检索结果-内蒙古大学图书馆

Hybrid system of ART and RBF neural networks for online clustering

APPLIED SOFT COMPUTING 2017年 58卷 1-10页

作者： Bielecki, Andrzej Wojcik, Mateusz AGH Univ Sci & Technol Fac Elect Engn Automat Comp Sci & Biomed Engn Chair Appl Comp Sci Al Mickiewicza 30 PL-30059 Krakow Poland

An online clustering task is considered for machine state monitoring purpose. In the previous authors researches a classical ART-2 network was tested for online classification of operational states in the context of a wind turbine monitoring. Some drawbacks, however, were found when a data stream size had been increased. This case is investigated in this paper. Classical ART-2 network can cluster data incorrectly when data points are compared by using Euclidean distance. Furthermore, ART-2 network can lose accuracy when data stream is processed for long time. The way of improving the ART-2 network is considered and two main steps of that are taken. At first, the stereographic projection is implemented. At the second step, a new type of hybrid neural system which consists of ART-2 and RBF networks with data processed by using the stereographic projection is introduced. Tests contained elementary scenarios for low-dimensional cases as well as higher dimensional real data from wind turbine monitoring. All the tests implied that an efficient system for online clustering had been found. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Online classification Hybrid system ART-2 neural network RBF neural network expectation-maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Latent Markov and growth mixture models for ordinal individual responses with covariates: A comparison

引用

STATISTICAL ANALYSIS AND DATA MINING 2017年第1期10卷 29-39页

作者： Pennoni, Fulvia Romeo, Isabella Univ Milano Bicocca Dept Stat & Quantitat Methods Via Bicocca Arcimboldi 8ED U7 P-2 I-20126 Milan Italy Ist Ric Farmacol Mario Negri Lab Environm Chem & Toxicol Milan Italy

ObjectiveWe review two alternative ways of modeling stability and change of longitudinal data by using time-fixed and time-varying covariates for the observed individuals. Both the methods build on the foundation of finite mixture models, and are commonly applied in many fields but they look at the data from different perspectives. Our attempt is to make comparisons when the ordinal nature of the response variable is of interest. MethodsThe latent Markov model is based on time-varying latent variables to explain the observable behavior of the individuals. It is proposed in a semiparametric formulation as the latent process has a discrete distribution and is characterized by a Markov structure. The growth mixture model is based on a latent categorical variable that accounts for the unobserved heterogeneity in the observed trajectories and on a mixture of Gaussian random variables to account for the variability in the growth factors. We refer to a real data example on self-reported health status to illustrate their peculiarities and differences.

关键词： dynamic factor model expectation-maximization algorithm forward-backward recursions latent trajectories maximum likelihood Monte Carlo methods

来源：评论

学校读者我要写书评

暂无评论

Accelerated life tests for log-normal series system with dependent masked data under Type-I progressive hybrid censoring

引用

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2017年第2期46卷 1628-1646页

作者： Cai, Jing Shi, Yimin Yue, Hongbo Northwestern Polytech Univ Dept Appl Math Xian Peoples R China Guizhou Minzu Univ Coll Sci Guiyang Peoples R China

This article considers the constant stress accelerated life test for series system products, where independent log-normal distributed lifetimes are assumed for the components. Based on Type-I progressive hybrid censored and masked data, the expectation-maximization algorithm is applied to obtain the estimation for the unknown parameters, and the parametric bootstrap method is used for the standard deviation estimation. In addition, Bayesian approach combining latent variable with Gibbs sampling is developed. Further, the reliability functions of the system and components are estimated at use stress level. The proposed method is illustrated through a numerical example under different masking probabilities and censoring schemes.

关键词： Accelerated life tests Dependent masked data expectation-maximization algorithm Gibbs sampling Log-normal distribution Masking probabilities 62N01 62N05 62F15

来源：评论

学校读者我要写书评

暂无评论

Genetic-based EM algorithm to improve the robustness of Gaussian mixture models for damage detection in bridges

引用

STRUCTURAL CONTROL & HEALTH MONITORING 2017年第3期24卷 1-9页

作者： Santos, Adam Figueiredo, Eloi Silva, Moises Santos, Reginaldo Sales, Claudomiro Costa, Joao C. W. A. Fed Univ Para Appl Electromagnetism Lab R Augusto CorreaGuama 01 BR-66075110 Belem Para Brazil Univ Lusofona Humanidades & Tecnol Fac Engn Campo Grande 376 P-1749024 Lisbon Portugal

