EM-based detection scheme for differential unitary space-time modulation

The performance loss of an approximately 3 dB signal-to-noise ratio is always paid with conventional differential detection compared to the related coherent detection. A new detection scheme consisting of two steps is proposed for the differential unitary space-time modulation （DUSTM） system. In the first step, the data sequence is estimated by conventional unitary space-time demodulation （DUSTD） and differentially encoded again to produce an initial estimate of the transmitted symbol stream. In the second step, the initial estimate of the symbol stream is utilized to initialize an expectation maximization （EM）-based iterative detector. In each iteration, the most recent detected symbol stream is employed to estimate the channel, which is then used to implement coherent sequence detection to refine the symbol stream. Simulation results show that the proposed detection scheme performs much better than the conventional DUSTD after several iterations.

Online split-and-merge expec tation-maximization training of Gaussian mixture model and its optimization

This paper presents a new online incremental training algorithm of Gaussian mixture model （GMM）, which aims to perform the expectation-maximization（EM） training incrementally to update GMM model parameters online sample by sample, instead of waiting for a block of data with the sufficient size to start training as in the traditional EM procedure. The proposed method is extended from the split-and-merge EM procedure, so inherently it is also capable escaping from local maxima and reducing the chances of singularities. In the application domain, the algorithm is optimized in the context of speech processing applications. Experiments on the synthetic data show the advantage and efficiency of the new method and the results in a speech processing task also confirm the improvement of system performance.

隐马尔科夫模型在三维模型自动分类中的应用

针对三维模型的分类问题,提出了一种基于隐马尔科夫模型(HMM)和最大期望(EM)算法的三维模型自动分类方法。将HMM引入三维模型自动分类问题中使得更多先验知识在分类过程中被利用。算法首先对三维模型进行预处理和组合切分,并提取各切分部分的形状直方图特征。对形状直方图特征进行离散归一化后形成HMM模型在某一时刻的观测值,这些观测值将用来训练HMM参数。HMM参数通过EM算法进行估计。最后通过计算未知模型和各类模型的HMM参数间的最大后验概率,获得三维模型的分类结果。在HMM建模过程中利用HMM本身所具有的时序性来描述三维模型的空间几何结构和局部几何特征。实验表明该方法在三维模型自动分类中有较高的准确率。

面向协同过滤的真实偏好高斯混合模型

针对协同过滤问题,提出了一种基于高斯混合的概率模型,称为真实偏好高斯混合模型.用户对项目的评分由三个因素决定:用户对项目主题和内容的真实偏好,用户的评分习惯,以及项目的公众评价.引入了两个隐含变量,分别用于描述用户类和项目类,用户和项目依概率可以同时属于多个类.模型包括离线建模过程和在线预测过程,在线预测可以在常数时间内完成.实验表明新模型的预测结果明显优于其他几种协同过滤算法.

基于因子分析的隐马尔可夫模型(英文)

状态输出概率密度为对角协方差矩阵高斯分布的隐马尔可夫模型(HMM-DG)在帧内特征相关建模方面存在缺陷.本文将因子分析方法与HMM-DG的混合高斯建模相结合,提出了一种具有弹性的帧内特征相关隐马尔可夫模型框架——基于因子分析的隐马尔可夫模型(HMM-FA),并导出了HMM-FA的训练算法.理论分析和仿真实验都表明:在训练数据相同的条件下,HMM-FA的性能优于HMM-DG.

基于图像序列的人体跟踪

由于人体的非刚体性和人体之间会经常发生遮挡,使得人体跟踪是一个很有挑战性的课题。针对这一特点,提出了用结合卡尔曼滤波和贝叶斯的方法来完成多个人体的跟踪,先建立简单的背景模型,然后用背景差分法得到前景区域,提取运动人体,并用EM(期望-最大化)算法建立相应的人体模型。在人体间没有发生遮挡时,用卡尔曼滤波方法来跟踪各个人体;人体间出现遮挡时,用贝叶斯方法来判别和跟踪相应人体。实验表明,该方法既能保证跟踪的快速性,又能很好地处理人体间相互遮挡的情况,该算法鲁棒性好,跟踪结果令人满意。

Gaussian mixture model clustering with completed likelihood minimum message length criterion

An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.

