Statistical Inference of the Generalized Inverted Exponential Distribution under Joint Progressively Type-II Censoring

In this paper, we study the statistical inference of the generalized inverted exponential distribution with the same scale parameter and various shape parameters based on joint progressively type-II censored data. The expectation maximization (EM) algorithm is applied to calculate the maximum likelihood estimates (MLEs) of the parameters. We obtain the observed information matrix based on the missing value principle. Interval estimations are computed by the bootstrap method. We provide Bayesian inference for the informative prior and the non-informative prior. The importance sampling technique is performed to derive the Bayesian estimates and credible intervals under the squared error loss function and the linex loss function, respectively. Eventually, we conduct the Monte Carlo simulation and real data analysis. Moreover, we consider the parameters that have order restrictions and provide the maximum likelihood estimates and Bayesian inference.     

Statistical Inference of Inverted Exponentiated Rayleigh Distribution under Joint Progressively Type-II Censoring

Inverted exponentiated Rayleigh distribution is a widely used and important continuous lifetime distribution, which plays a key role in lifetime research. The joint progressively type-II censoring scheme is an effective method used in the quality evaluation of products from different assembly lines. In this paper, we study the statistical inference of inverted exponentiated Rayleigh distribution based on joint progressively type-II censored data. The likelihood function and maximum likelihood estimates are obtained firstly by adopting Expectation-Maximization algorithm. Then, we calculate the observed information matrix based on the missing value principle. Bootstrap-p and Bootstrap-t methods are applied to get confidence intervals. Bayesian approaches under square loss function and linex loss function are provided respectively to derive the estimates, during which the importance sampling method is introduced. Finally, the Monte Carlo simulation and real data analysis are performed for further study.     

正则化参数求解方法研究

针对BP神经网络算法训练过程中出现的过拟合问题,提出了利用一阶原点矩,二阶原点矩,方差和极大似然估计概念的推广来计算L2正则化中正则化参数 值的方法。该方法通过对算法数据集[X,Y]中的X矩阵进行运算得到的四个 值,BP神经网络算法训练时通常采用的是贝叶斯正则化方法,贝叶斯正则化方法存在着对先验分布和数据分布依赖等问题,而利用上述概念的推广计算的参数代入L2正则化的方法简便没有应用条件限制。在BP神经网络手写数字识别的实验中,将该方法与贝叶斯正则化方法应用到实验中后的算法识别结果进行比较,正确率提高了1.14-1.50个百分点。因而计算得到的 值应用到L2正则化方法与贝叶斯正则化方法相比更能使得BP神经网络算法的泛化能力强,证明了该算法的有效性。     

A maximum likelihood approach to wavefront sensing

A novel approach to two-dimensional wavefront sensing based on a maximum likelihood algorithm is introduced. The data used is the same as the Misell type algorithm i.e. two or more intensity patterns near the focus. The algorithm developed is iterative but requires only a forward transformation not an inverse transformation. This means that the method has the advantage of not being restricted to low numerical aperture cases. The feasibility of the method is demonstrated through numerical simulations. These demonstrate that compared with the Misell algorithm, the maximum likelihood method is relatively insensitive to noise and works well for a wider range of chosen defocus settings. This latter feature is particularly important for a practical application of the technique for lab-based testing of lenses. Results of applying the method to experimental data are also presented.     

噪声方差未知时的分布式量化估计融合方法

为了解决观观测噪声和信道噪声概率分布不完全已知时的多传感器分布式量化估计融合问题,提出了一种期望极大化算法(EM算法)的分布式量化估计融合方法。该方法将未知的噪声参数以及局部量化器量化概率建模为EM算法中二元高斯混合模型参数,利用极大似然估计方法的估计不变性得到目标参数的估计融合结果。仿真实验结果表明:该方法在局部传感器观测样本数目大于6000和信噪比大于6 dB时与已有理想信道条件下的估计方法性能相当。本文方法对水下分布式协同探测问题提供了一种简化的估计融合实现途径。      

