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LPV model identification with an unknown scheduling variable in the presence of missing observations - a robust global approach

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IET CONTROL THEORY AND APPLICATIONS 2018年第10期12卷 1465-1473页

作者： Yang, Xianqiang Liu, Xin Han, Boxuan Harbin Inst Technol Res Inst Intelligent Control & Syst Sch Astronaut Harbin 150080 Heilongjiang Peoples R China

This study focuses on identifying the linear parameter varying (LPV) system with an unknown scheduling variable in the presence of missing measurements and the system output data contaminated with outliers. The parameter interpolated LPV autoregressive exogenous (ARX) model with an unknown scheduling variable is considered and the scheduling variable dynamic is described by a non-linear state-space model. The outliers treatment and unknown scheduling variable estimation with missing observations are both taken into consideration. The robust LPV model is established based on the Student's t-distribution in order to handle the outliers and the particle smoother is adopted to estimate the true scheduling variable from incomplete data set. The formulations of the proposed algorithm are finally derived in the expectation-maximisation algorithm scheme and the formulas to estimate the unknown parameters of LPV ARX model and scheduling variable dynamic model are derived simultaneously. A numerical example and a chemical process are used to present the efficacy of the proposed approach.

关键词： nonlinear control systems identification control system synthesis autoregressive processes linear systems state-space methods parameter estimation robust control expectation-maximisation algorithm time-varying systems interpolation LPV model identification missing observations (LPV) system system output data LPV autoregressive exogenous model scheduling variable dynamic state-space model unknown scheduling variable estimation robust LPV model unknown parameters LPV ARX model

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HANKEL-EM-SVD: a hybrid data dropout estimation technique for high voltage partial discharge signals

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2019年第6期13卷 824-835页

作者： Govindarajan, Suganya Subbaiah, Jayalalitha Krithivasan, Kannan Natarajan, Muthukumar SASTRA Deemed Be Univ Sch EEE Thanjavur Tamil Nadu India SASTRA Deemed Be Univ Tata Realty Srinivasa Ramanujam Res Cell Data Sci Lab Thanjavur India

Partial discharge (PD) measurement and characterisation is the most effective method to assess the insulation conditions of equipment for its diagnosis. A significant challenge in this area is PD denoising. Despite recent advancements in denoising algorithms, data drops out because of inappropriate threshold selection during denoising. Processing PD signals with missing data leads to an inaccurate assessment of the insulation conditions, indicating recovering such missed data is a potential research scope under the moonlight. This study addresses the issues of recovering such data dropouts by considering the PD data vector matrix as a low-rank matrix that needs completion by Hankel-matrix decomposition methods. After finding the probabilistic estimate of the missing values using an expectation maximisation algorithm, singular-value decomposition is used to find the actual missing values by soft-thresholding the singular values of the matrix. After testing this technique on simulated PD data, it is implemented to test SASTRA-High-Voltage Laboratory data, showing root mean square error (RMSE) 0.77% and mean absolute error 0.84%. The efficiency of this technique is confirmed when tested with large-sized noise free PD data of 36 samples from the Laboratory of BOLOGNA University (with RMSE varying from 0.15 to 0.31% and mean absolute error varying from 0.29 to 0.6%).

关键词： mean square error methods expectation-maximisation algorithm signal denoising partial discharge measurement partial discharges singular value decomposition HANKEL-EM-SVD high voltage partial discharge signals insulation conditions PD denoising inappropriate threshold selection inaccurate assessment missed data potential research scope PD-data vector-matrix low-rank matrix Hankel-matrix decomposition methods probabilistic estimate expectation maximisation algorithm singular-value decomposition actual missing values singular values simulated PD data SASTRA-High-Voltage Laboratory data root mean square mean absolute error hybrid data dropout estimation technique partial discharge measurement partial discharge characterisation PD signal processing BOLOGNA University

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Coherent compensation and high-resolution technology of multi-band inverse synthetic aperture radar fusion imaging

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IET RADAR SONAR AND NAVIGATION 2021年第2期15卷 167-180页

作者： Zhu, Xiaoxiu Liu, Limin Guo, Baofeng Hu, Wenhua Ma, Juntao Shi, Lin Army Engn Univ Dept Elect & Opt Engn Shijiazhuang Campus Shijiazhuang Hebei Peoples R China

Multi-band inverse synthetic aperture radar (ISAR) fusion imaging technology can effectively improve the range resolution without incurring high hardware cost. The coherent phase between sub-bands is a prerequisite to achieve multi-band ISAR fusion imaging. Here, a joint approach of coherent compensation and high-resolution imaging is proposed to compensate the incoherent phase and obtain high-resolution ISAR fusion images. First, an incoherent phase estimation model based on sparse representation is established, and the phase estimation accuracy is improved by a modified coherent dictionary in case of off-grid. Then, a multi-band ISAR fusion imaging model based on sparse representation is established. The complex Gaussian scale mixture priors and the complex Gaussian priors are imposed on the scatterers and noise, respectively. The solution is derived in the complex domain based on the variational Bayesian expectation maximization framework. The proposed method can not only achieve better incoherent phase compensation in the case of off-grid, but also obtain high-quality ISAR fusion images under low signal-to-noise ratio and low bandwidth sampling ratio. Experimental results verify the effectiveness and robustness of the proposed method based on both numerical simulations and real data.