大数据背景下考虑删失特点的继保设备运行状态评估

未能记录到设备失效准确时间的失效数据称为删失数据。针对大数据背景下继保设备失效数据存在删失的特点,提出了大数据背景下考虑删失数据的继保设备运行状态评估方法。首先,通过对继保设备失效数据进行特点分析,对失效数据进行预处理,利用期望-最大化(EM)算法并结合指数分布和威布尔分布模型,估计继保设备失效模型的参数。其次,将参数估计值代入失效模型中,得到设备的可靠度、故障概率密度、失效率、平均无故障时间等可靠性指标。然后,通过仿真验证对比分析偶然失效期和老化失效期内不同估计方法得到的模型参数精度,验证了文中方法处理删失数据的有效性。最后,以某型号继保设备为例分析可靠性指标,验证了利用文中方法规划设备检修周期的可行性。

基于块稀疏贝叶斯学习的跳频通信梳状干扰抑制

梳状干扰是对跳频(FHSS)通信的一种有效干扰样式,抑制梳状干扰对于确保FHSS通信的有效性至关重要。现有基于奈奎斯特采样定理的梳状干扰抑制方法存在应用中受限于采样率较高的问题。将压缩感知(CS)应用于FHSS通信梳状干扰的抑制,利用FHSS信号与梳状干扰的不同压缩域特性以及梳状干扰在频域表现出的块稀疏特性,建立了基于块稀疏贝叶斯学习(BSBL)框架的FHSS通信梳状干扰抑制模型。利用期望最大化(EM)算法,设计了基于BSBL_EM的FHSS通信梳状干扰抑制算法。该算法利用BSBL_EM算法从压缩采样数据中重构出梳状干扰,进而在时域对消干扰。仿真结果表明:所提方法能够有效地抑制FHSS通信中的梳状干扰,与传统方法相比具有显著优势,干扰抑制效果受干扰强度、干扰梳齿带宽和压缩率变化的影响;相同干扰强度条件下,梳齿带宽越窄,压缩率越大,干扰抑制效果越好。

Improved dark channel image dehazing method based on Gaussian mixture model

To solve the problem of color distortion after dehazing in the sky region by using the classical dark channel prior method to process the hazy images with large regions of sky,an improved dark channel image dehazing method based on Gaussian mixture model is proposed.Firstly,we use the Gaussian mixture model to model the hazy image,and then use the expectation maximization(EM)algorithm to optimize the parameters,so that the hazy image can be divided into the sky region and the non-sky region.Secondly,the sky region is divided into a light haze region,a medium haze region and a heavy haze region according to the different dark channel values to estimate the transmission respectively.Thirdly,the restored image is obtained by combining the atmospheric scattering model.Finally,adaptive local tone mapping for high dynamic range images is used to adjust the brightness of the restored image.The experimental results show that the proposed method can effectively eliminate the color distortion in the sky region,and the restored image is clearer and has better visual effect.

An evolutionary particle filter based EM algorithm and its application

In this paper, an evolutionary recursive Bayesian estimation algorithm is presented, which incorporates the latest observation with a new proposal distribution, and the posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted expectation-maximization algorithm. This step replaces the resampling stage needed by most particle filters and relieves the effect caused by sample impoverishment. A nonlinear tracking problem shows that this new approach outperforms other related particle filters.

基于Box-Cox变换与随机系数回归模型的非线性退化设备剩余寿命预测方法

准确预测退化设备的剩余寿命可以为设备维护管理提供重要信息支撑,进而避免设备运行中发生计划外失效、减少设备运行维护成本。针对工程实际设备广泛存在的非线性退化现象,提出了基于Box-Cox变换与随机系数回归模型的非线性退化设备剩余寿命预测方法。首先,采用Box-Cox变换对非线性退化数据进行线性化处理,在此基础上通过随机系数回归模型构建退化模型,并运用Bayesian理论与蒙特卡洛-期望最大化算法在线更新模型参数;然后,基于随机系数回归模型的特性,推导出剩余寿命的分布函数以及其点估计值;最后,通过数值仿真和锂电池实际退化数据验证所提方法的有效性。

Maximum Likelihood Blind Separation of Convolutively Mixed Discrete Sources

In this paper,a Maximum Likelihood(ML) approach,implemented by Expectation-Maximization(EM) algorithm,is proposed to blind separation of convolutively mixed discrete sources.In order to carry out the expectation procedure of the EM algorithm with a less computational load,the algorithm named Iterative Maximum Likelihood algorithm(IML) is proposed to calculate the likelihood and recover the source signals.An important feature of the ML approach is that it has robust performance in noise environments by treating the covariance matrix of the additive Gaussian noise as a parameter.Another striking feature of the ML approach is that it is possible to separate more sources than sensors by exploiting the finite alphabet property of the sources.Simulation results show that the proposed ML approach works well either in determined mixtures or underdetermined mixtures.Furthermore,the performance of the proposed ML algorithm is close to the performance with perfect knowledge of the channel filters.

