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Robust sparse representation model for blade tip timing

JOURNAL OF SOUND AND VIBRATION 2021年 500卷 116028-116028页

作者： Wang, Zeng-Kun Yang, Zhi-Bo Li, Hao-Qi Wu, Shu-Ming Tian, Shao-Hua Chen, Xue-Feng Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

Blade tip timing (BTT) is one of the most important non-contact monitoring methods for blade vibration estimation. BTT predominantly consists of two steps: 1) acquiring the original pulse signal generated by the rotating blade through optical probes. 2) Obtaining the arrival time of the original pulses through a high-precision counter and then transforming it to deflection. Multiple noise is involved in BTT measurement, which is further complicated by the variable operating environment owing to the complexity and multiplicity of blade vibration and the transmission path. With the introduction of prior knowledge, sparse representation has proved a promising tool for the reconstruction of blade vibration features. However, the classical sparse representation model applied in BTT, is mostly formulated and conducted based on the simple assumption that the noise follows a Gaussian distribution. The assumption, too idealized for real practices, restricts the performance promotion of sparse representation in BTT. To address this problem and to represent the unknown noise, a robust sparse representation model based on a mixture of Gaussians (MoG) is proposed in this work. The solution algorithm of the proposed model is then derived from the perspective of the expectation maximization (em) algorithm. To validate the effectiveness of the present method, the performance of the developed methodology is discussed in terms of different regular items. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Blade tip timing (BTT) Sparse representation Noise modeling em algorithm

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Quantitative trait locus mapping analysis of multiple traits when using genotype data with potential errors

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PEERJ 2021年 9卷 e12187页

作者： Tong, Liang Zhou, Ying Guo, Yixing Ding, Hui Ji, Donghai Harbin Univ Sci & Technol Sch Sci Harbin Peoples R China Suihua Univ Sch Informat Engn Suihua Peoples R China Heilongjiang Univ Sch Math Sci Harbin Peoples R China Heilongjiang Prov Key Lab Theory & Computat Compl Harbin Peoples R China Dalian Univ Sci & Technol Dalian Peoples R China

Background: Quantitative trait locus (QTL) analysis aims to locate and estimate the effects of the genes influencing quantitative traits and infer the relationship between gene variants and changes in phenotypic characteristics using statistical methods. Some methods have been developed to map QTLs of multiple traits in the case of no genotype error in a given dataset. However, practical genetic data that people use may contain some potential errors because of the limitations of biotechnology. Common genetic data correction methods can only reduce errors, but cannot calculate the degree of error. In this paper, we propose a QTL mapping strategy for multiple traits in the presence of genotype errors. Methods: The additive effect, dominant effect, recombination rate, error rate, and other parameters of QTLs can be simultaneously obtained using this new method in the framework of multiple-interval mapping. Results: Our simulation results show that the accuracy of parameter estimation can be improved by considering the errors of marker genotypes during the analysis of genetic data. Real data analysis also shows that the new method proposed in this paper can map the QTLs of multiple traits more accurately.

关键词： em algorithm Error rate Multiple-interval mapping Multiple traits QTL Recombination rate

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Relativistic Maxwellian mixture model

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PHYSICS OF PLASMAS 2021年第12期28卷 122106-122106页

作者： Ueno, Genta Zenitani, Seiji Inst Stat Math 10-3 Midori Cho Tokyo 1908562 Japan Kobe Univ Res Ctr Urban Safety & Secur Nada Ku 1-1 Rokkodai Cho Kobe Hyogo 6578501 Japan

For analyzing distribution functions of relativistic plasma, we propose a mixture model composed of relativistic Maxwellian distributions. We first summarize the basic properties of the relativistic Maxwellian distribution, including the derivation of the normalization constant when there is a bulk velocity. We also examine the maximum likelihood estimation of the relativistic Maxwellian distribution. We then introduce a relativistic Maxwellian mixture model (R-MMM), which is a weighted sum of relativistic Maxwellian distributions. We develop an expectation-maximization algorithm for estimating the parameters of R-MMM, namely, the mixing proportion, the bulk velocity, and the temperature of each component. We apply a two-component R-MMM to a distribution function by a particle-in-cell (PIC) simulation of relativistic pair plasma and separate the simulated distribution function into two components. We find that one component has a large bulk velocity while the other is almost stagnant, and that the two components have almost the same temperatures, which is also consistent with the initial temperature of PIC simulation. (c) 2021 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http:// ***/licenses/by/4.0/). https://***/10.1063/5.0059126

关键词： Topics: Maxwell-Boltzmann distribution Special relativity Nonequilibrium statistical mechanics Relativistic effects Statistical models Particle-in-cell method Numerical algorithms Magnetic reconnection em algorithm Particle distribution functions.

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Enhanced performance of high-speed IM/DD DMT systems using an em channel estimation algorithm for short reach optical communication 10

Enhanced performance of high-speed IM/DD DMT systems using a...

