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Time series modeling of surface EMG based hand manipulation identification via expectation maximization algorithm

NEUROCOMPUTING 2015年 168卷 661-668页

作者： Lu, Yang Ju, Zhaojie Liu, Yurong Shen, Yuxuan Liu, Honghai Heilongjiang Bayi Agr Univ Coll Informat Technol Daqing 163319 Peoples R China Univ Portsmouth Sch Comp Intelligent Syst & Biomed Robot Grp Portsmouth PO1 3HE Hants England Yangzhou Univ Dept Math Yangzhou 225002 Jiangsu Peoples R China King Abdulaziz Univ Fac Engn Commun Syst & Networks CSN Res Grp Jeddah 21589 Saudi Arabia Northeast Petr Univ Coll Elect & Informat Engn Daqing 163318 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China

In this paper, we focus on the method of employing the expectation maximization (EM) algorithm to the modeling of surface electromyography (sEMG) signals based on hand manipulations via available time series of the measured data. The model for the sEMG is developed as a hidden Markov model (HMM) framework. In order to represent dynamical characteristics of sEMG when multichannel observation sequence are given, a stochastic dynamic process is included in it based on the maximum likelihood estimation (MLE) principle. By using the EM algorithm, the hidden model parameters and the feature of the signal can be identified easily. Ten people of different time series data sets of different hand grasps and in-hand manipulations captured from different subjects are collected. The two different classifiers were used to recognize these hand manipulation signal. Compared with time and time-frequency domains and their combination feature, the proposed algorithm of the inferred model gains better performance and demonstrates the effectiveness. The average identification accuracy rate is 93% and the maximum classification ratio is 100%. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Surface electromyography recognition Hidden Markov model expectation maximization algorithm Time series data

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Stochastic dynamic modeling of lithium battery via expectation maximization algorithm

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NEUROCOMPUTING 2016年第PartA期175卷 421-426页

作者： Wang, Wu Liu, Xiaocheng Cai, Fenghuang Wang, Jianming Fuzhou Univ Coll Elect Engn & Automat Fuzhou 350116 Fujian Peoples R China Fuzhou Univ Res Ctr Adv Proc Control Fuzhou 350116 Fujian Peoples R China

Lithium battery is a reliable source for mobile, computers and electric vehicles. However, the internal chemical reaction of lithium battery is complex and susceptible to external influences, such that the traditional model-driven approach cannot model it accurately. In this paper, based on the data-driven approach, an expectation maximization algorithm is proposed to model a class of lithium battery. By using the expectation maximization algorithm, the model parameters and actual values of test, as well as the noise intensity can be identified simultaneously. The NASA battery data sets are employed to demonstrate the effectiveness of the proposed algorithm. Several indices are presented to evaluate the inferred lithium battery models. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Lithium battery Data-driven approach expectation maximization algorithm Stochastic dynamic model

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Blind source separation with time series variational Bayes expectation maximization algorithm

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DIGITAL SIGNAL PROCESSING 2012年第1期22卷 17-33页

作者： Sun, Shijun Peng, Chenglin Hou, Wensheng Zheng, Jun Jiang, Yingtao Zheng, Xiaolin Zhanjiang Normal Coll Sch Phys Sci & Technol Zhanjiang 524048 Guangdong Peoples R China Chongqing Univ Bioengn Coll Chongqing 400030 Peoples R China New Mexico Inst Min & Technol Dept Comp Sci Socorro NM 87801 USA Univ Nevada Dept Elect & Comp Engn Las Vegas NV 89154 USA

This paper presents a variational Bayes expectation maximization algorithm for time series based on Attias' variational Bayesian theory. The proposed algorithm is applied in the blind source separation (BSS) problem to estimate both the source signals and the mixing matrix for the optimal model structure. The distribution of the mixing matrix is assumed to be a matrix Gaussian distribution due to the correlation of its elements and the inverse covariance of the sensor noise is assumed to be Wishart distributed for the correlation between sensor noises. The mixture of Gaussian model is used to approximate the distribution of each independent source. The rules to update the posterior hyperparameters and the posterior of the model structure are obtained. The optimal model structure is selected as the one with largest posterior. The source signals and mixing matrix are estimated by applying LMS and MAP estimators to the posterior distributions of the hidden variables and the model parameters respectively for the optimal structure. The proposed algorithm is tested with synthetic data. The results show that: (1) the logarithm posterior of the model structure increases with the accuracy of the posterior mixing matrix: (2) the accuracies of the prior mixing matrix, the estimated mixing matrix, and the estimated source signals increase with the logarithm posterior of the model structure. This algorithm is applied to Magnetoencephalograph data to localize the source of the equivalent current dipoles. (C) 2010 Elsevier Inc. All rights reserved.