During the service life of bridges, the bridge management systems (BMSs) seek to handle all performed assessment activities by controlling regular inspections, evaluations, and maintenance of these structures. However, the BMSs still rely heavily on qualitative and visual bridge inspections, which compromise the structural evaluation and, consequently, the maintenance decisions as well as the avoidance of bridge collapses. The structural health monitoring appears as a natural field to aid the bridge management, providing more reliable and quantitative information. Herein, the machine learning algorithms have been used to unveil structural anomalies from monitoring data. In particular, the Gaussian mixture models (GMMs), supported by the expectation-maximization (EM) on the parameter estimation, have been proposed to model the main clusters that correspond to the normal and stable state conditions of a bridge, even when it is affected by unknown sources of operational and environmental variations. Unfortunately, the performance of the EM algorithm is strongly dependent on the choice of the initial parameters. This paper proposes a hybrid approach based on a standard genetic algorithm (GA) to improve the stability of the EM algorithm on the searching of the optimal number of clusters and their parameters, strengthening the damage classification performance. The superiority of the GA-EM-GMM approach, over the classic EMGMM one, is tested on a damage detection strategy implemented through the Mahalanobis-squared distance, which permits one to track the outlier formation in relation to the chosen main group of states, using real-world data sets from the Z-24 Bridge, in Switzerland. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： Structural health monitoring Data normalization Damage detection Gaussian mixture models expectation-maximization algorithm Genetic algorithms Operational and environmental conditions

来源：评论

学校读者我要写书评

暂无评论

Statistical inference for masked interval data with Weibull distribution under simple step-stress test and tampered failure rate model

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2017年第6期231卷 654-665页

作者： Azizi, Fariba Haghighi, Firoozeh Ghadiri, Elahe Torabi, Leila Univ Tehran Dept Math Stat & Comp Sci Tehran Iran

In this article, we consider the planning of simple step-stress life test in the presence of competing risks with the possibility of masked causes of failure and interval censoring. It is assumed that the competing risks have independent Weibull distribution with the common shape parameter, but different scale parameters and a tampered failure rate model is hold. Maximum likelihood estimates of the model parameters are obtained for both cases, with and without masking. An expectation-maximization algorithm is used to estimate maximum likelihood estimates based on the masked data. For the case without masking, the Fisher information matrix is derived, and the problem of choosing the optimal test plan is considered using variance-optimality as well as determinant-optimality criteria. Through Monte Carlo simulations, the precision of the estimates is assessed and the optimal test plans are compared. Finally, the results are illustrated with an example.

关键词： Competing risks determinant optimality expectation-maximization algorithm interval censoring masked data optimal plan tampered failure rate model variance optimality Weibull distribution

来源：评论

学校读者我要写书评

暂无评论

Semiparametric time-to-event modeling in the presence of a latent progression event

引用

BIOMETRICS 2017年第2期73卷 463-472页

作者： Rice, John D. Tsodikov, Alex Univ Michigan Dept Biostat Ann Arbor MI 48109 USA

In cancer research, interest frequently centers on factors influencing a latent event that must precede a terminal event. In practice it is often impossible to observe the latent event precisely, making inference about this process difficult. To address this problem, we propose a joint model for the unobserved time to the latent and terminal events, with the two events linked by the baseline hazard. Covariates enter the model parametrically as linear combinations that multiply, respectively, the hazard for the latent event and the hazard for the terminal event conditional on the latent one. We derive the partial likelihood estimators for this problem assuming the latent event is observed, and propose a profile likelihood-based method for estimation when the latent event is unobserved. The baseline hazard in this case is estimated nonparametrically using the EM algorithm, which allows for closed-form Breslow-type estimators at each iteration, bringing improved computational efficiency and stability compared with maximizing the marginal likelihood directly. We present simulation studies to illustrate the finite-sample properties of the method;its use in practice is demonstrated in the analysis of a prostate cancer data set.

关键词： expectation-maximization algorithm Semiparametric methods Survival analysis

来源：评论

学校读者我要写书评

暂无评论

ExomeHMM: A Hidden Markov Model for Detecting Copy Number Variation Using Whole-Exome Sequencing Data

引用

CURRENT BIOINFORMATICS 2017年第2期12卷 147-155页

作者： Guo, Cheng Yu, Zhenhua Wang, Minghui Li, Ao Univ Sci & Technol China Sch Informat Sci & Technol Hefei 230027 Anhui Peoples R China Univ Sci & Technol China Res Ctr Biomed Engn Hefei Anhui Peoples R China

Background: Copy number variations (CNVs), including amplification and deletion, are alterations of DNA copy number compared to a reference genome. CNVs play a crucial role in tumourigenesis and progression, including amplification of oncogenes and deletion of tumor suppressor genes that may significantly increase the risk of cancer. CNVs are also reported to be closely related with non-cancer diseases, such as Down syndrome, Parkinson disease, and Alzheimer disease. Objective: Whole-exome sequencing (WES) has been successfully applied to the discovery of gene mutations as well as clinical diagnosis. But it is quite challenging to evaluate the copy number using WES data due to read depth bias, exons' distribution pattern and normal cell contamination. Our aim is develop an efficient method to overcome these challenges and detect CNVs using WES data. Method: In this study, we present ExomeHMM, a hidden Markov model (HMM) based CNV detecting algorithm. ExomeHMM exploits relative read depth, a ratio based signal, to mitigate read depth distortion and employs exponential attenuated transition matrix to handle sparsely and non-uniformly distributed exons. expectation-maximization algorithm is used to optimize parameters for the proposed model. Finally, we use standard Viterbi algorithm to infer the copy number of exons. Results: Using previously identified CNVs in 1000 Genome Project data as golden standard, ExomeHMM achieves the highest F-score among the four methods compared in this study. When applied to triple-negative breast cancer data, ExomeHMM is capable to find abnormal genes that are significantly associated with breast cancer. Conclusion: In conclusion, ExomeHMM is a suitable tool for CNV detections in both healthy samples as well as clinic tumor samples on whole-exome sequencing data.