关键词： high-resolution imaging modified coherent dictionary high-quality ISAR fusion images phase estimation accuracy coherent phase phase estimation multiband inverse synthetic aperture radar fusion imaging technology sparse representation incoherent phase compensation high-resolution ISAR fusion images radar imaging range resolution multiband ISAR fusion imaging model high hardware cost coherent compensation complex Gaussian scale mixture priors radar resolution image fusion incoherent phase estimation model high-resolution technology synthetic aperture radar expectation-maximisation algorithm

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On estimation and influence diagnostics for the Grubbs' model under heavy-tailed distributions

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第4期53卷 1249-1263页

作者： Osorio, Felipe Paula, Gilberto A. Galea, Manuel Univ Valparaiso CIMFAV Dept Estadist Valparaiso 5030 Chile Univ Sao Paulo Inst Matemat & Estatist BR-05508 Sao Paulo Brazil

The Grubbs' measurement model is frequently used to compare several measuring devices. It is common to assume that the random terms have a normal distribution. However, such assumption makes the inference vulnerable to outlying observations, whereas scale mixtures of normal distributions have been an interesting alternative to produce robust estimates, keeping the elegancy and simplicity of the maximum likelihood theory. The aim of this paper is to develop an EM-type algorithm for the parameter estimation, and to use the local influence method to assess the robustness aspects of these parameter estimates under some usual perturbation schemes, In order to identify outliers and to criticize the model building we use the local influence procedure in a Study to compare the precision of several thermocouples. (C) 2008 Elsevier B.V. All rights reserved.

关键词： LINEAR STRUCTURAL RELATIONSHIPS COMPARATIVE CALIBRATION MODELS MIXED-EFFECTS MODELS LOCAL INFLUENCE INCOMPLETE-DATA MAXIMUM-LIKELIHOOD MEASURING DEVICES SCALE MIXTURES T-DISTRIBUTION EM algorithm Diagnosis Thermocouple maximum likelihood expectation-maximisation algorithm measuring instruments Parameter estimation Student's t-distribution Gaussian distribution outliers Robustness

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Clustering and classification of fuzzy data using the fuzzy EM algorithm

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FUZZY SETS AND SYSTEMS 2016年第Mar.1期286卷 134-156页

作者： Quost, Benjamin Denoeux, Thierry Univ Technol Compiegne UMR CNRS Heudiasyc 7253 F-60205 Compiegne France

In this article, we address the problem of clustering imprecise data using a finite mixture of Gaussians. We propose to estimate the parameters of the model using the fuzzy EM algorithm. This extension of the EM algorithm allows us to handle imprecise data represented by fuzzy numbers. First, we briefly recall the principle of the fuzzy EM algorithm. Then, we provide closed-forms for the parameter estimates in the case of Gaussian fuzzy data. We also describe a Monte-Carlo procedure for estimating the parameter updates in the general case. Experiments carried out on synthetic and real data demonstrate the interest of our approach for taking into account attribute and label uncertainty. (C) 2015 Elsevier B.V. All rights reserved.

关键词： expectation-maximisation algorithm Fuzzy Gauss Cluster Analysis

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Automated acute lymphoblastic leukaemia detection system using microscopic images

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IET IMAGE PROCESSING 2019年第13期13卷 2548-2553页

作者： Sukhia, Komal Nain Ghafoor, Abdul Riaz, Muhammad Mohsin Iltaf, Naima NUST Islamabad Pakistan COMSATS Islamabad CAST Islamabad Pakistan

An automatic and novel approach for acute lymphoblastic leukaemia classification is proposed. The proposed scheme is based on pre-processing and segmentation of white blood cell nuclei using expectation maximisation algorithm, feature extraction, feature selection using principal component analysis and classification using sparse representation. The accuracy of the proposed scheme significantly outperforms the existing schemes in terms of acute lymphoblastic leukaemia classification.