关键词： Time series Variational Bayes expectation maximization algorithm Blind source separation

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expectation maximization algorithm for a Battery State of Health Model with Missing Data

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JOURNAL OF THE ELECTROCHEMICAL SOCIETY 2023年第11期170卷 110518-110518页

作者： Han, Dongqi Wuxi Inst Technol Wuxi Peoples R China

With the popularity of lithium-ion batteries, battery state of health (SOH) estimation has become one of the current research hotspots. Due to network congestion, collected data usually encounter time-delay or packet loss. In this paper, an expectation maximization (EM) algorithm is proposed for the SOH model which is approximated by a polynomial model. Based on the EM method, the missing data are computed in the E step, and the parameters are updated in the M step. Compared with the least square method, the proposed algorithm has more accurate estimation accuracy. The simulation example shows the effectiveness of the proposed algorithm.

关键词： parameter estimation state of health missing data expectation maximization algorithm

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An expectation-maximization algorithm-based Autoregressive Model for the Fuzzy Job Shop Scheduling Problem

arXiv

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arXiv 2025年

作者： Wang, Yijian Guo, Tongxian Liu, Zhaoqiang The School of Computer Science and Engineering University of Electronic Science and Technology of China China

The fuzzy job shop scheduling problem (FJSSP) emerges as an innovative extension to the job shop scheduling problem (JSSP), incorporating a layer of uncertainty that aligns the problem more closely with the complexities of real-world manufacturing environments. This improvement increases the computational complexity of deriving the solution while improving its applicability. In the domain of deterministic scheduling, neural combinatorial optimization (NCO) has recently demonstrated remarkable efficacy. However, its application to the realm of fuzzy scheduling has been relatively unexplored. This paper aims to bridge this gap by investigating the feasibility of employing neural networks to assimilate and process fuzzy information for the resolution of FJSSP, thereby leveraging the advancements in NCO to enhance fuzzy scheduling methodologies. To achieve this, we approach the FJSSP as a generative task and introduce an expectation-maximization algorithm-based autoregressive model (EMARM) to address it. During training, our model alternates between generating scheduling schemes from given instances (E-step) and adjusting the autoregressive model weights based on these generated schemes (M-step). This novel methodology effectively navigates around the substantial hurdle of obtaining ground-truth labels, which is a prevalent issue in NCO frameworks. In testing, the experimental results demonstrate the superior capability of EMARM in addressing the FJSSP, showcasing its effectiveness and potential for practical applications in fuzzy scheduling. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

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Image fusion based on an expectation maximization algorithm

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OPTICAL ENGINEERING 2005年第7期44卷 077001-1-077001页

作者： Liu, G Jing, ZL Sun, SY Shanghai Jiao Tong Univ Inst Aerosp Informat & Control Sch Elect Informat & Elect Engn Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Inst Aerosp Sci & Technol Shanghai 200030 Peoples R China

A novel image fusion method based on an expectation maximization (EM) algorithm and the discrete wavelet frame (DWF) transform is proposed. The registered images are first decomposed using the DWF transform, which is both aliasing-free and translation-invariant. The DWF decomposes the image signal into a multiresolution representation with both low-frequency coarse information and high-frequency detail information. The EM algorithm is used to fuse the low-frequency coarse information of the registered images. The informative importance measure is applied to fuse the high-frequency detail information of the registered images. The final fused image is obtained by taking the inverse transform of the composite multiresolution representations. Simulation results show that the proposed method outperforms the conventional image fusion methods. (c) 2005 Society of Photo-Optical Instrumentation Engineers.

