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A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments

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SENSORS 2021年第4期21卷 1181-1181页

作者： Zhu, Chenhao Cai, Sheng Yang, Yifan Xu, Wei Shen, Honghai Chu, Hairong Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Key Lab Airborne Opt Imaging & Measurement Changchun 130033 Peoples R China

In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.

关键词： MEMS gyroscope random vibration environments long short-term memory network Kalman filter expectation-maximization algorithm

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Nonparametric Method for Modeling Clustering Phenomena in Emergency Calls Under Spatial-Temporal Self-Exciting Point Processes

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IEEE ACCESS 2019年 7卷 24865-24876页

作者： Li, Chenlong Song, Zhanjie Wang, Xu Tianjin Univ Sch Math Tianjin 300350 Peoples R China Wilfrid Laurier Univ Dept Math Waterloo ON N2L 3C5 Canada Tianjin Univ Visual Pattern Anal Res Lab Tianjin 300072 Peoples R China

In this paper, a nonparametric spatial-temporal self-exciting point process is proposed to model clustering features in emergency calls. Gaussian kernel density functions are considered. The expectation-maximization algorithm is adopted for estimating the model. A simulation study is designed to carefully examine the performance of the proposed nonparametric method. The spatial-temporal patterns of the emergency calls in Montgomery County of Pennsylvania are studied using the proposed nonparametric model. The results demonstrate that the proposed nonparametric model captures the clustering phenomena present in the emergency calls from Montgomery County very well. Further, the proposed parameter estimation method results in robust and precise estimates.

关键词： Spatial-temporal point processes emergency calls nonparametric model maximum likelihood estimation expectation-maximization algorithm

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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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Empirical Bayes matrix completion

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2019年 137卷 195-210页

作者： Matsuda, Takeru Komaki, Fumiyasu Univ Tokyo Grad Sch Informat Sci & Technol Dept Math Informat Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan RIKEN Ctr Brain Sci 2-1 Hirosawa Wako Saitama 3510198 Japan

We develop an empirical Bayes (EB) algorithm for the matrix completion problems. The EB algorithm is motivated from the singular value shrinkage estimator for matrix means by Efron and Morris. Since the EB algorithm is derived as the expectation-maximization algorithm applied to a simple model, it does not require heuristic parameter tuning other than tolerance. Also, it can account for the heterogeneity in variance of observation noise. Numerical results demonstrate that the EB algorithm attains at least comparable accuracy to existing algorithms for matrices not close to square and that it works particularly well when the rank is relatively large or the proportion of observed entries is small. Application to real data also shows the practical utility of the EB algorithm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Empirical Bayes expectation-maximization algorithm Matrix completion Singular value shrinkage

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Semiparametric model for bivariate survival data subject to biased sampling

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2019年第2期81卷 409-429页

作者： Piao, Jin Ning, Jing Shen, Yu Univ Southern Calif Los Angeles CA USA Univ Texas MD Anderson Canc Ctr Houston TX 77030 USA

To understand better the relationship between patient characteristics and their residual survival after an intermediate event such as the local recurrence of cancer, it is of interest to identify patients with the intermediate event and then to analyse their residual survival data. One challenge in analysing such data is that the observed residual survival times tend to be longer than those in the target population, since patients who die before experiencing the intermediate event are excluded from the cohort identified. We propose to model jointly the ordered bivariate survival data by using a copula model and appropriately adjusting for the sampling bias. We develop an estimating procedure to estimate simultaneously the parameters for the marginal survival functions and the association parameter in the copula model, and we use a two-stage expectation-maximization algorithm. Using empirical process theory, we prove that the estimators have strong consistency and asymptotic normality. We conduct simulation studies to evaluate the finite sample performance of the method proposed. We apply the method to two cohort studies to evaluate the association between patient characteristics and residual survival.

关键词： Bivariate survival outcomes Copula model expectation-maximization algorithm Prevalent cohort Sampling bias Two-stage estimation

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A shared-parameter continuous-time hidden Markov and survival model for longitudinal data with informative dropout

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STATISTICS IN MEDICINE 2019年第6期38卷 1056-1073页

作者： Bartolucci, Francesco Farcomeni, Alessio Univ Perugia Fac Econ Perugia Italy Sapienza Univ Rome Dept Publ Hlth & Infect Dis Piazzale Aldo Moro 5 I-00186 Rome Italy

A shared-parameter approach for jointly modeling longitudinal and survival data is proposed. With respect to available approaches, it allows for time-varying random effects that affect both the longitudinal and the survival processes. The distribution of these random effects is modeled according to a continuous-time hidden Markov chain so that transitions may occur at any time point. For maximum likelihood estimation, we propose an algorithm based on a discretization of time until censoring in an arbitrary number of time windows. The observed information matrix is used to obtain standard errors. We illustrate the approach by simulation, even with respect to the effect of the number of time windows on the precision of the estimates, and by an application to data about patients suffering from mildly dilated cardiomyopathy.

