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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Chee-Ming Ting S. Balqis Samdin Sh-Hussain Salleh M. Hafizi Omar I. Kamarulafizam Center for Biomedical Engineering UTM 81310 Skudai Johor Malaysia

ISBN: (纸本)9781457717871

This paper applies an expectation-maximization (EM) based Kalman smoother (KS) approach for single-trial event-related potential (ERP) estimation. Existing studies assume a Markov diffusion process for the dynamics of ERP parameters which is recursively estimated by optimal filtering approaches such as Kalman filter (KF). However, these studies only consider estimation of ERP state parameters while the model parameters are pre-specified using manual tuning, which is time-consuming for practical usage besides giving suboptimal estimates. We extend the KF approach by adding EM based maximum likelihood estimation of the model parameters to obtain more accurate ERP estimates automatically. We also introduce different model variants by allowing flexibility in the covariance structure of model noises. Optimal model selection is performed based on Akaike Information Criterion (AIC). The method is applied to estimation of chirp-evoked auditory brainstem responses (ABRs) for detection of wave V critical for assessment of hearing loss. Results shows that use of more complex covariances are better estimating inter-trial variability.

关键词： Event-related potentials Kalman smoother expectation-maximization algorithm

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ℓ 1-Penalized censored Gaussian graphical model

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Biostatistics (Oxford, England) 2020年第2期21卷 e1-e16页

作者： Augugliaro, Luigi Abbruzzo, Antonino Vinciotti, Veronica Department of Economics Business and Statistics University of Palermo Building 13 Viale delle Scienze Palermo Italy Department of Mathematics Brunel University London Kingston Lane Uxbridge UB8 3PH United Kingdom

Graphical lasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. Typical examples are data generated by polymerase chain reactions and flow cytometer. The combination of censoring and high-dimensionality make inference of the underlying genetic networks from these data very challenging. In this article, we propose an $\ell_1$-penalized Gaussian graphical model for censored data and derive two EM-like algorithms for inference. We evaluate the computational efficiency of the proposed algorithms by an extensive simulation study and show that, when censored data are available, our proposal is superior to existing competitors both in terms of network recovery and parameter estimation. We apply the proposed method to gene expression data generated by microfluidic Reverse Transcription quantitative Polymerase Chain Reaction technology in order to make inference on the regulatory mechanisms of blood development. A software implementation of our method is available on github (https://***/LuigiAugugliaro/cglasso). © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: ***@***.

关键词： Censored data expectation-maximization algorithm Gaussian graphical model Graphical Lasso High-dimensional data

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Parameter identification of nonlinear multirate time-delay system with uncertain output delays

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2018年第12期40卷 3498-3506页

作者： Yang, Xianqiang Xiong, Weili Wang, Zeyuan Liu, Xin Harbin Inst Technol Harbin Heilongjiang Peoples R China Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi Peoples R China

The joint parameter and time-delay estimation problems for a class of nonlinear multirate time-delay system with uncertain output delays are addressed in this paper. The practical process typically has time-delay properties and the process data are often multirate, sampled with output data inevitably corrupted by uncertain delays. The linear parameter varying (LPV) finite impulse response (FIR) multirate time-delay model is initially built to describe the considered system. The problems of over-parameterization and the existence of both continuous model parameters and discrete time-delays have made the conventional maximum likelihood difficult to solve the considered problems. In order to handle these problems, the joint parameter and time-delay estimation for the LPV FIR multirate time-delay model are formulated in the expectation-maximization scheme, and the algorithm to estimate the model parameters and time-delays is derived, simultaneously based on multirate process data. The efficacy of the proposed method is verified through a numerical simulation and a practical chemical plant.