IMPROVED DAMAGE IDENTIFICATION METHOD BASED ON MODAL INFORMATION

In this paper, a newly derived algorithm to predict locations and severities of damage in structures using changes in modal characteristics is presented. First, two existing algorithms of damage detection are reviewed and the new algorithm is formulated in order to improve the accuracy of damage localization and severity estimation by eliminating erratic assumptions and limits in the existing algorithms. Next, the damage prediction accuracy is numerically assessed for each algorithm when applied to a two-span continuous beam for which pre- and post-damage modal parameters are available for only a few modes of vibration. Compared to the existing damage detection algorithms, the new algorithm improved the accuracy of damage localization and severity estimation results in the test beam.     

Measuring clock jumps using pulsar timing

In this paper,we investigate the statistical signal-processing algorithm to measure the instant local clock jump from the timing data of multiple pulsars.Our algorithm is based on the framework of Bayesian statistics.In order to make the Bayesian algorithm applicable with limited computational resources,we dedicated our efforts to the analytic marginalization of irrelevant parameters.We found that the widely used parameter for pulsar timing systematics,the"Efac"parameter,can be analytically marginalized.This reduces the Gaussian likelihood to a function very similar to the Student’s t-distribution.Our iterative method to solve the maximum likelihood estimator is also explained in the paper.Using pulsar timing data from the Yunnan Kunming 40-m radio telescope,we demonstrate the application of the method,where 80-ns level precision for the clock jump can be achieved.Such a precision is comparable to that of current commercial time transferring service using satellites.We expect that the current method could help developing the autonomous pulsar time scale.     

Statistical Inference of Truncated Normal Distribution Based on the Generalized Progressive Hybrid Censoring

In this paper, the parameter estimation problem of a truncated normal distribution is discussed based on the generalized progressive hybrid censored data. The desired maximum likelihood estimates of unknown quantities are firstly derived through the Newton–Raphson algorithm and the expectation maximization algorithm. Based on the asymptotic normality of the maximum likelihood estimators, we develop the asymptotic confidence intervals. The percentile bootstrap method is also employed in the case of the small sample size. Further, the Bayes estimates are evaluated under various loss functions like squared error, general entropy, and linex loss functions. Tierney and Kadane approximation, as well as the importance sampling approach, is applied to obtain the Bayesian estimates under proper prior distributions. The associated Bayesian credible intervals are constructed in the meantime. Extensive numerical simulations are implemented to compare the performance of different estimation methods. Finally, an authentic example is analyzed to illustrate the inference approaches.     

基于多帧背景的泄漏气体自适应匹配滤波检测

被动傅里叶变换红外(FTIR)扫描遥测成像系统采集的红外高光谱图像具有空间、光谱等维度信息,可被用于大气环境中有毒有害气体的识别、定量及可视化。该系统具有光谱分辨率高、非接触式及远距离探测等优点,然而其单帧图像的像元数量少且部分存在气体吸收或发射特征,无法直接用于红外高光谱图像的目标检测。提出了基于多帧背景的泄漏气体自适应匹配滤波(AMF)检测方法,以短时间内、同一区域的多帧红外高光谱图像为基础,筛选出无目标气体特征的背景光谱并计算探测区域的背景最大似然估计,应用于后续帧的目标气体泄漏检测。红外高光谱图像来自于SF₆气体的遥测实验,共扫描四帧（120像元/帧）,去除前三帧内含有目标气体特征的像元光谱,剩余背景光谱被用于计算背景的最大似然估计,第四帧红外高光谱图像逐像元对SF₆气体进行的AMF检测,并与非线性最小二乘法反演的SF₆柱浓度图像比对,结果表明AMF检测高值与柱浓度高值有较强的相关性。为验证多帧背景在不同空间检测方法下的性能,分别对该帧数据进行了基于正交子空间的自适应子空间检测(ASD)、基于混合空间的自适应余弦检测(ACE)及基于斜子空间的最大似然比检测(OGLRT),并分别与SF₆柱浓度图像比对,结果表明多帧背景适用于不同空间的检测方法。此外,为验证存在目标气体吸收特征的非背景光谱对背景空间的影响,向背景空间中加入多条含有SF₆气体吸收特征的光谱,通过ROC曲线检验,结果表明背景空间中混入目标气体特征会降低AMF方法的检测性能。AMF检测值的假彩色图像也能应用于被动FTIR扫描遥测成像系统,相较于柱浓度假彩色图像,泄漏源及扩散趋势更为明显。基于红外高光谱图像的检测方法依赖于整体背景的统计特性,相较于单像元光谱波段的反演算法,极大地降低了背景的依赖性。多帧背景下的AMF泄漏气体检测方法能很好地应用于被动FTIR扫描遥测成像系统上并满足在线监测要求。     

