑动窗口同目标辐射信号区域近似匹配的条件下,基于最优窗口统计量的融合检验规则可以获得良好的系统级检测性能,与已有的非参数类投票计数融合规则相比,相同信噪比下,基于最优窗口统计量的融合规则目标检测性能更好.     

改进的基于非局部均值的极化SAR相干斑抑制

贝叶斯形式的非局部均值模型在极化SAR图像相干斑抑制中有良好的应用,在实现抑制相干斑的同时较好地保持了边缘细节和点目标.通过分析合成孔径雷达(SAR)图像多视数据的空间统计分布,结合贝叶斯形式的非局部均值模型,得出在该模型下多视与单视SAR图像中像素间相似性度量函数一致性的结论,并对该相似性度量函数进行了修正,使之满足对称性;最后针对算法全局使用一个固定滤波参数影响滤波效果的问题,提出一种根据像素间相似程度自适应选取滤波参数的方法.实验结果验证了本文算法的有效性.     

小样本下基于Bootstrap的航程油耗预测方法

利用传统的数据分析方法预测飞机燃油消耗量需要大量的样本,针对这一问题,提出一种基于Bootstrap统计理论建立油耗预测模型的方法.基于真实的QAR(quick access recorder)数据,首先利用Bootstrap统计方法得到相关航程下油耗均值和一定置信水平下均值的置信区间,然后对多组均值和置信区间的上、下限分别进行拟合建模,能够得到油耗与航程关系模型及航程与燃油消耗带关系模型.最后,将结果分别与最小二乘法下的预测模型及2203组数据样本下的油耗模型作对比,结果表明:小样本量下的Bootstrap方法预测模型准确度较高.     

一种改进的基于非局部均值的极化SAR相干斑抑制算法

贝叶斯形式的非局部均值模型在极化SAR图像相干斑抑制中有良好的应用,在实现抑制相干斑的同时较地保持了边缘细节和点目标。本文通过分析SAR图像多视数据的空间统计分布,结合贝叶斯形式的非局部均值模型,得出了在该模型下多视与单视SAR图像中像素间相似性度量函数一致性的结论,并对该相似性度量函数进行了修正,使之满足对称性;最后针对算法全局使用一个固定滤波参数影响滤波效果的问题,提出了一种根据像素间相似程度自适应选取滤波参数的方法。实验结果验证了本文算法的有效性。     

一种基于超统计理论的非平稳时间序列异常点检测方法研究

从非平稳时间序列的分布函数及其参数入手,主要研究分布函数不变分布参数变化的这一类非平稳的时间序列异常点检测方法,提出了基于超统计的异常检测方法,并将其应用于非平稳网络流量时间序列。从网络流量的非平稳和突发性特点出发,特别考虑到由于攻击流量所引起的流量特性的变化,结合超统计理论,主要研究分布参量的变化。根据超统计的理论,先应建立分布统计模型,研究分布模型不同参数变化对分布的决定性作用,从而将异常网络流量的检测研究转化成对慢变量参数序列的检测研究。该检测方法大大降低了计算的复杂度。通过大量实验表明该方法具有良好的效果。     

基于邻域信息的B样条密度模型的医学图像分割研究

针对有参混合模型的聚类算法需要假设模型为某种已知的参数模型而存在模型不匹配及应用于图像分割时对噪声比较敏感的问题,提出了一种基于空间邻域信息的B样条密度模型的图像分割方法。首先,通过构建基于规范化的B样条密度函数的非参数混合模型,定义空间信息函数,使得分割模型具有空间邻域信息;其次,利用非参数B样条期望最大(NNBEM)算法估计密度模型的未知参数;最后根据贝叶斯准则实现图像的分割。该图像分割方法不需要假设图像符合某种模型,就可以克服实际数据分布与假设图像模型不一致的问题。此方法有效克服了"模型失配"问题,而且有力抑制了噪声点,同时很好地保留了边界的特性。分别对模拟图像进行仿真,验证了基于空间邻域信息的B样条密度模型的分割方法的有效性。     

基于X2统计检验的线性离散时滞系统故障检测

基于X2统计检验,研究了一类含状态时滞线性离散时变系统的故障检测问题. 与基于残差的传统故障检测方法不同, 本文直接应用测量输出构造残差评价函数, 并通过投影与新息分析, 得到了残差评价函数的Riccati递推解. 分析表明, 该方法有效降低了残差评价函数的计算量, 并且在无故障发生情况下服从X2分布. 进一步, 通过X2统计检验可以判断系统是否有故障发生. 最后,通过一算例验证了提出方法的有效性.     