关键词： cellular biophysics principal component analysis expectation-maximisation algorithm feature extraction medical image processing image segmentation image classification blood cancer microscopic images automatic approach acute lymphoblastic leukaemia classification white blood cell nuclei expectation maximisation algorithm feature extraction principal component analysis automated acute lymphoblastic leukaemia detection system sparse representation

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BEYOND THE BINARY BOLTZMANN MACHINE

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第5期6卷 1229-1236页

作者： ANDERSON, NH TITTERINGTON, DM Department of Statistics University of Glasgow Glasgow UK

The definition of the usual Boltzmann machine is extended to allow for neurons with polytomous (multicategory) rather than simply binary responses. Updating rules are defined along with the associated stationary distributions, and an alternating minimization method is described for the purposes of training. Emphasis Is placed on the relevance of statistical ideas, including polytomous logistic regression, the iterative proportional fitting procedure and the EM algorithm. The paper builds on previous work in [1] and [2].

关键词： Binary expectation-maximisation algorithm Boltzmann machines Minimization methods

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Multiple model PMHT and its application to the second benchmark radar tracking problem

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2004年第4期40卷 1337-1350页

作者： Ruan, YH Willett, P Univ Connecticut Storrs CT 06269 USA

The probabilistic multiple hypothesis tracker (PMHT) uses the expectation-maximization (EM) algorithm to solve the measurement-origin uncertainty problem. Here we explore some of its variants for maneuvering targets and in particular discuss the multiple model PMHT. We apply this PMHT to the six "typical" tracking scenarios given in the second benchmark problem from Blair et al. [6]. The manner in which the PMHT is used to track the targets and to manage radar allocation is discussed, and the results compared with those of the interacting multiple model probabilistic data association filter (IMM/PDAF) and IMM/MHT (multiple hypothesis tracker). The PMHT works well: its performance lies between those of the IMM/PDAF and IMM/MHT both in terms of tracking performance and computational load.

关键词： probability radar tracking target tracking tracking filters IMM/MHT IMM/PDAF benchmark radar tracking expectation-maximization algorithm interacting multiple model probabilistic data association filter measurement-origin uncertainty problem multiple mode Benchmark testing expectation-maximisation algorithm radar target tracking Target tracking tracking filters

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Unsupervised change detection in SAR images based on locally fitting model and semi-EM algorithm

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2014年第2期35卷 621-650页

作者： Su, Linzhi Gong, Maoguo Sun, Bo Jiao, Licheng Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Change detection in synthetic aperture radar (SAR) images has become increasingly important along with the development of SAR techniques. In this study, a novel locally fitting and expectation-maximization (EM) approach is proposed for unsupervised change detection tasks in SAR images. A difference image is generated first, and an in-depth study of the inherent characteristics of the histogram of the difference image is then made. Thus, the new approach is proposed corresponding to these characteristics. In this approach, the locally fitting model orientated to deal with the unchanged class is put forward to reach a best fit, and the semi-EM algorithm for the changed class is used to tackle the phenomenon of overlapping. Then, through the Bayesian decision rule, the optimal threshold is determined. Our contributions lie in two aspects. First, in the locally fitting model, the location of the optimal threshold can be determined, which leads to an accurate fit over a short interval. Second, the use of the semi-EM algorithm not only retains the efficacy of the EM algorithm to cope with overlapping for the changed class but also simplifies the computing process. Experiments on real data sets confirm the effectiveness of the proposed approach, which results in final maps very similar to the ground truth and is more effective in determining the optimal threshold in comparison with others. The experimental results also demonstrate its effectiveness when the changed areas are of different geometrical shapes.

关键词： expectation-maximisation algorithm ground truth Radar polarimetry difference image Specific absorption rate images Rescue mode matching

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A Quasi-Likelihood Approach to Nonnegative Matrix Factorization

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NEURAL COMPUTATION 2016年第8期28卷 1663-1693页

作者： Devarajan, Karthik Cheung, Vincent C. K. Temple Univ Hlth Syst Dept Biostat & Bioinformat Fox Chase Canc Ctr Philadelphia PA 19111 USA Chinese Univ Hong Kong Sch Biomed Sci Shatin Hong Kong Peoples R China

A unified approach to nonnegative matrix factorization based on the theory of generalized linear models is proposed. This approach embeds a variety of statistical models, including the exponential family, within a single theoretical framework and provides a unified view of such factorizations from the perspective of quasi-likelihood. Using this framework, a family of algorithms for handling signal-dependent noise is developed and its convergence proved using the expectation-maximization algorithm. In addition, a measure to evaluate the goodness of fit of the resulting factorization is described. The proposed methods allow modeling of nonlinear effects using appropriate link functions and are illustrated using an application in biomedical signal processing.

关键词： expectation-maximisation algorithm factorization statistical models (nuclear) Nonnegative matrix decomposition family Perspective goodness of fit linear models Frameworks medical signal processing Converge

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