关键词： System identification nonlinear time-delay model multirate data the continuous stirred tank reactor system expectation-maximization algorithm

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Estimating metro passengers' path choices by combining self-reported revealed preference and smart card data

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2018年第Jul.期92卷 76-89页

作者： Zhang, Yongsheng Yao, Enjian Zhang, Junyi Zheng, Kangning Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China Hiroshima Univ Grad Sch Int Dev & Cooperat 1-5-1 Kagamiyama Higashihiroshima 7398529 Japan

With the help of automated fare collection systems in the metro network, more and more smart card (SC) data has been widely accumulated, which includes abundant information (i.e., Big Data). However, its inability to record passengers' transfer information and factors affecting passengers' travel behaviors (e.g., socio-demographics) limits further potential applications. In contrast, self-reported Revealed Preference (RP) data can be collected via questionnaire surveys to include those factors;however, its sample size is usually very small in comparison to SC data. The purpose of this study is to propose a new set of approaches of estimating metro passengers' path choices by combining self-reported RP and SC data. These approaches have the following attractive features. The most important feature is to jointly estimate these two data sets based on a nested model structure with a balance parameter by accommodating different scales of the two data sets. The second feature is that a path choice model is built to incorporate stochastic travel time budget and latent individual risk-averse attitude toward travel time variations, where the former is derived from the latter and the latter is further represented based on a latent variable model with observed individual socio-demographics. The third feature is that an algorithm of combining the two types of data is developed by integrating an expectation-maximization algorithm and a nested logit model estimation method. The above-proposed approaches are examined based on data from Guangzhou Metro, China. The results show the superiority of combined data over single data source in terms of both estimation and forecasting performance.

关键词： Data fusion expectation-maximization algorithm Metro network Stochastic travel time budget Risk-averse attitude

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Model-based capacitated clustering with posterior regularization

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2018年第2期271卷 594-605页

作者： Mai, Feng Fry, Michael J. Ohlmann, Jeffrey W. Stevens Inst Technol Sch Business Hoboken NJ 07030 USA Univ Cincinnati Dept Operat Business Analyt & Informat Syst Cincinnati OH 45221 USA Univ Iowa Tippie Coll Business Iowa City IA 52242 USA

We propose a heuristic approach to address the general class of optimization problems involving the capacitated clustering of observations consisting of variable values that are realizations from respective probability distributions. Based on the expectation-maximization algorithm, our approach unifies Gaussian mixture modeling for clustering analysis and cluster capacity constraints using a posterior regularization framework. To test our algorithm, we consider the capacitated p-median problem in which the observations consist of geographic locations of customers and the corresponding demand of these customers. Our heuristic has superior performance compared to classic geometrical clustering heuristics, with robust performance over a collection of instance types. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Heuristics Gaussian mixture models expectation-maximization algorithm Posterior regularization Capacitated p-median problem

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Gaussian mixture learning via adaptive hierarchical clustering

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SIGNAL PROCESSING 2018年 150卷 116-121页

作者： Li, Jichuan Nehorai, Arye Washington Univ Dept Elect & Syst Engn St Louis MO 63130 USA

In signal processing, a large number of samples can be generated by a Monte Carlo method and then encoded as a Gaussian mixture model for compactness in computation, storage, and communication. With a large number of samples to learn from, the computational efficiency of Gaussian mixture learning becomes important. In this paper, we propose a new method of Gaussian mixture learning that works both accurately and efficiently for large datasets. The proposed method combines hierarchical clustering with the expectation-maximization algorithm, with hierarchical clustering providing an initial guess for the expectation-maximization algorithm. We also propose adaptive splitting for hierarchical clustering, which enhances the quality of the initial guess and thus improves both the accuracy and efficiency of the combination. We validate the performance of the proposed method in comparison with existing methods through numerical examples of Gaussian mixture learning and its application to distributed particle filtering. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Gaussian mixture learning Adaptive hierarchical clustering Adaptive splitting expectation-maximization algorithm Distributed particle filtering

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Real-Valued Sparse DOA Estimation for MIMO Array System Under Unknown Nonuniform Noise

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IEEE ACCESS 2018年 6卷 52218-52226页

作者： Dong, Fang Shen, Chong Zhang, Kun Wang, Huafei Hainan Univ State Key Lab Marine Resource Utilizat South Chin Haikou 570228 Hainan Peoples R China Hainan Univ Coll Informat Sci & Technol Haikou 570228 Hainan Peoples R China

In this paper, the problem of the direction of arrival (DOA) estimation for the multiple input multiple output (MIMO) array system is considered as a real-valued sparse signal recover procedure under the condition of unknown nonuniform noise. Then, a real-valued covariance vector-based sparse Bayesian learning framework is proposed, in which the reduced dimensional (RD) transformation is utilized to remove the redundant elements of MIMO array system, and a linear transformation is applied to eliminate the influence of unknown non-uniform noise. Then by supposing that the source powers follow an independent prior Gaussian distribution with zero-mean, a real-valued covariance vector-based sparse Bayesian model is formulated. And considering its unknown variance as hyperparameters, they can be estimated by adopting the expectation-maximization algorithm. Finally, the DOA can be achieved according to the spatial spectrum of hyperparameters. Simulation results have demonstrated that our proposed method not only achieves more superior performance but also provides robustness against nonuniform noise, compared with other recently reported sparse signal representation based methods.

