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IEEE International Symposium on Circuits and Systems

作者： Pumrin, S Dailey, DJ Chulalongkorn Univ Dept Elect Engn Bangkok 10330 Thailand

ISBN: (纸本)0780377613

In this paper, we present a statistical method to extract images. of passenger cars from highway traffic scenes. The expectation-maximization (EM) algorithm is used to classify the vehicles in the images as' either being passenger cars or some other bigger vehicles, cars versus non-cars. The vehicle classification algorithm uses training sets of 100-frame video sequences. The car group is comprised of passenger cars and light trucks. The non-car group is comprised of heavy single trucks as well as 3-axle and more combination trucks. We use the properties of their dimensional,distribution and the probability of their appearance to identify the vehicle group. We present a validation of our algorithm using real-world traffic scenes.

关键词： image classification intelligent transportation systems expectation-maximization algorithm

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Black-Box expectation-maximization algorithm for Estimating Latent States of High-Speed Vehicles 18

Black-Box Expectation-Maximization Algorithm for Estimating ...

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18th International Conference on Control, Automation and Systems (ICCAS)

作者： Kim, Yoon-Yeong Lee, Wonsung Moon, Il-Chul Korea Adv Inst Sci & Technol Dept Ind & Syst Engn Daejeon 34141 South Korea

ISBN: (纸本)9788993215151

Tracking an object under a noisy environment is difficult especially when there exist unknown parameters that affect the object's behavior. In the case of a high-speed ballistic vehicle, the trajectory of the ballistic vehicle is affected by the change of atmospheric conditions as well as the various parameters of the object itself. To filter these latent factors of the dynamics model, this paper proposes a black-box expectation-maximization algorithm to estimate the latent parameters for enhancing the accuracy of the trajectory tracking. The expectation step calculates the likelihood of the observation by the Extended Kalman Smoothing that reflects the forward-backward probability combination. The maximization step optimizes the unknown parameters to maximize the likelihood by the Bayesian optimization with Gaussian process. Our simulation experiment results show that the error of tracking position of the ballistic vehicle reduced when there exist much noise in the observations, and some important parameters are unknown.

关键词： Target Tracking expectation-maximization algorithm Kalman Filter Ballistic Object

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An expectation-maximization algorithm based Kalman Smoother Approach for Single-Trial Estimation of Event-related Potentials

An Expectation-Maximization Algorithm based Kalman Smoother ...

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34th Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Ting, Chee-Ming Samdin, S. Balqis Salleh, Sh-Hussain Omar, M. Hafizi Kamarulafizam, I. UTM Ctr Biomed Engn Skudai 81310 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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Naive Bayes using the expectation-maximization algorithm for reject inference

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Communications in Statistics Case Studies Data Analysis and Applications 2022年第3期8卷 484-504页

作者： Anderson, Billie UMKC Henry W. Bloch School of Management Kansas City MO United States

In the last several years, there has been significant research in applying semi-supervised machine learning models to the reject inference problem. When a financial institution wants to build a model to predict the default of credit applicants, the institution only has a known good/bad outcome loan status for the accepted applicants;this causes an inherent bias in the model. Reject inference is used to infer the good or bad loan status of credit applicants that were rejected by a financial institution. This paper presents a reject inference technique in which a semi-supervised framework is developed using a Naive Bayes model. The framework uses the expectation-maximization (EM) algorithm to incorporate rejected applicants into the parameter estimation of the model using a bootstrapping approach. The proposed method has an advantage over traditional reject inference methods because the rejected applicant data will participate in the estimation of the model parameters, thus avoiding the extrapolation problem. The Naive Bayes model using the EM algorithm is compared to logistic regression and several semi-supervised techniques. © 2022 Taylor & Francis Group, LLC.

关键词： Credit scoring expectation-maximization algorithm feature binning Naive Bayes reject inference semi-supervised algorithm

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Noise Benefits in the expectation-maximization algorithm: NEM Theorems and Models

Noise Benefits in the Expectation-Maximization Algorithm: NE...