Distributions of HLA-A and-B alleles and haplotypes in the Yi ethnic minority of Yunnan, China: relationship to other populations

Objective: To investigate the distributions of human leukocyte antigen (HLA)-A and-B alleles and HLA-A-B haplotypes in the Yi ethnic minority of the Yunnan Province, situated in southwestern China. Methods: DNA typing for HLA-A and-B loci was performed using the polymerase chain reaction-sequence-based typing (PCR-SBT) method on 114 randomly selected healthy individuals of the Yi population. The allelic frequencies of HLA-A and-B loci were calculated by direct counting and HLA-A-B haplotypes were estimated using the expectation maximization algorithm. Results: A total of 17 HLA-A and 38 HLA-B alleles were found in the Yi population. The most frequent alleles were A2402 (32.46%), A1101 (26.32%), and A0203 (10.09%) at the HLA-A locus and B4601 (12.28%), B1525 (10.09%), B4001 (8.77%), and B3802 (7.89%) at the HLA-B locus. The predominant HLA-A-B haplotypes were A2402-B1525 (7.86%) and A0203-B3802 (5.64%), followed by A1101-B4001 (4.69%). Phylogenetic analysis indicates that the Yi population in the Honghe, Yunnan Province of China basically belongs to groups of southeastern Asian origin, but shares some characteristics with northeastern Asian groups. Conclusion: The present study may add to the understanding of HLA polymorphism in the Yi ethnic group that was poorly defined previously, and provide useful information for bone marrow transplantation, anthropological research, and forensic sciences as well as for disease-association studies.

Bayesian operational modal analysis of a long-span cable-stayed sea-crossing bridge

Sea-crossing bridges have attracted considerable attention in recent years as an increasing number of projects have been constructed worldwide.Situated in the coastal area,sea-crossing bridges are subjected to a harsh environment(e.g.strong winds,possible ship collisions,and tidal waves)and their performance can deteriorate quickly and severely.To enhance safety and serviceability,it is a routine process to conduct vibration tests to identify modal properties(e.g.natural frequencies,damping ratios,and mode shapes)and to monitor their long-term variation for the purpose of early-damage alert.Operational modal analysis(OMA)provides a feasible way to investigate the modal properties even when the cross-sea bridges are in their operation condition.In this study,we focus on the OMA of cable-stayed bridges,because they are usually long-span and flexible to have extremely low natural frequencies.It challenges experimental capability(e.g.instrumentation and budgeting)and modal identification techniques(e.g.low frequency and closely spaced modes).This paper presents a modal survey of a cable-stayed sea-crossing bridge spanning 218 m+620 m+218 m.The bridge is located in the typhoon-prone area of the northwestern Pacific Ocean.Ambient vibration data was collected for 24 h.A Bayesian fast Fourier transform modal identification method incorporating an expectation-maximization algorithm is applied for modal analysis,in which the modal parameters and associated identification uncertainties are both addressed.Nineteen modes,including 15 translational modes and four torsional modes,are identified within the frequency range of[0,2.5 Hz].

一种有限混合模型对无监督文本聚类的广义方法

提出一种有限混合模型对无监督文本聚类的广义方法.它将特征对各混合成员的相关性作为隐变量引入混合模型,在一个统一框架中完成混合模型的模型选择、特征选择以及参数估计.在大规模文本数据集上的实验结果表明该方法在模型选择、特征选择和聚类结果3个方面都取得较好效果.

A new constrained maximum margin approach to discriminative learning of Bayesian classifiers

We propose a novel discriminative learning approach for Bayesian pattern classification, called ＇constrained maximum margin （CMM）＇. We define the margin between two classes as the difference between the minimum decision value for positive samples and the maximum decision value for negative samples. The learning problem is to maximize the margin under the con- straint that each training pattern is classified correctly. This nonlinear programming problem is solved using the sequential un- constrained minimization technique. We applied the proposed CMM approach to learn Bayesian classifiers based on Gaussian mixture models, and conducted the experiments on 10 UCI datasets. The performance of our approach was compared with those of the expectation-maximization algorithm, the support vector machine, and other state-of-the-art approaches. The experimental results demonstrated the effectiveness of our approach.