关键词： information fusion image fusion discrete wavelet frame transform expectation maximization algorithm retinal process

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An expectation maximization algorithm for the hidden markov models with multiparameter student-t observations

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COMPUTATIONAL STATISTICS 2024年第6期39卷 3287-3301页

作者： Ghorbel, Emna Louati, Mahdi ESPRIT Sch Engn ZI Chotrana II Tunis 2083 Tunisia Digital Res Ctr Sfax Lab Signals Syst Artificial Intelligence & Network Sfax 3021 Tunisia

Hidden Markov models are a class of probabilistic graphical models used to describe the evolution of a sequence of unknown variables from a set of observed variables. They are statistical models introduced by Baum and Petrie in Baum (JMA 101:789-810) and belong to the class of latent variable models. Initially developed and applied in the context of speech recognition, they have attracted much attention in many fields of application. The central objective of this research work is upon an extension of these models. More accurately, we define multiparameter hidden Markov models, using multiple observation processes and the Riesz distribution on the space of symmetric matrices as a natural extension of the gamma one. Some basic related properties are discussed and marginal and posterior distributions are derived. We conduct the Forward-Backward dynamic programming algorithm and the classical expectation maximization algorithm to estimate the global set of parameters. Using simulated data, the performance of these estimators is conveniently achieved by the Matlab program. This allows us to assess the quality of the proposed estimators by means of the mean square errors between the true and the estimated values.

关键词： Hidden markov models expectation maximization algorithm Forward-backward programming algorithm Multiparameter t'distribution Wishart distribution Riesz distribution

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An Improved Brain MRI Segmentation Method Based on Scale-Space Theory and expectation maximization algorithm 16th

An Improved Brain MRI Segmentation Method Based on Scale-Spa...

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16th Pacific-Rim Conference on Multimedia (PCM)

作者： Song, Yuqing Bao, Xiang Liu, Zhe Yuan, Deqi Song, Minshan Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang Peoples R China Zhenjiang First Peoples Hosp New Dist Branch Zhenjiang Peoples R China

ISBN: (纸本)9783319240787;9783319240770

expectation maximization (EM) algorithm is an unsupervised clustering algorithm, but initialization information especially the number of clusters is crucial to its performance. In this paper, a new MRI segmentation method based on scale-space theory and EM algorithm has been proposed. Firstly, gray level density of a brain MRI is estimated;secondly, the corresponding fingerprints which include initialization information for EM using scale-space theory are obtained;lastly, segmentation results are achieved by the initialized EM. During the initialization phase, restrictions of clustering component weights decrease the influence of noise or singular points. Brain MRI segmentation results indicate that our method can determine more reliable initialization information and achieve more accurate segmented tissues than other initialization methods.

关键词： expectation maximization algorithm Initialization information Scale-space theory Fingerprints Brain MRI segmentation

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Modified expectation maximization algorithm for MRI Segmentation

Modified Expectation Maximization Algorithm for MRI Segmenta...

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15th Iberoamerican Congress on Pattern Recognition

作者： Donoso, Ramiro Veloz, Alejandro Allende, Hector Univ Tecn Federico Santa Maria Dept Informat Valparaiso Chile

ISBN: (纸本)9783642166860

Magnetic Resonance Image segmentation is a fundamental task in a wide variety of computed-based medical applications that support therapy, diagnostic and medical applications. In this work, spatial information is included for estimating paramaters of a finite mixture model, with gaussian distribution assumption, using a modified version of the well-know expectation maximization algorithm proposed in [3]. Our approach is based on aggregating a transition step between E-step and M-step, that includes the information of spatial dependences between neighboring pixels. Our proposal is compared with other approaches proposed in the image segmentation literature using the size and shape test, obtaining accurate and robust results in the presence of noise.

关键词： expectation maximization algorithm Finite Mixture models spatial information Magnetic Resonance Imaging segmentation

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Microarray Images Processing Using the Offset Vector Field and expectation maximization algorithm

Microarray Images Processing Using the Offset Vector Field a...

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4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE)

作者： Weng Guirong Su Jian Soochow Univ Sch Mech & Elect Engn Suzhou 215021 Peoples R China

ISBN: (纸本)9781424447138

DNA microarrays provide a simple tool to identify and quantify the gene expression for tens of thousands of genes simultaneously. Image processing is an important step in microarrays experiments. This paper presents a novel technique for removing gene's noises based on the offset vector field and segmenting genes using the expectation maximization algorithm. Simulations show that the new technique for microarray images filtering and segmentation has better performance than most of the common ways. The results of experiments are computationally attractive, have excellent performance and can preserve spots' data while efficiently suppress noises.

关键词： DNA microarrays offset vector field expectation maximization algorithm image processing

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