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10th International Conference on Information Optics and Photonics

作者： Li, Mengyuan Wang, Muguang Ding, Qi Beijing Jiaotong Univ Minist Educ Key Lab All Opt Network & Adv Telecommun Network Inst Lightwave Technol Beijing 100044 Peoples R China

ISBN: (纸本)9781510625808

A semi-blind Expectation-Maximization (em) channel estim ation algorithm is proposed for 50 Gb/s quadrature phase shift keying (QPSK)-Discrete MultiTone (DMT) signal transmission systems using intensity modulation/direct detection (IM/DD) over 100 km standard single mode fiber (SSMF). The reported channel estimation methods for DMT systems can be roughly divided into two categories: semi-blind channel estimation and blind channel estimation. Due to the low accuracy of traditional blind channel estimation algorithm and lower spectral efficiency of the semi-blind channel estimation algorithm relying on more training sequences (TS), em algorithm is proposed that is a two-step iterative procedure to maximize the likelihood function for achieving channel estimation instead of classical semi-blind channel estimation methods with more TS. Also, we assume the channel of the IM/DD DMT system as an additive white gaussian noise (AWGN) channel. Simulation results show that using em algorithm yields about 2 dB optical signal noise ratio (OSNR) improvement at a bit error ratio (BER) of 3.8x10(-3) compared to classical channel estimation based on TS under the same number of TS and has the similar performance compared to classical channel estimation relying on more TS. In addition, it is shown that at high OSNR (> 19 dB), the performance of em algorithm outperforms that of LMS algorithm. On the contrary, the performance of least mean square (LMS) algorithm outperforms that of em algorithm at low OSNR (< 19 dB).

关键词： short reach optical communication DMT channel estimation algorithm em algorithm LMS algorithm

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Robust online rain removal for surveillance videos with dynamic rains

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KNOWLEDGE-BASED SYSTemS 2021年 222卷 107006-107006页

作者： Yi, Lixuan Zhao, Qian Wei, Wei Xu, Zongben Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Xi An Jiao Tong Univ Minist Educ Key Lab Intelligent Networks & Network Secur Xian 710049 Peoples R China

The current rain removal techniques proposed for surveillance videos mainly assume consistent rains with invariant extents and types, and are implemented in a batch-mode learning manner. Such assumption deviates from the continuously varying insights of practical rains, and the batch mode further makes the techniques infeasible for real long-lasting videos. To alleviate these issues, this study proposes a novel online rain removal approach to represent practical dynamic rains embedded in surveillance videos. Particularly, we model the rain streaks scattered in each video frame as a patch-wise mixture of Gaussians (P-MoG) distribution, and update its parameters frame by frame. Such a P-MoG modeling manner finely reflects the non-i.i.d. dynamic variations of rains along time. In specific, the P-MoG rain model in each frame is regularized by the learned rain knowledge in previous frames, making the online model adaptable to not-identically-distributed rains in each frame while regularized by not-independently-distributed rains in previous frames. The proposed model is formulated as a concise probabilistic MAP model, which can be readily solved by em algorithm. We further embed an affine transformation operator into the proposed model, making it adaptable to a wider range of videos with camera jitters. The superiority of the proposed method is substantiated by extensive experiments implemented on synthetic and real videos containing static and dynamic rains as compared with state-of-the-arts in both accuracy and efficiency. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Rain removal Patch-wise mixture of Gaussians Online algorithm em algorithm

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Research on fault diagnosis technology of centrifugal pump blade crack based on PCA and GMM

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MEASURemENT 2021年 173卷 108558-108558页

作者： Cao, Shuping Hu, Zhenjiang Luo, Xiaohui Wang, Huawei Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Peoples R China

Centrifugal pumps are widely used in modern industry, and blades are the key parts of it. The cracks on blades may result in a very serious consequence. In this paper, a fault diagnosis method based on principal component analysis (PCA) and Gaussian mixed model (GMM) was proposed, which combined signal processing and knowledge. Also, the theory model of proposed methods was established, and Expectation Maximization (em) algorithm was used to make the model converge. PCA was used to reduce the data dimensionalities and increase the feature resolution, and GMM was used as classifier for crack fault. In order to verify the diagnostic effect of the model, the experimental bench of centrifugal pump was established, and various working conditions of the centrifugal pump were simulated. Experimental results showed that the classifier based on these parameters performed very well in data testing.

