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.

The study of a neutron spectrum unfolding method based on particle swarm optimization combined with maximum likelihood expectation maximization

The neutron spectrum unfolding by Bonner sphere spectrometer(BSS) is considered a complex multidimensional model,which requires complex mathematical methods to solve the first kind of Fredholm integral equation. In order to solve the problem of the maximum likelihood expectation maximization(MLEM) algorithm which is easy to suffer the pitfalls of local optima and the particle swarm optimization(PSO) algorithm which is easy to get unreasonable flight direction and step length of particles, which leads to the invalid iteration and affect efficiency and accuracy, an improved PSO-MLEM algorithm, combined of PSO and MLEM algorithm, is proposed for neutron spectrum unfolding. The dynamic acceleration factor is used to balance the ability of global and local search, and improves the convergence speed and accuracy of the algorithm. Firstly, the Monte Carlo method was used to simulated the BSS to obtain the response function and count rates of BSS. In the simulation of count rate, four reference spectra from the IAEA Technical Report Series No. 403 were used as input parameters of the Monte Carlo method. The PSO-MLEM algorithm was used to unfold the neutron spectrum of the simulated data and was verified by the difference of the unfolded spectrum to the reference spectrum. Finally, the 252Cf neutron source was measured by BSS, and the PSO-MLEM algorithm was used to unfold the experimental neutron spectrum.Compared with maximum entropy deconvolution(MAXED), PSO and MLEM algorithm, the PSO-MLEM algorithm has fewer parameters and automatically adjusts the dynamic acceleration factor to solve the problem of local optima. The convergence speed of the PSO-MLEM algorithm is 1.4 times and 3.1 times that of the MLEM and PSO algorithms. Compared with PSO, MLEM and MAXED, the correlation coefficients of PSO-MLEM algorithm are increased by 33.1%, 33.5% and 1.9%, and the relative mean errors are decreased by 98.2%, 97.8% and 67.4%.

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EM-VB的分布式接收运动目标直接符号检测方法

相比于传统分布式组阵接收采用的参数差异估计、信号校准合成以及符号检测的逐级处理结构,直接利用多个观测信号进行符号检测能够抑制信号间校准精度不佳带来的性能损失问题,但现有方法主要针对收发均静止或收发理想同步的情形。研究了一种最大似然准则下的分布式接收运动目标直接符号检测方法,首先给出了直接符号检测求解模型,针对模型中多组未知参数的优化问题,推导分析了各参数近似闭式解,采用基于迭代重估的闭环处理结构,利用多个未知参数和信息符号进行联合寻优。仿真实验结果表明,所提方法性能明显优于传统合成处理方法,与现有联合处理结构相比,在观测站数目较多时具有明显优势。

双边定时截尾下Pareto分布的参数的极大似然估计的EM算法

给出了当寿命分布为Pareto分布时,双边定时截尾寿命试验下形状参数的极大似然估计。由于似然方程形式较复杂,无法得到参数的显式表达式。但可证明此极大似然估计是唯一存在的,并利用EM算法求出了此参数的一种估计。

Asymptotic properties and expectation-maximization algorithm for maximum likelihood estimates of the parameters from Weibull-Logarithmic model

In this article, we consider a lifetime distribution, the Weibull-Logarithmic distri- bution introduced by [6]. We investigate some new statistical characterizations and properties. We develop the maximum likelihood inference using EM algorithm. Asymptotic properties of the MLEs are obtained and extensive simulations are conducted to assess the performance of parameter estimation. A numerical example is used to illustrate the application.

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。

Studies on unfolding energy spectra of neutrons using maximumlikelihood expectation–maximization method

Energy spectra of neutrons are important for identification of unknown neutron sources and for determination of the equivalent dose. Although standard energy spectra of neutrons are available in some situations, e.g., for some radiotherapy treatment machines, they are unknown in other cases, e.g., for photoneutrons created in radiotherapy rooms and neutrons generated in nuclear reactors. In situations where neutron energy spectra need to be determined, unfolding the required neutron energy spectra using the Bonner sphere spectrometer (BSS) and nested neutron spectrometer (NNS) has been found promising. However, without any prior knowledge on the spectra, the unfolding process has remained a tedious task. In this work, a standalone numerical tool named ‘‘NRUunfold’’ was developed which could satisfactorily unfold neutron spectra for BSS or NNS, or any other systems using similar detection methodology. A generic and versatile algorithm based on maximum-likelihood expectation– maximization method was developed and benchmarked against the widely used STAY’SL algorithm which was based on the least squares method. The present method could output decent results in the absence of precisely calculated initial guess, although it was also remarked that employment of exceptionally bizarre initial spectra could lead to some unreasonable output spectra. The neutron count rates computed using the manufacturer’s response functions were used for sensitivity studies. The present NRUunfold code could be useful for neutron energy spectrum unfolding for BSS or NNS applications in the absence of a precisely calculated initial guess.

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.

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.

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.

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.

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.