基于AUC的非参数快速变点检测算法

目前,变点检测技术已经广泛应用到各个领域。然而,由于实际生产环境的复杂性,变点检测技术中的常用参数方法往往存在一定的局限性。为了克服这些问题,提出一种新的变点检测的非参数方法,通过互联网检索应为首次应用AUC（曲线下面积）对样本数据进行在线变点检测。该方法将变点检测分为2个阶段：预分析阶段,对样本数据进行加窗处理,通过计算窗口中样本数据的AUC值的方式来间接得到其均值和方差;检测阶段,通过假设检验的方法对经过处理后的样本数据进行变点检测。通过实验仿真,可以观察到此算法与常规CUSUM算法相比,具有更好的稳健性,而且对检测多个突变点的情况同样有效。     

Improved DCT-based method for online detection of oscillations in univariate time series

Automatic oscillation detection for univariate time series is the very first step in detection and compensation of oscillations in process industries. This paper is motivated by our industrial experience of applying the discrete cosine transform (DCT)-based method for oscillation detection. An improved DCT-based method is proposed with three main modifications, namely, a revised hypothesis test based on the confidence interval of coefficient of variation, a fitness index to determine a dominant oscillation component, and a hypothesis test on the regularity of oscillation magnitudes. These modifications are also applicable to other oscillation detection methods in the literature. Moreover, an online oscillation detection method is proposed, with a mechanism by which the size of a supervision time window is adaptive to frequency variation of process variables. Industrial examples are provided to demonstrate the effectiveness of the two proposed methods.     

改进最小二乘变点识别法在负荷分解的应用

随着全世界正进行的大规模智能电表的推广安装,使用非侵入式负荷监测分解方法,总电能消耗分解为单独设备的消耗,成为最近的研究热点。而变点识别是负荷分解方法中的第一步。精确的变点检测为后续提取特征以及识别负荷,打下了坚实的基础。提出了一种基于均值变点模型的识别算法,通过滑动窗口,利用最小二乘法计算目标函数,以确定变点个数。最后,提出假设检验,来验证变点检测的准确性。它能根据相关信号准确检测到负荷投切等引起的电气量变化、发生时刻等重要信息,并记录下来,然后为后续的负荷识别和分解提供保障。最后以某商业写字楼为例,通过测量该商业部分用电负荷数据,从而验证了该算法的可行性。     

A control chart based on likelihood ratio test for detecting patterned mean and variance shifts

Control charts based on generalized likelihood ratio test (GLRT) are attractive from both theoretical and practical points of view. Most of the existing works in the literature focusing on the detection of the process mean and variance are almost based on the assumption that the shifts remain constant over time. The case of the patterned mean and variance changes may not be well discussed. In this research, we propose a new control chart which integrates the exponentially weighted moving average (EWMA) procedure with the GLRT statistics to monitor the process with patterned mean and variance shifts. The attractive advantage of our control chart is its reference-free property. Due to the good properties of GLRT and EWMA procedures, our simulation results show that the proposed chart provides quite effective and robust detecting ability for various types of shifts. The implementation of our proposed control chart is illustrated by a real data example from chemical process control.     

SAR变化检测技术发展综述

合成孔径雷达(SAR)具有全天候、全天时的特点,是很好的变化检测信息源,研究SAR图像变化检测技术有着非常广阔的应用前景。系统介绍了SAR变化检测的整个流程--数据预处理、变化检测算法、检测后处理及变化检测算法评价。为了方便算法设计者,分门别类地阐述了变化检测算法的主要方法及核心决策规则,包括差值法、统计假设检验法、预测模型、相干模型;并对每种方法的特征进行了分析和比较。针对SAR技术的迅猛发展,我们对将来SAR变化检测技术的发展方向给予了预测。     

Utilizing advances in correlation analysis for community structure detection

In the era of big data, some data records are interrelated with each other in many areas, such as marketing, management, health care, and education. These interrelated data can be more naturally represented as networks with nodes and edges. Inside this type of networks, there is usually a hidden community structure where each community represents a relatively independent functional module. Such hidden community structures are useful for many applications, such as word-of-mouth marketing, promoting decentralized social interactions inside organizations, and searching biological pathways related to various diseases. Therefore, how to detect hidden community structures becomes an important task with wide applications. Currently, modularity-based methods are widely-used among many existing community structure detection methods. They detect communities with more internal edges than expected under the null hypothesis of independence. Since research in correlation analysis also searches for patterns which occur more than expected under the null hypothesis of independence, this paper proposed a framework of changing the original modularity function according to different existing correlation functions in the correlation analysis research area. Such a framework can utilize not only the current but also the future potential research progresses in correlation analysis to advance community detection. In addition, a novel graphical analysis on different modified-modularity functions is conducted to analyze their different preferences, which are also validated by our evaluation on both real life and simulated networks. Our work to connect modularity-based methods with correlation analysis has several significant impacts on the community detection research and its applications to expert and intelligent systems. First, the research progress in correlation analysis can be utilized to define a more effective objective function in community detection for better detection results since different real-life applications might need communities with different resolutions. Second, any existing research progress for the modularity function, such as the Louvain method for speeding up the search and different extensions for overlapping community detection, can be applied in a similar way to the new objective function derived from existing correlation functions, because the new objective function is unified within one framework with the modularity function. Third, our framework opens a large unexplored area for the researchers interested in community detection. For example, what is the best heuristic search method for each different objective function? What are the characteristics of each objective function when applied to overlapping community detection? Among different extensions to overlapping community detection, which extension is better for each objective function?     