Extreme Value Theory in Application to Delivery Delays

This paper uses the Extreme Value Theory (EVT) to model the rare events that appear as delivery delays in road transport. Transport delivery delays occur stochastically. Therefore, modeling such events should be done using appropriate tools due to the economic consequences of these extreme events. Additionally, we provide the estimates of the extremal index and the return level with the confidence interval to describe the clustering behavior of rare events in deliveries. The Generalized Extreme Value Distribution (GEV) parameters are estimated using the maximum likelihood method and the penalized maximum likelihood method for better small-sample properties. The findings demonstrate the advantages of EVT-based prediction and its readiness for application.     

Radar sensor network for target detection using Chernoff information and relative entropy

In this paper, we propose to apply information theory to Ultra wide band (UWB) radar sensor network (RSN) to detect target in foliage environment. Information theoretic algorithms such as Maximum entropy method (MEM) and mutual information are proven methods, that can be applied to data collected by various sensors. However, the complexity of the environment poses uncertainty in fusion center. Chernoff information provides the best error exponent of detection in Bayesian environment. In this paper, we consider the target detection as binary hypothesis testing and use Chernoff information as sensor selection criterion, which significantly reduces the processing load. Another strong information theoretic algorithm, method of types, is applicable to our MEM based target detection algorithm as entropy is dependent on the empirical distribution only. Method of types analyzes the probability of a sequence based on empirical distribution. Based on this, we can find the bound on probability of detection. We also propose to use Relative entropy based processing in the fusion center based on method of types and Chernoff Stein Lemma. We study the required quantization level and number of nodes in gaining the best error exponent. The performance of the algorithms were evaluated, based on real world data.     

Modified Maximum Entropy Method and Estimating the AIF via DCE-MRI Data Analysis

Background: For the kinetic models used in contrast-based medical imaging, the assignment of the arterial input function named AIF is essential for the estimation of the physiological parameters of the tissue via solving an optimization problem. Objective: In the current study, we estimate the AIF relayed on the modified maximum entropy method. The effectiveness of several numerical methods to determine kinetic parameters and the AIF is evaluated—in situations where enough information about the AIF is not available. The purpose of this study is to identify an appropriate method for estimating this function. Materials and Methods: The modified algorithm is a mixture of the maximum entropy approach with an optimization method, named the teaching-learning method. In here, we applied this algorithm in a Bayesian framework to estimate the kinetic parameters when specifying the unique form of the AIF by the maximum entropy method. We assessed the proficiency of the proposed method for assigning the kinetic parameters in the dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), when determining AIF with some other parameter-estimation methods and a standard fixed AIF method. A previously analyzed dataset consisting of contrast agent concentrations in tissue and plasma was used. Results and Conclusions: We compared the accuracy of the results for the estimated parameters obtained from the MMEM with those of the empirical method, maximum likelihood method, moment matching (“method of moments”), the least-square method, the modified maximum likelihood approach, and our previous work. Since the current algorithm does not have the problem of starting point in the parameter estimation phase, it could find the best and nearest model to the empirical model of data, and therefore, the results indicated the Weibull distribution as an appropriate and robust AIF and also illustrated the power and effectiveness of the proposed method to estimate the kinetic parameters.     