驾驶疲劳对危险化学品道路运输事故风险的影响规律

近年来,随着危险化学品使用量的急剧攀升,危险化学品道路运输事故率也呈现上升的趋势,且此类事故的发生往往会导致严重后果。为研究危险化学品道路运输事故动态风险变化规律,在修正贝叶斯网络模型基础上,利用2017—2021年历史数据进行机器学习,根据驾驶疲劳程度计算得到“驾驶人行为”动态节点的状态转移概率矩阵,建立基于动态贝叶斯网络(Dynamic Bayesian Network,DBN)的危险化学品道路运输动态风险预测模型并进行推理分析。研究显示:在驾驶3 h内,驾驶人“疲劳驾驶”发生概率随时间推移而增加,但增幅有所下降;在最常见情境下,随驾驶人“疲劳驾驶”概率增加,“侧翻”和“碰撞”事故类型的发生概率明显增加,进而导致“泄漏”事故后果的发生概率有所增加;驾驶人“疲劳驾驶”概率增加会导致“有伤亡事故”发生概率增加,即加重事故的严重程度;在驾驶3 h内,“侧翻”“碰撞”“泄漏”和“有伤亡事故”发生概率的变化趋势与驾驶人“疲劳驾驶”发生概率的变化趋势一致。

考虑车流速度分布扰动的干道绿波最大带宽协调控制

城市车辆速度受多种因素影响呈现某种分布,鲁棒的干道协调控制模型应考虑不同速度下的车辆带宽需求。针对速度波动变化干道传统协调控制方法适用性差的问题,分析了带干扰的干道车辆速度时空分布特征,以推荐速度带宽最大与期望带宽最大为目标,提出了考虑车流速度分布扰动的干道绿波最大带宽协调控制方法。针对佛山市同济路4交叉口干道,速度分布扰动干道协调方法能够提供更符合车辆分布特性的协调方案,比Maxband模型和改进的Maxband模型提升约70%和14%的期望带宽。研究结果表明:笔者方法能够降低Maxband模型平均停车次数25%,平均延误45%和平均排队长度17%;针对不同速度期望和方差情况下的灵敏度分析实验表明,该方法能够为干道提供更具有鲁棒性的干道协调控制方案。

基于稀疏贝叶斯学习的GFDM系统联合迭代信道估计与符号检测

针对当前广义频分复用(Generalized Frequency Division Multiplexing,GFDM)系统时变信道估计精度低的问题,提出基于稀疏贝叶斯学习的GFDM系统联合信道估计与符号检测算法.具体地,采用无干扰导频插入的GFDM多重响应信号模型,在稀疏贝叶斯学习框架下,结合期望最大化算法(Expectation-Maximization,EM)和卡尔曼滤波与平滑算法实现块时变信道的最大似然估计;基于信道状态信息的估计值进行GFDM符号检测,并通过信道估计与符号检测的迭代处理逐步提高信道估计与符号检测的精度.仿真结果表明,所提算法能够获得接近完美信道状态信息条件下的误码率性能,且具有收敛速度快、对多普勒频移鲁棒性高等优点.

遥感图像最大似然分类方法的EM改进算法

基于参数化密度分布模型的最大似然方法 (MLC)是遥感影像分类最常用手段之一 ,与其他非参数方法 (如神经网络 )相比较 ,它具有清晰的参数解释能力、易于与先验知识融合和算法简单而易于实施等优点。但是由于遥感信息的统计分布具有高度的复杂性和随机性 ,当特征空间中类别的分布比较离散而导致不能服从预先假设的分布 ,或者样本的选取不具有代表性 ,往往得到的分类结果会偏离实际情况。首先介绍了用基于有限混合密度理论的期望最大(EM)算法来作为最大似然函数 (MLC)参数估计的方法———EM MLC。该模型首先假设总体混合密度分布可被分解为有限个参数化的高斯密度分布 ,然后把具有先验知识的样本与随机选取的未知样本混合在一起 ,通过EM迭代计算来估计出各密度分布的最大似然函数的参数集 ,从而一定程度上避免了参数估计可能出现的偏离。最后 ,本文提出了基于EM MLC遥感影像分类的具体实施流程和应用示范 ,并与一般最大似然方法 (MLC)得到的分类结果进行了定性和定量的综合比较 ,认为EM

基于似然函数EM迭代的红外与可见光图像配准

为了实现红外与可见光图像的自动配准,提出了基于似然函数EM迭代的图像配准算法。该算法以图像边缘作为配准点特征,将异源图像配准转化为边缘点集配准。通过点集的高斯混合建模建立了点集配准似然函数,以该函数作为目标函数,仿射变换参数作为优化变量,利用EM迭代优化方法进行最优变换参数求解。迭代过程中,引入基于概率密度自适应阈值分割的外点剔除机制,解决了外点对目标函数的干扰问题,实现了边缘点集的精确配准。利用实测的可见光和红外图像进行了算法验证,证明了该算法的有效性。

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。