关键词： Baum-Welch recursions expectation-maximization algorithm latent class model mildly dilated cardiomyopathy

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Logistic regression augmented community detection for network data with application in identifying autism-related gene pathways

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BIOMETRICS 2019年第1期75卷 222-234页

作者： Zhao, Yunpeng Pan, Qing Du, Chengan Arizona State Univ Sch Math & Nat Sci Tempe AZ 85281 USA George Washington Univ Dept Stat Washington DC 20032 USA Yale Univ Ctr Outcomes Res & Evaluat New Haven CT 06510 USA

When searching for gene pathways leading to specific disease outcomes, additional information on gene characteristics is often available that may facilitate to differentiate genes related to the disease from irrelevant background when connections involving both types of genes are observed and their relationships to the disease are unknown. We propose method to single out irrelevant background genes with the help of auxiliary information through a logistic regression, and cluster relevant genes into cohesive groups using the adjacency matrix. expectation-maximization algorithm is modified to maximize a joint pseudo-likelihood assuming latent indicators for relevance to the disease and latent group memberships as well as Poisson or multinomial distributed link numbers within and between groups. A robust version allowing arbitrary linkage patterns within the background is further derived. Asymptotic consistency of label assignments under the stochastic blockmodel is proven. Superior performance and robustness in finite samples are observed in simulation studies. The proposed robust method identifies previously missed gene sets underlying autism related neurological diseases using diverse data sources including de novo mutations, gene expressions, and protein-protein interactions.

关键词： Autism spectral disease Covariates augmented community detection expectation-maximization algorithm Gene clustering Logistic regression Pseudo-likelihood

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Robust identification approach for nonlinear state-space models

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NEUROCOMPUTING 2019年 333卷 329-338页

作者： Liu, Xin Yang, Xianqiang Harbin Inst Technol Sch Astronaut Harbin 150080 Heilongjiang Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin 150080 Heilongjiang Peoples R China

The identification of nonlinear state-space model (NSSM) with output observations corrupted by outliers is investigated in this paper. The outlier is commonly encountered in practical industrial processes which should not be ignored in nonlinear processes modeling. The statistical scheme based on the Student's t-distribution is applied to resist the outlier and the expectation-maximization (EM) algorithm is employed to simultaneously identify the undetermined model and noise parameters. A particle smoother is introduced and used to approximately calculate the desired Q-function. The usefulness of the proposed approach is demonstrated via the numerical and mechanical examples. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Nonlinear system identification Robustness Student's t-distribution Particle smoother expectation-maximization algorithm

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A varying-coefficient generalized odds rate model with time-varying exposure: An application to fitness and cardiovascular disease mortality

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BIOMETRICS 2019年第3期75卷 853-863页

作者： Zhou, Jie Zhang, Jiajia Mclain, Alexander C. Lu, Wenbin Sui, Xuemei Hardin, James W. Univ South Carolina Dept Epidemiol & Biostat Columbia SC 29208 USA North Carolina State Univ Dept Stat Raleigh NC USA Univ South Carolina Dept Exercise Columbia SC 29208 USA

Varying-coefficient models have become a common tool to determine whether and how the association between an exposure and an outcome changes over a continuous measure. These models are complicated when the exposure itself is time-varying and subjected to measurement error. For example, it is well known that longitudinal physical fitness has an impact on cardiovascular disease (CVD) mortality. It is not known, however, how the effect of longitudinal physical fitness on CVD mortality varies with age. In this paper, we propose a varying-coefficient generalized odds rate model that allows flexible estimation of age-modified effects of longitudinal physical fitness on CVD mortality. In our model, the longitudinal physical fitness is measured with error and modeled using a mixed-effects model, and its associated age-varying coefficient function is represented by cubic B-splines. An expectation-maximization algorithm is developed to estimate the parameters in the joint models of longitudinal physical fitness and CVD mortality. A modified pseudoadaptive Gaussian-Hermite quadrature method is adopted to compute the integrals with respect to random effects involved in the E-step. The performance of the proposed method is evaluated through extensive simulation studies and is further illustrated with an application to cohort data from the Aerobic Center Longitudinal Study.

关键词： B-splines expectation-maximization algorithm generalized odds rate model joint modeling varying coefficient

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Noise-boosted bidirectional backpropagation and adversarial learning

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NEURAL NETWORKS 2019年 120卷 9-31页

作者： Adigun, Olaoluwa Kosko, Bart Univ Southern Calif Dept Elect & Comp Engn Signal & Image Proc Inst Los Angeles CA 90089 USA

Bidirectional backpropagation trains a neural network with backpropagation in both the backward and forward directions using the same synaptic weights. Special injected noise can then improve the algorithm's training time and accuracy because backpropagation has a likelihood structure. Training in each direction is a form of generalized expectation-maximization because backpropagation itself is a form of generalized expectation-maximization. This requires backpropagation invariance in each direction: The gradient log-likelihood in each direction must give back the original update equations of the backpropagation algorithm. The special noise makes the current training signal more probable as bidirectional backpropagation climbs the nearest hill of joint probability or log-likelihood. The noise for injection differs for classification and regression even in the same network because of the constraint of backpropagation invariance. The backward pass in a bidirectionally trained classifier estimates the centroid of the input pattern class. So the feedback signal that arrives back at the input layer of a classifier tends to estimate the local pattern-class centroid. Simulations show that noise speeded convergence and improved the accuracy of bidirectional backpropagation on both the MNIST test set of hand-written digits and the CIFAR-10 test set of images. The noise boost further applies to regular and Wasserstein bidirectionally trained adversarial networks. Bidirectionality also greatly reduced the problem of mode collapse in regular adversarial networks. (C) 2019 Published by Elsevier Ltd.

关键词： Bidirectional backpropagation Neural networks Noise benefit Stochastic resonance expectation-maximization algorithm Bidirectional associative memory Noise benefit

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