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International Joint Conference on Neural Networks (IJCNN)

作者： Osoba, Osonde Mitaim, Sanya Kosko, Bart Univ Southern Calif Inst Signal & Image Proc Dept Elect Engn Los Angeles CA 90089 USA Thammasat Univ Dept Elect & Comp Engn Fac Engn Bangkok 12120 Thailand

ISBN: (纸本)9781424496365

We prove a general sufficient condition for a noise benefit in the expectation-maximization (EM) algorithm. Additive noise speeds the average convergence of the EM algorithm to a local maximum of the likelihood surface when the noise condition holds. The sufficient condition states when additive noise makes the signal more probable on average. The performance measure is Kullback relative entropy. A Gaussian-mixture problem demonstrates the EM noise benefit. Corollary results give other special cases when noise improves performance in the EM algorithm.

关键词： Gaussian noise Gaussian-mixture problem Kullback relative entropy Maximum likelihood estimation NEM model NEM theorem Noise Noise measurement Probability density function Random variables Signal processing algorithms Stochastic resonance additive noise speeds entropy expectation-maximisation algorithm expectation-maximization algorithm noise benefits

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Blind iterative decoding of side-informed data hiding using the expectation-maximization algorithm

Blind iterative decoding of side-informed data hiding using ...

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6th Conference on Watermarking of Multimedia Contents

作者： Balado, F Pérez-González, F Comesaña, P Univ Coll Dublin Dublin 4 Ireland

ISBN: (纸本)0819452092

Distortion- Compensated Dither Modulation (DC-DM), also known as Scalar Costa Scheme (SCS), has been theoretically shown to be near-capacity achieving thanks to its use of side information at the encoder. In practice, channel coding is needed in conjunction with this quantization- based scheme in order to approach the achievable rate limit. The most powerful coding methods use iterative decoding (turbo codes, LDPC), but they require knowledge of the channel model. Previous works on the subject have assumed the latter to be known by the decoder. We investigate here the possibility of undertaking blind iterative decoding, of DC-DM. using maximum likelihood estimation of the channel model within the decoding procedure. The unknown attack is assumed to be i.i.d. and additive. Before each iterative decoding step, a new optimal estimation of the attack model is made using the reliability information provided by the previous step. This new model is used for the next iterative decoding stage, and the procedure is repeated until convergence. We show that the iterative expectation-maximization algorithm is suitable for solving the problem posed by model estimation, as it can be conveniently intertwined with iterative decoding.

关键词： side-informed data hiding Distortion-Compensated Dither Modulation (DC-DM) Scalar Costa Scheme (SCS) blind iterative decoding expectation-maximization algorithm

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Estimation of cortical multivariate autoregressive models for EEG/MEG using an expectation-maximization algorithm

Estimation of cortical multivariate autoregressive models fo...

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5th IEEE International Symposium on Biomedical Imaging

作者： Cheung, Bing Leung Patrick Van Veen, Barry D. Univ Wisconsin Dept Elect & Comp Engn Madison WI 53706 USA

ISBN: (纸本)9781424420025

A new method for estimating multivariate autoregressive (MWAR) models of cortical connectivity from surface EEG or MEG measurements is presented. Conventional approaches to this problem first attempt to solve the inverse problem to estimate cortical signals and then fit an MVAR model to the estimated signals. Our new approach expresses the measured data in terms of a hidden state equation describing MVAR cortical signal evolution and an observation equation that relates the hidden state to the surface measurements. We develop an expectation-maximization (EM) algorithm to find maximum likelihood estimates of the MVAR model parameters. Simulations show that this one-step approach performs significantly better than the conventional two-step approach at estimating the cortical signals and detecting functional connectivity between different cortical regions.