基于模糊分割的移动自组织网络故障检测算法

针对现有移动自组织网络故障检测算法精度较低、延时较大的问题,提出了一种基于模糊分割的检测算法ANFD(Ad hoc Networks Fault Detection)。ANFD算法以网络数据包与检测消息作为检测工具,通过模糊分割方法对接收到的信息进行处理,最终通过时频域方法进行故障分割,实施最终的故障识别。仿真试验证明,ANFD算法较之其他算法具有较高的故障检测精度、较短的响应延迟,并且具有较低的消息复杂度和内存消耗。     

适合DSP处理的低内存并行SPIHT算法

针对SPIHT(set partitioning in hierarchical trees)算法的编码过程具有重复运算、存储量大等问题,提出了一种适合于DSP(digital signal processors)处理的低内存并行SPIHT算法。该算法采用乒乓缓存策略,使得数据的传输和编码能够同时进行。通过引入基于行的整型提升方案,使得只需经少量行变换就能进行列变换,提高了小波的变换速度。根据DSP的并行特性和SPIHT算法的缺点,采用“改进的最大幅值求取方法”、“误差位数以及绝对零值和绝对零集合”、“最大值与零值图”和“单棵零树编码”等多种方法对其进行了改进,大大缓解了对内存的压力,减少了算法的运算量。该算法与LZC(listless zerotree coding)算法相比,重构图像的峰值信噪比相当,但速度提高了2倍,能满足一般的实时压缩要求。     

Near optimal performance joint semi-blind channel estimation and data detection techniques for Alamouti coded single-carrier (SC) MIMO communication systems

In this paper, two novel joint semi-blind channel estimation and data detection techniques are proposed and investigated for Alamouti coded single-carrier (SC) multiple-input multiple-output (MIMO) communication system using Rayleigh flat fading channel model. In the first novel semi-blind technique, blind channel estimation can be performed by using singular value decomposition (SVD) of received output autocorrelation matrix and training based channel estimation for orthogonal training symbols can be performed by using orthogonal pilot maximum likelihood (OPML) algorithm. Further using, that semi-blind channel estimate and received output, data detection is performed by using Maximum likelihood (ML) detection. Finally we derived new training symbols from error covariance matrix of estimated data and known orthogonal training symbols, which further applied to OPML algorithm for final channel estimate. In the second novel semi-blind technique, blind channel estimation can be performed by using matrix triangularization based on householder QR decomposition (H-QRD) of received output autocorrelation matrix instead of SVD decomposition. Other steps are same as the first novel technique to calculate data detection and final channel estimation. Simulation results are presented under 2-PSK, 4-PSK, 8-PSK and 16-QAM data modulation schemes using 2 transmitters and different combinations of receiver antennas to investigate the performances of novel techniques compare to conventional whitening rotation (WR) and rotation optimization maximum likelihood (ROML) based semi-blind channel estimation techniques. Result demonstrates that novel techniques outperform others by achieving near optimal performance.     

基于多检测器最大熵融合的多通道光谱图像异常检测

提出了一种多检测器最大熵融合的多通道光谱图像异常检测算法.选择多个不同的异常检测器,并利用自适应窗宽非参核密度估计方法估计其各自的输出分布,保留了多通道光谱图像数据的“长尾”特性,且避免了先验模型假设带来的模型误差.将各原始检测器的输出投影到具有标准正态边缘分布的变换空间中,利用变换空间中模型化的最大熵融合规则实现多检测器的决策级最优概率融合.在原数据空间通过似然函数的检验完成多通道光谱图像的目标检测.利用机载EPS-A航拍多通道光谱图像进行了实验,实验结果表明了算法的有效性.     

