Kinematic calibration under the expectation maximization framework for exoskeletal inertial motion capture system

This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.

Novel method for extraction of ship target with overlaps in SAR image via EM algorithm

The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.

Parallel Expectation-Maximization Algorithm for Large Databases

A new parallel expectation-maximization （EM） algorithm is proposed for large databases. The purpose of the algorithm is to accelerate the operation of the EM algorithm. As a well-known algorithm for estimation in generic statistical problems, the EM algorithm has been widely used in many domains. But it often requires significant computational resources. So it is needed to develop more elaborate methods to adapt the databases to a large number of records or large dimensionality. The parallel EM algorithm is based on partial Esteps which has the standard convergence guarantee of EM. The algorithm utilizes fully the advantage of parallel computation. It was confirmed that the algorithm obtains about 2.6 speedups in contrast with the standard EM algorithm through its application to large databases. The running time will decrease near linearly when the number of processors increasing.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

基于EM-KF算法的微地震信号去噪方法

针对微地震信号能量较弱,噪声较强,使微地震弱信号难以提取问题,提出了一种基于EM-KF(Expectation Maximization Kalman Filter)的微地震信号去噪方法。通过建立一个符合微地震信号规律的状态空间模型,并利用EM(Expectation Maximization)算法获取卡尔曼滤波的参数最优解,结合卡尔曼滤波,可以有效地提升微地震信号的信噪比,同时保留有效信号。通过合成和真实数据实验结果表明,与传统的小波滤波和卡尔曼滤波相比,该方法具有更高的效率和更好的精度。

基于PSO和MLEM混合算法的NDP测量反演算法研究

中子深度剖面(NDP)分析技术是一种无损检测方法,能够同时测量样品中目标核素的浓度与空间信息,已被广泛应用于锂电池、半导体等产业。在NDP分析过程中,由测量能谱反演出目标核素浓度的分布信息是关键步骤。目前NDP测量反演中常用的算法为最大似然期望最大化(MLEM)算法。针对MLEM算法计算结果易陷入局部最优解的情况,本文提出了粒子群(PSO)与MLEM混合(PSO-MLEM)算法,并通过动态加速因子提高了算法的收敛速度与计算精度。应用PSO-MLEM算法、PSO算法、MLEM算法、奇异值分解求解最小二乘(SVDLS)算法对锂电池中^(6)Li的NDP模拟能谱进行反演,并对反演计算结果进行了评价。结果表明:对比PSO算法,PSO-MLEM算法的收敛效率与计算精度明显提升;对比MLEM算法,PSO-MLEM算法的全局寻优能力有效提升了反演精度,避免了局部最优解的影响;对比SVDLS算法,PSO-MLEM算法的反演精度明显提升。

基于EM自注意力残差的图像超分辨率重建网络

基于深度学习的图像超分辨率(SR)重建方法主要通过增加模型的深度来提升图像重建的质量,但同时增加了模型的计算代价,很多网络利用注意力机制来提高特征提取能力,但难以充分学习到不同区域的特征。为此,提出一种基于期望最大化(EM)自注意力残差的图像超分辨率重建网络。该网络通过改进基础残差块,构建特征增强残差块,以更好地复用残差块中所提取的特征。为增加特征信息在空间上的相关性,引入EM自注意力机制,构建EM自注意力残差模块来增强模型中每个模块的特征提取能力,并通过级联EM自注意力残差模块来构建整个模型的特征提取结构。所获得的特征图通过上采样的图像重建模块获得重建的高分辨率图像。将所提方法与主流方法进行实验对比,结果表明:所提方法在5个流行的SR测试集上能够取得较好的主观视觉效果和更优的性能指标。

Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection

Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity.Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability.Nonetheless,it is Naïve use of the mean data value for the cluster core that presents a major drawback.The chances of two circular clusters having different radius and centering at the same mean will occur.This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together.However,if the clusters are not spherical,it fails.To overcome this issue,a new integrated hybrid model by integrating expectation maximizing(EM)clustering using a Gaussian mixture model(GMM)and naïve Bays classifier have been proposed.In this model,GMM give more flexibility than K-Means in terms of cluster covariance.Also,they use probabilities function and soft clustering,that’s why they can have multiple cluster for a single data.In GMM,we can define the cluster form in GMM by two parameters:the mean and the standard deviation.This means that by using these two parameters,the cluster can take any kind of elliptical shape.EM-GMM will be used to cluster data based on data activity into the corresponding category.

The Fuzzy Modeling Algorithm for Complex Systems Based on Stochastic Neural Network

A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.

Modelling the Survival of Western Honey Bee Apis mellifera and the African Stingless Bee Meliponula ferruginea Using Semiparametric Marginal Proportional Hazards Mixture Cure Model

Classical survival analysis assumes all subjects will experience the event of interest, but in some cases, a portion of the population may never encounter the event. These survival methods further assume independent survival times, which is not valid for honey bees, which live in nests. The study introduces a semi-parametric marginal proportional hazards mixture cure (PHMC) model with exchangeable correlation structure, using generalized estimating equations for survival data analysis. The model was tested on clustered right-censored bees survival data with a cured fraction, where two bee species were subjected to different entomopathogens to test the effect of the entomopathogens on the survival of the bee species. The Expectation-Solution algorithm is used to estimate the parameters. The study notes a weak positive association between cure statuses (ρ1=0.0007) and survival times for uncured bees (ρ2=0.0890), emphasizing their importance. The odds of being uncured for A. mellifera is higher than the odds for species M. ferruginea. The bee species, A. mellifera are more susceptible to entomopathogens icipe 7, icipe 20, and icipe 69. The Cox-Snell residuals show that the proposed semiparametric PH model generally fits the data well as compared to model that assume independent correlation structure. Thus, the semi parametric marginal proportional hazards mixture cure is parsimonious model for correlated bees survival data.