基于多模型假设检验的认知无线电频谱感知方法

提出一种基于序贯概率似然比多模型假设检验的认知无线电协作频谱感知方法,用于检测可能含有不同结构和参数不确定性的未知信号.传统的认知无线电协作频谱感知方法(如基于序贯概率似然比的单模型假设检验、M元假设检验等),仅限于处理已知信号分布,不考虑信号分布的不确定性,可能会造成检测误判.所提出方法不仅可以处理认知无线电信号分布模型的不确定性问题,而且可以得到满足错误概率约束的有效检测.对频谱感知的一个典型场景进行仿真实验,结果表明所提出基于序贯概率似然比多模型假设检验方法相对于传统方法的检测有效性.     

Testing correct model specification using extreme learning machines

Testing the correct model specification hypothesis for artificial neural network (ANN) models of the conditional mean is not standard. The traditional Wald, Lagrange multiplier, and quasi-likelihood ratio statistics weakly converge to functions of Gaussian processes, rather than to convenient chi-squared distributions. Also, their large-sample null distributions are problem dependent, limiting applicability. We overcome this challenge by applying functional regression methods of Cho et al. [8] to extreme learning machines (ELM). The Wald ELM (WELM) test statistic proposed here is easy to compute and has a large-sample standard chi-squared distribution under the null hypothesis of correct specification. We provide associated theory for time-series data and affirm our theory with some Monte Carlo experiments.     

Small sample improvements in the threshold cointegration test using residual-based moving block bootstrap

A residual-based moving block bootstrap procedure for testing the null hypothesis of linear cointegration versus cointegration with threshold effects is proposed. When the regressors and errors of the models are serially and contemporaneously correlated, our test compares favourably with the Sup LM test proposed by Gonzalo and Pitarakis. Indeed, shortcomings of the former motivated the development of our test. The small sample performance of the bootstrap test is investigated by Monte Carlo simulations, and the results show that the test performs better than the Sup LM test.     

一种基于自然图像统计模型的图像合成检测方法

Nowadays, digital images can be easily tampered due to the availability of powerful image processing software. As digital cameras continue to replace their analog counterparts, the importance of authenticating digital images, identifying their sources, and detecting forgeries is increasing. Blind image forensics is used to analyze an image in the complete absence of any digital watermark or signature. Image compositing is the most common form of digital tampering. Assuming that image compositing operations affect the inherent statistics of the image, we propose an image compositing detection method on based on a statistical model for natural image in the wavelet transform domain. The generalized Gaussian model (GGD) is employed to describe the marginal distribution of wavelet coefficients of images, and the parameters of GGD are obtained using maximum-likelihood estimator. The statistical features include GGD parameters, prediction error, mean, variance, skewness, and kurtosis at each wavelet detail subband. Then, these feature vectors are used to discriminate between natural images and composite images using support vector machine (SVM). To evaluate the performance of our proposed method, we carried out tests on the Columbia Uncompressed Image Splicing Detection Dataset and another advanced dataset, and achieved a detection accuracy of 92% and 79%, respectively. The detection performance of our method is better than that of the method using camera response function on the same dataset.     

Fault detection using multiscale PCA-based moving window GLRT

The presence of measurement errors (noise) in the data and mode l uncertainties degrade the performance quality of fault detection (FD) techniques. Therefore, an objective of this paper is to enhance the quality of FD by suppressing the effect of these errors using wavelet-based multiscale representation of data, which is a powerful feature extraction tool. Multiscale representation of data has been used to improve the FD abilities of principal component analysis. Thus, combining the advantages of multiscale representation with those of hypothesis testing should provide further improvements in FD. To do that, a moving window generalized likelihood ratio test (MW-GLRT) method based on multiscale principal component analysis (MSPCA) is proposed for FD. The dynamical multiscale representation is proposed to extract the deterministic features and decorrelate autocorrelated measurements. An extension of the popular hypothesis testing GLRT method is applied on the residuals from the MSPCA model, in order to further enhance the fault detection performance. In the proposed MW-GLRT method, the detection statistic equals the norm of the residuals in that window, which is equivalent to applying a mean filter on the squares of the residuals. This means that a proper moving window length needs to be selected, which is similar to estimating the mean filter length in data filtering. The fault detection performance of the MSPCA-based MW-GLRT chart is illustrated through two examples, one using synthetic data, and the other using simulated Tennessee Eastman Process (TEP) data. The results demonstrate the effectiveness of the MSPCA-based MW-GLRT method over the conventional PCA-based and MSPCA-based GLRT methods, and both of them provide better performance results when compared with the conventional PCA and MSPCA methods, through their respective charts T² and Q charts.