基于互信息熵-近红外光谱的过程模式故障检测

近红外光谱分析在工业过程故障检测方面具有独特的优势,是一种准确且高效的方法。结合互信息熵和传统的主成分分析,对近红外光谱特征信息进行提取,通过构建过程的模式来刻画工业过程的运行状态。利用近红外光谱数据,从有机分子含氢基团振动信息中获取工业系统的过程模式,从微观分子层面探索提高工业过程故障检测准确率的有效方法,结合贝叶斯统计学习技术,提出了基于近红外光谱数据的工业过程故障检测技术。针对近红外光谱信息量丰富,谱带较宽,特征性不强的特点,首先对工业过程不同运行状态下的近红外光谱吸光度数据进行一阶导数预处理,采用主成分分析法（principal component analysis,PCA）压缩光谱数据量,扩大不同运行状态下光谱特征信息的差异性,提取光谱的内部特征信息。然后采用互信息熵（mutual information entropy,MIE）作为光谱特征信息相关性度量函数,基于最小冗余最大相关算法进一步减少光谱特征信息间的冗余并最大化光谱特征信息与类别的相关性,弥补了PCA无监督特征波长选择的不足,提出一种基于PCA-MIE的过程模式构建方法,获得的过程模式子集更紧凑更具类别表现力。再利用贝叶斯统计学习算法,根据后验概率对构建的模式子集进行决策,判别生产过程的正常状态和故障状态。由于过程模式子集结合了PCA浓聚方差的优势和互信息熵相关性测度的特征信息选择方法,蕴含了更多的近红外光谱的本质信息与内在规律,从而更能刻画工业过程的运行状态。接着,设置测试准确率TA作为评估标准,用以评价故障检测方法的性能效果。最后利用某化工厂提供的原油脱盐脱水过程近红外光谱数据对所提方法进行验证,并与传统近红外光谱特征信息提取方法PCA和MIE方法性能进行对比分析,结果表明基于PCA-MIE的过程模式故障检测方法几乎在所有维数子集上性能都优于其他两种方法,在特征维数为18维时获得最高的准确率94. 6%,证明了方法的优越性。     

一种公共网络攻击数据挖掘智能算法研究

公共网络的开放性和自组织特性导致网络容易受到病毒干扰和入侵攻击,对攻击数据的准确高效挖掘能确保网络安全。传统方法采用时频指向性波束特征聚类方法实现攻击数据挖掘,在信噪比较低时攻击数据准确挖掘概率较低。提出一种基于自适应滤波检测和时频特征提取的公共网络攻击数据挖掘智能算法。首先进行公共网络攻击数据的信号拟合和时间序列分析,对含噪的攻击数据拟合信号进行自适应滤波检测,提高信号纯度,对滤波输出数据进行时频特征提取,实现攻击数据的准确挖掘。仿真结果表明,采用该算法进行网络攻击数据挖掘,对攻击数据特征的准确检测性能较高,对干扰的抑制性能较强,能有效实现网络安全防御。     

Preliminary attempt on maximum likelihood tomosynthesis reconstruction of DEI data

Tomosynthesis is a three-dimension reconstruction method that can remove the effect of superimposition with limited angle projections. It is especially promising in mammography where radiation dose is concerned. In this paper, we propose a maximum likelihood tomosynthesis reconstruction algorithm (ML-TS) on the apparent absorption data of diffraction enhanced imaging (DEI). The motivation of this contribution is to develop a tomosynthesis algorithm in low-dose or noisy circumstances and make DEI get closer to clinic application. The theoretical statistical models of DEI data in physics are analyzed and the proposed algorithm is validated with the experimental data at the Beijing Synchrotron Radiation Facility (BSRF). The results of ML-TS have better contrast compared with the well known `shift-and-add' algorithm and FBP algorithm.

     

A simpler and elegant algorithm for computing fractal dimension in higher dimensional state space

Chaotic systems are now frequently encountered in almost all branches of sciences. Dimension of such systems provides an important measure for easy characterization of dynamics of the systems. Conventional algorithms for computing dimension of such systems in higher dimensional state space face an unavoidable problem of enormous storage requirement. Here we present an algorithm, which uses a simple but very powerful technique and faces no problem in computing dimension in higher dimensional state space. The unique indexing technique of hypercubes, used in this algorithm, provides a clever means to drastically reduce the requirement of storage. It is shown that theoretically this algorithm faces no problem in computing capacity dimension in any dimension of the embedding state space as far as the actual dimension of the attractor is finite. Unlike the existing algorithms, memory requirement offered by this algorithm depends only on the actual dimension of the attractor and has no explicit dependence on the number of data points considered.