关键词： cortical connectivity multivariate autoregressive model expectation-maximization algorithm Granger causality partial directed coherence

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Adaptive Cubature Kalman Filter Based on the expectation-maximization algorithm

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IEEE ACCESS 2019年 7卷 158198-158206页

作者： Zhou, Weidong Liu, Lu Harbin Engn Univ Coll Automat Harbin 150001 Peoples R China

A cubature Kalman filter is considered to be one of the most useful methods for nonlinear systems. However, when the statistical characteristics of noise are unknown, the estimation accuracy is degraded. Therefore, an adaptive square-root cubature Kalman filter (ASCKF) is designed to handle the unknown noise. The maximum likelihood criterion and expectation-maximization algorithm are employed to adaptively estimate the parameters of unknown noise, thus restraining the disturbance resulting from unknown noise and improving the estimation accuracy. The stability of the proposed algorithm is theoretically analyzed. Finally, simulations are carried out to illustrate that the performance of the ASCKF algorithm is much more reliable than that of a standard square-root cubature Kalman filter.

关键词： Cubature Kalman filter expectation-maximization algorithm maximum likelihood criterion noise estimation nonlinear system

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Modal identification of structures from input/output data using the expectation-maximization algorithm and uncertainty quantification by mean of the bootstrap

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STRUCTURAL CONTROL & HEALTH MONITORING 2019年第1期26卷 e2272-e2272页

作者： Cara, Javier Univ Politecn Madrid ETSI Ind Jose Gutierrez Abascal 2 E-28006 Madrid Spain

Modal testing in civil engineering includes the possibility to apply measured forces in addition to the unmeasured ambient excitation. In these cases, it is necessary to consider mathematical models that account for both excitation sources, what explains the increasing interest in sophisticated system identification methods for modal analysis with input/output data. In this work, the maximum likelihood estimation of the state space model from input/output vibration data is investigated. This model can be estimated using different techniques: Among them, the maximum likelihood method has optimal statistical properties, so modal parameters computed using this approach will be optimum in a statistical point of view. The algorithm considered for maximizing the likelihood is the expectation-maximization algorithm. The quantification of modal parameters uncertainty is addressed using a Monte Carlo type approach called the bootstrap, which is based on resampling the residuals of the estimated model. Finally, the proposed techniques are applied to synthetic data and also to field data recorded on a stress-ribbon footbridge.

关键词： bootstrap combined experimental-operational modal analysis expectation-maximization algorithm mass-normalized modal vectors modal parameters operational modal analysis with exogenous inputs state space model stress-ribbon footbridge

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Structure function learning of hierarchical multi-state systems with a change-point: An embedded expectation-maximization algorithm

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2023年第1期240卷

作者： Shao, Zhidong Zhang, Qin Liu, Yu Xie, Chaoyang Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Sichuan Peoples R China Univ Elect Sci & Technol China Ctr Syst Reliabil & Safety Chengdu 611731 Sichuan Peoples R China China Acad Engn Phys Inst Syst Engn Mianyang 621000 Sichuan Peoples R China 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China

Hierarchical multi-state systems (HMSSs) are one of the most important structures in engineering practices, and their reliability assessment has received increasing attention in the past decades. However, the degradation behaviors of HMSSs may exhibit two distinct phases during their operations due to a sudden change in their structure functions caused by the shift in functional requirements or working environments. Motivated by this phenomenon, this article studies a new system structure, namely HMSSs with a change-point. A dynamic Bayesian network (DBN) model is applied for reliability assessment of HMSSs with a change-point. A novel embedded expectation-maximization (EM) algorithm is developed to learn the unknown parameters, including the change-point and structure functions, of the DBN model using incomplete observation data. In contrast to traditional EM algorithms, an additional optimization procedure for estimating the change-point is embedded into the M-step of the proposed embedded EM algorithm. Two illustrative examples, including a numerical case and a lighting system in a manufacturing workshop, are exemplified to demonstrate the effectiveness of the proposed method. The results indicate that the proposed method can accurately identify the change-point and learn structure functions from incomplete observation sequences.

关键词： Hierarchical multi-state system Change-point Dynamic Bayesian network Structure function expectation-maximization algorithm

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