关键词： Centrifugal pump Fault diagnosis Principal component analysis Gaussian mixed model em algorithm

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Parametric Inference Using Nomination Sampling with an Application to Mercury Contamination in Fish

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SANKHYA-SERIES A-MATHemATICAL STATISTICS AND PROBABILITY 2020年第1期82卷 115-146页

作者： Nourmohammadi, Mohammad Jozani, Mohammad Jafari Johnson, Brad C. Stat Res & Training Ctr Iran Tehran Iran Univ Manitoba Dept Stat Winnipeg MB R3T 2N2 Canada

Randomized nomination sampling (RNS) is a rank-based sampling technique which has been shown to be effective in several nonparametric studies involving environmental, agricultural, medical and ecological applications. In this paper, we investigate parametric inference using RNS design for estimating an unknown vector of parameters theta in some parametric families of distributions. We examine both maximum likelihood (ML) and method of moments (MM) approaches. We introduce four types of RNS-based data as well as necessary em algorithms for the ML estimation under each data type, and evaluate the performance of corresponding estimators in estimating theta compared with those based on simple random sampling (SRS). Our results can address many parametric inference problems in reliability theory, sport analytics, fisheries, etc. Theoretical results are augmented with numerical evaluations, where we also study inference based on imperfect ranking. We apply our methods to a real data problem in order to study the distribution of the mercury contamination in fish body using RNS designs.

关键词： Randomized nomination sampling Method of moments Maximum likelihood Modified maximum likelihood em algorithm

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Classical and Bayesian Inference for the Burr Type XII Distribution Under Generalized Progressive Type I Hybrid Censored Sample

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JOURNAL OF STATISTICAL THEORY AND APPLICATIONS 2020年第4期19卷 547-557页

作者： Parviz, Parya Panahi, Hanieh Islamic Azad Univ Dept Stat Sci & Res Branch Tehran Iran Islamic Azad Univ Dept Math & Stat Lahijan Branch Lahijan Iran

This paper describes the classical and Bayesian estimation for the parameters of the Burr Type XII distribution based on generalized progressive Type I hybrid censored sample. We first discuss the maximum likelihood estimators of unknown parameters using the expectation-maximization (em) algorithm and associated interval estimates using Fisher information matrix. We then derive the Bayes estimators with respect to different symmetric and asymmetric loss functions. In this regard, we use Lindley's approximation and importance sampling methods. Highest posterior density (HPD) intervals of unknown parameters are constructed as well. The results of simulation studies and real data analysis are conducted to compare the performance of the proposed point and interval estimators. (C) 2020 The Authors. Published by Atlantis Press B.V.

关键词： Burr Type XII distribution Confidence intervals em algorithm Generalized progressive hybrid censoring Markov chain Monte Carlo technique

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GemBag: group estimation of multiple Bayesian graphical models

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2021年第1期22卷 2450-2497页

作者： Xinming Yang Lingrui Gan Naveen N. Narisetty Feng Liang Department of Statistics University of Illinois at Urbana-Champaign Champaign IL Facebook Menlo Park CA

In this paper, we propose a novel hierarchical Bayesian model and an efficient estimation method for the problem of joint estimation of multiple graphical models, which have similar but different sparsity structures and signal strength. Our proposed hierarchical Bayesian model is well suited for sharing of sparsity structures, and our procedure, called as GemBag, is shown to enjoy optimal theoretical properties in terms of ℓ∞ norm estimation accuracy and correct recovery of the graphical structure even when some of the signals are weak. Although optimization of the posterior distribution required for obtaining our proposed estimator is a non-convex optimization problem, we show that it turns out to be convex in a large constrained space facilitating the use of computationally efficient algorithms. Through extensive simulation studies and an application to a bike sharing data set, we demonstrate that the proposed GemBag procedure has strong empirical performance in comparison with alternative methods.

关键词： graphical models Bayesian regularization spike-and-slab priors selection consistency non-convex optimization em algorithm

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Variable selection for generalized odds rate mixture cure models with interval-censored failure time data

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2021年 156卷 107115-107115页

作者： Xu, Yang Zhao, Shishun Hu, Tao Sun, Jianguo Jilin Univ Ctr Appl Stat Res Changchun Peoples R China Jilin Univ Coll Math Changchun Peoples R China Capital Normal Univ Sch Math Sci Beijing Peoples R China Univ Missouri Dept Stat Columbia MO 65211 USA

Variable selection for failure time data with a cured fraction has been discussed by many authors but most of existing methods apply only to right-censored failure time data. In this paper, we consider variable selection when one faces interval-censored failure time data arising from a general class of generalized odds rate mixture cure models, and we propose a penalized variable selection method by maximizing a derived penalized likelihood function. In the method, the sieve approach is employed to approximate the unknown function, and it is implemented using a novel penalized expectation-maximization (em) algorithm. Also the asymptotic properties of the proposed estimators of regression parameters, including the oracle property, are obtained. Furthermore, a simulation study is conducted to assess the finite sample performance of the proposed method, and the results indicate that it works well in practice. Finally, the approach is applied to a set of real data on childhood mortality taken from the Nigeria Demographic and Health Survey. (C) 2020 Elsevier B.V. All rights reserved.

关键词： em algorithm Generalized odds rate mixture cure model Penalized maximum likelihood estimators Sieve approach

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