关键词： MIMO array system nonuniform noise DOA estimation real-valued sparse signal recover expectation-maximization algorithm

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Statistical Leakage Analysis Using Gaussian Mixture Model

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IEEE ACCESS 2018年 6卷 51939-51950页

作者： Kwon, Hyunjeong Woo, Mingyu Kim, Young Hwan Kang, Seokhyeong Pohang Univ Sci & Technol Dept Elect Engn Pohang 37673 South Korea Univ Calif San Diego Dept Comp Engn La Jolla CA 92093 USA

In the design process of advanced semiconductor devices, statistical leakage analysis has emerged as a major step due to uncertainties in the leakage current caused by the process variations. In this paper, a novel statistical leakage analysis which uses Gaussian mixture model (GMM) as the density function of leakage current is proposed. To estimate the probability density function, our proposed method clusters the rapidly converged leakage data using the GMM. The GMM can represent any distributions, so it is suitable to estimate the leakage distribution, which varies as the technology node or operating condition changes. In addition, our proposed method (SLA-GMM) defines a terminating condition that guarantees the convergence of the leakage data and prevents the underfitting or overfitting in the GMM modeling process. With sequential addition, SLA-GMM significantly reduced the error that can occur during the addition process. In studies with a goodness-of-fit test, SLA-GMM achieved up to 98% and 94% improvements in the Chi-square static and the K-S static compared with the previous method based on an analytic model.

关键词： expectation-maximization algorithm Gaussian mixture model machine learning statistical leakage analysis

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Robust Identification Method for LPV ARX Systems and Its Application to a Mechanical Unit

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IEEE ACCESS 2019年 7卷 164418-164428页

作者： Liu, Xin Zhang, Tingting Liu, Xiaofeng Hohai Univ Coll IoT Engn Changzhou 213022 Peoples R China

This paper introduces a robust identification solution for the linear parameter varying Autoregressive Exogenous systems with outlier-contaminated outputs. The Laplace distribution with heavy tails and the expectation maximization algorithm are combined to build the robust system identification framework. To overcome the obstacles brought by the outliers, the Laplace distribution which can be decomposed into infinite Gaussian components, is applied to mathematically model the system noise. The problem of parameter estimation is solved using the expectation maximization algorithm, and the equations to infer the system model and noise parameters are simultaneously provided in the developed identification method. Finally, the verification tests performed on a numerical example and a mechanical unit are used to prove the validity of the developed identification method.

关键词： Robust system identification outlier-contaminated outputs expectation-maximization algorithm Laplace distribution

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Joint modeling of linear degradation and failure time data with masked causes of failure under simple step-stress test

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2018年第8期88卷 1603-1615页

作者： Azizi, Fariba Haghighi, Firoozeh Univ Tehran Sch Math Stat & Comp Sci Coll Sci Tehran Iran

In this paper, we propose a method to model the relationship between degradation and failure time for a simple step-stress test where the underlying degradation path is linear and different causes of failure are possible. It is assumed that the intensity function depends only on the degradation value. No assumptions are made about the distribution of the failure times. A simple step-stress test is used to induce failure experimentally and a tampered failure rate model is proposed to describe the effect of the changing stress on the intensities. We assume that some of the products that fail during the test have a cause of failure that is only known to belong to a certain subset of all possible failures. This case is known as masking. In the presence of masking, the maximum likelihood estimates of the model parameters are obtained through the expectation-maximization algorithm by treating the causes of failure as missing values. The effect of incomplete information on the estimation of parameters is studied through a Monte-Carlo simulation. Finally, a real-world example is analysed to illustrate the application of the proposed methods.

关键词： expectation-maximization algorithm cause of failure intensity linear degradation path masked data reliability function

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