关键词： Copy number variation expectation-maximization algorithm hidden Markov model next generation sequencing viterbi algorithm whole-exome sequencing

来源：评论

学校读者我要写书评

暂无评论

Stomped-t: A novel probability distribution for rough-probabilistic clustering

引用

INFORMATION SCIENCES 2017年 421卷 104-125页

作者： Banerjee, Abhirup Maji, Pradipta Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata India

Rough clustering is one of the principal research areas in data mining, machine learning, pattern recognition, and bioinformatics. Among different variants of rough clustering, rough-probabilistic clustering is a new concept introduced recently. In rough-probabilistic clustering, a class is defined as the union of two disjoint regions, namely, a crisp lower approximation region and a probabilistic boundary region. In this regard, stomped normal (SN) distribution provides a statistical modeling of the data set in rough-probabilistic clustering framework. The SN distribution models the central tendency, dispersion, and width of the lower approximation region of each class using its mean, variance, and width parameter, respectively. However, it does not take into consideration the property of kurtosis of the class distribution, which controls the concentration of values around mean and shape of the tail of data distribution. In this background, a novel probability distribution, named stomped-t (St) distribution, is introduced in the paper for rough-probabilistic clustering. The proposed probability distribution incorporates the property of kurtosis into the SN framework. The proposed St probability distribution is then integrated within the rough-probabilistic clustering framework for precise and robust clustering of the data. The efficacy of the proposed clustering algorithm is demonstrated for unsupervised data clustering and image segmentation problems, along with a comparative performance analysis with related algorithms. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Rough-probabilistic clustering Stomped normal distribution Image segmentation Hidden Markov random field model expectation-maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Poles tracking of weakly nonlinear structures using a Bayesian smoothing method

引用

MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2017年第PartB期84卷 136-151页

作者： Stephan, Cyrille Festjens, Hugo Renaud, Franck Dion, Jean-Luc Off Natl Etud & Rech Aerosp F-92322 Chatillon France SUPMECA St Ouen St Ouen France

This paper describes a method for the identification and the tracking of poles of a weakly nonlinear structure from its free responses. This method is based on a model of multichannel damped sines whose parameters evolve over time. Their variations are approximated in discrete time by a nonlinear state space model. States are estimated by an iterative process which couples a two-pass Bayesian smoother with an expectation maximization (EM) algorithm. The method is applied on numerical and experimental cases. As a result, accurate frequency and damping estimates are obtained as a function of amplitude. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Modal testing Frequency Damping Identification Unscented Kalman filter expectation-maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

Robust generative asymmetric GMM for brain MR image segmentation

引用

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017年 151卷 123-138页

作者： Ji, Zexuan Xia, Yong Zheng, Yuhui Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Northwestern Polytech Univ Sch Comp Sci Shaanxi Key Lab Speech & Image Informat Proc SAII Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci CMCC Xian 710072 Shaanxi Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing 210044 Jiangsu Peoples R China

Background and objectives: Accurate segmentation of brain tissues from magnetic resonance (MR) images based on the unsupervised statistical models such as Gaussian mixture model (GMM) has been widely studied during last decades. However, most GMM based segmentation methods suffer from limited accuracy due to the influences of noise and intensity inhomogeneity in brain MR images. To further improve the accuracy for brain MR image segmentation, this paper presents a Robust Generative Asymmetric GMM (RGAGMM) for simultaneous brain MR image segmentation and intensity inhomogeneity correction. Method: First, we develop an asymmetric distribution to fit the data shapes, and thus construct a spatial constrained asymmetric model. Then, we incorporate two pseudo-likelihood quantities and bias field estimation into the model's log-likelihood, aiming to exploit the neighboring priors of within-cluster and between-cluster and to alleviate the impact of intensity inhomogeneity, respectively. Finally, an expectation maximization algorithm is derived to iteratively maximize the approximation of the data log-likelihood function to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. Results: To demonstrate the performances of the proposed algorithm, we first applied the proposed algorithm to a synthetic brain MR image to show the intermediate illustrations and the estimated distribution of the proposed algorithm. The next group of experiments is carried out in clinical 3T-weighted brain MR images which contain quite serious intensity inhomogeneity and noise. Then we quantitatively compare our algorithm to state-of-the-art segmentation approaches by using Dice coefficient (DC) on benchmark images obtained from IBSR and BrainWeb with different level of noise and intensity inhomogeneity. The comparison results on various brain MR images demonstrate the superior performances of the proposed algorithm in dealing with the noise and intensity i

关键词： Gaussian mixture model Asymmetric distribution expectation-maximization algorithm Brain MR image segmentation Markov random fields Intensity inhomogeneity

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：