EM算法在Wiener过程随机参数的超参数值估计中的应用

Wiener过程广泛用于产品的性能退化建模,为了便于Bayesian统计推断大都采用随机参数的共轭先验分布。针对目前的二步法得到的超参数先验估计值精度不高的问题,研究了最大期望(expectation maximization,EM)算法在Wiener过程超参数先验估计中的应用。EM算法将随机参数作为隐含变量对先验信息进行整体处理,利用随机参数的期望值代替其估计值,通过Expectation和Maximization组成的递归迭代过程寻找超参数的估计值。仿真实验表明,EM算法相比于二步法提高了估计精度,特别是在采样数量较少时EM算法具有较大的精度优势。GaAs激光器实例应用表明EM算法不但具备很好的收敛性而且有良好的工程应用价值。

基于EM和贝叶斯网络的丢失数据填充算法

实际应用中存在大量的丢失数据的数据集,对丢失数据的处理已成为目前分类领域的研究热点。分析和比较了几种通用的丢失数据填充算法,并提出一种新的基于EM和贝叶斯网络的丢失数据填充算法。算法利用朴素贝叶斯估计出EM算法初值,然后将EM和贝叶斯网络结合进行迭代确定最终更新器,同时得到填充后的完整数据集。实验结果表明,与经典填充算法相比,新算法具有更高的分类准确率,且节省了大量开销。

在小波域中进行图像噪声方差估计的EM方法

提出一种估计图像噪声的方法 ,该方法用混合高斯概率密度模型拟合图像的小波系数中最高频率子带的直方图 ,用 EM算法估计模型的参数 ,选取其中最小的标准方差作为图像噪声标准方差 .用该方法能准确地估计图像高斯噪声的标准方差 ,尤其当图像的噪声比较弱时 ,该方法比传统方法更准确 .

基于快速EM算法和模糊融合的多波段遥感影像变化检测

提出了一种基于快速EM(expectation maximization)算法和模糊融合的多波段遥感影像无监督变化检测方法.该方法首先对各波段差异影像采用基于直方图分析的快速EM迭代算法获取变化分类阈值和变化信息,随后对各波段的变化信息进行模糊融合和判决,生成最终的变化检测图.利用真实的多波段遥感影像进行了实验,本文方法在运行时间和检测效果两个方面都具有优越性.

基于分裂EM算法的GMM参数估计

期望最大化(Expectation Maximization,EM)算法是一种求参数极大似然估计的迭代算法,常用来估计混合密度分布模型的参数。EM算法的主要问题是参数初始化依赖于先验知识且在迭代过程中容易收敛到局部极大值。提出一种新的基于分裂EM算法的GMM参数估计算法,该方法从一个确定的单高斯分布开始,在EM优化过程中逐渐分裂并估计混合分布的参数,解决了参数迭代收敛到局部极值问题。大量的实验表明,与现有的其他参数估计算法相比,算法具有较好的运算效率和估算准确性。

基于EM和GMM相结合的自适应灰度图像分割算法

提出一种阈值自适应、EM方法估计GMM参量的图像分割算法,能够根据图像的内容结合区域和边界两方面的信息自适应地选择阈值,精确地进行图像边界分割.算法首先提取图像的边界,然后根据边界的直方图计算图像的可分割性,由可分割性确定EM方法的阈值进行GMM分割,最后合并图像的近似区域.实验数据表明,相比其它图像分割算法,以及固定阈值的传统EM算法,本算法的分割结果更为准确.

结合EM/MPM算法和Voronoi划分的图像分割方法

为了在模型参数先验分布知识未知情况下实现基于区域和统计的图像分割,并同时获取更加精确的模型参数,提出了一种结合Voronoi划分技术、最大期望值(Expectation Maximization,EM)和最大边缘概率(Maximizationof the Posterior Marginal,MPM)算法的图像分割方法。该方法利用Voronoi划分技术将图像域划分为若干子区域,待分割图像中的同质区域可以由一组子区域拟合而成,并假定各同质区域内像素强度服从同一独立的正态分布,从而建立图像模型,然后结合EM/MPM算法进行图像分割和模型参数估计,其中,MPM算法用于实现面向同质区域的图像分割,EM算法用于估计图像模型参数。为了验证提出的图像分割方法,分别对合成图像和真实图像进行了分割实验,并和传统的基于像素的MRF分割结果进行对比,测试结果的定性和定量分析表明了该方法的有效性和准确性。

一种基于EM算法的快速收敛参数估计方法

将EM算法用于参数估计中,提出了一种在EM算法迭代中使用符号后验概率修正先验概率的快速收敛参数估计方法。通过分析参数估计的CRB与EM算法收敛速率的关系,指出通过降低参数估计的CRB可以提高EM算法的收敛速率。证明了修正之后的算法能加速算法收敛的机理,即降低了缺失数据的熵;同时证明了修正后的算法仍然收敛到修正前的似然函数。最后以载波相位估计为例与传统基于EM算法的相位估计方法进行比较,仿真结果表明,在不影响估计性能的前提下,算法收敛速率明显加快。

基于EM算法的极大似然分布式量化估计融合新方法

该文针对水下目标探测中的多传感器分布式量化估计融合问题,建立了分布式量化估计融合模型,在考虑信道噪声且其统计特性不完全已知条件下,充分利用EM算法在观测数据缺失时参数估计的优越性,提出了一种基于期望极大化(EM)算法的极大似然分布式量化估计融合新方法。该方法将未知的水声信道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。仿真实验表明:该方法在局部传感器观测样本数目大于5000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,该方法为水下目标探测中分布式量化估计融合系统的工程实现提供了理论依据。