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Development of a Censored Robust Design Model for Time-Oriented Quality Characteristics

QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2009年第2期25卷 181-197页

作者： Shaibu, A. -B. Cho, Byung Rae Kovach, Jami Univ Houston Dept Informat & Logist Technol Houston TX 77204 USA Clemson Univ Dept Ind Engn Adv Qual Engn Lab Clemson SC 29634 USA

Robust design (RD) techniques, which are based on the concept of building quality into products or processes, are increasingly popular in industry primarily because of their practicality. Traditional RD principles have often been applied to situations in which the quality characteristics of interest are time-insensitive. However, when time-oriented quality characteristics are studied, censored data often occur. As a result, current RD models reported in the literature may not be effective in finding solutions based on such data. To address such practical needs, this paper develops a censored RD model. We also propose an estimation method that is closely related to the expectation-maximization algorithm and compare it with the method of maximum likelihood estimation via a numerical example. Model validation is conducted, and comparative studies are discussed for model verification. Copyright (C) 2008 John Wiley & Sons, Ltd.

关键词： robust design time-oriented quality characteristics lifetime studies censored data expectation-maximization algorithm method of maximum likelihood estimation

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Robust Parameter Estimation for a Class of Nonlinear System With EM algorithm

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IEEE ACCESS 2020年 8卷 30797-30804页

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

This paper is concerned with the robust parameter estimation for linear parameter varying (LPV) finite impulse response (FIR) model. The practical process data are typically polluted by outliers and conventional parameter estimation methods may fail to derive an unbiased estimate. In order to deal with outliers, the Laplace noise model is adopted and the robust system model for the described parameter estimation problem is established. The robust parameter estimation for LPV FIR model is formulated and solved in the EM algorithm scheme and the equations to estimate all the unknown parameters are derived. The efficacy of the proposed method is verified through a numerical simulation and a chemical unit.

关键词： expectation-maximization algorithm Laplace distribution LPV FIR model outliers robust parameter estimation

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Cross-View Action Recognition Based on a Statistical Translation Framework

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2016年第8期26卷 1461-1475页

作者： Wang, Jing Zheng, Huicheng Gao, Jinyu Cen, Jiepeng Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510275 Guangdong Peoples R China

Actions captured under view changes pose serious challenges to modern action recognition methods. In this paper, we propose an effective approach for cross-view action recognition based on a statistical translation framework, which boils down to estimation of visual word transfer probabilities across views. Specifically, local features are extracted from action video frames and form bags of words based on k-means clustering. Though the appearance of an action may vary due to view changes, the underlying transfer tendency between visual words across views can be exploited. We propose two methods to measure the visual-word-based transfer relationship that are eventually based on frequency counts of word pairs. In the first method, word transfer probabilities are estimated by maximizing the likelihood of a shared action set with the EM algorithm. In the second method, word transfer probabilities are estimated by using likelihood-ratio tests. The two methods achieve comparable results and perform better when they are combined. For cross-view action classification, we compute action transfer probabilities based on the estimated word transfer probabilities and then implement a K-NN-like classification based on action video transfer probabilities. We verified our method on the public multiview IXMAS dataset and the WVU dataset. Promising results are obtained compared with state-of-the-art methods.

关键词： Cross-view action recognition expectation-maximization algorithm log-likelihood-ratio tests statistical machine translation transfer probabilities

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Probabilistic based recursive model for adaptive processing of data structures

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EXPERT SYSTEMS WITH APPLICATIONS 2008年第2期34卷 1403-1422页

作者： Cho, Siu-Yeung Nanyang Technol Univ Sch Comp Engn Div Comp Syst Singapore 639798 Singapore

One of the most popular frameworks for the adaptive processing of data structures to date, was proposed by Frasconi et al. [Frasconi, P., Gori, M., & Sperduti, A. (1998). A general framework for adaptive processing of data structures. IEEE Transactions oil Neural Networks, 9(September), 768-785], who used a Backpropagation Through Structures (BPTS) algorithm [Goller, C., & Kuchler, A. (1996). Learning task-dependent distributed representations by back-propagation through structures. In Proceedings of IEEE international conference on neural networks (pp. 347-352);Tsoi, A. C. (1998). Adaptive processing of data structure: An expository overview and comments. Technical report in Faculty Informatics. Wollongong, Australia: University of Wollongong] to carry out supervised learning. This supervised model has been successfully applied to a number of learning tasks that involve complex symbolic structural patterns, such as image semantic structures, internet behavior, and chemical compounds. In this paper, we extend this model, using probabilistic estimates to acquire discriminative information from the learning patterns. Using this probabilistic estimation, smooth discriminant boundaries can be obtained through a process of clustering onto the observed input attributes. This approach enhances the ability of class discrimination techniques to recognize structural patterns. The proposed model is represented by a set of Gaussian Mixture Models (GMMs) at the hidden layer and a set of "weighted sum input to sigmoid function" models at the output layer. The proposed model's learning framework is divided into two phases: (a) locally unsupervised learning for estimating the parameters of the GMMs and (b) globally supervised learning for fine-tuning the GMMs' parameters and optimizing weights at the output layer. The unsupervised learning phase is formulated as a maximum likelihood problem that is solved by the expectation-maximization (EM) algorithm. The supervised learning phase

关键词： adaptive processing of data structures probabilistic recursive model Gaussian mixture model expectation-maximization algorithm Levenberg-Marquardt algorithm

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Maximum likelihood analysis of multi-stress accelerated life test data of series systems with competing log-normal causes of failure

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART O-JOURNAL OF RISK AND RELIABILITY 2015年第2期229卷 119-130页

作者： Roy, Soumya Mukhopadhyay, Chiranjit Indian Inst Management Kozhikode Kozhikode 673570 India Indian Inst Sci Dept Management Studies Bangalore 560012 Karnataka India

This article presents frequentist inference of accelerated life test data of series systems with independent log-normal component lifetimes. The means of the component log-lifetimes are assumed to depend on the stress variables through a linear stress translation function that can accommodate the standard stress translation functions in the literature. An expectation-maximization algorithm is developed to obtain the maximum likelihood estimates of model parameters. The maximum likelihood estimates are then further refined by bootstrap, which is also used to infer about the component and system reliability metrics at usage stresses. The developed methodology is illustrated by analyzing a real as well as a simulated dataset. A simulation study is also carried out to judge the effectiveness of the bootstrap. It is found that in this model, application of bootstrap results in significant improvement over the simple maximum likelihood estimates.

关键词： Bootstrap competing risks expectation-maximization algorithm missing information principle prediction simulation

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Randomized mixture models for probability density approximation and estimation

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INFORMATION SCIENCES 2018年 467卷 135-148页

作者： Nguyen, Hien D. Wang, Dianhui McLachlan, Geoffrey J. La Trobe Univ Dept Math & Stat Melbourne Vic 3086 Australia La Trobe Univ Dept Comp Sci & Informat Technol Melbourne Vic 3086 Australia Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China Univ Queensland Sch Math & Phys St Lucia Qld 4072 Australia

Neural networks (NNs) with random weights are an interesting alternative to conventional NNs that are used more for data modeling. The random vector functional-link (RVFL) network is an established and theoretically well-grounded randomized learner. A key theoretical result for RVFL networks is that they provide universal approximation for continuous maps, in expectation, with respect to the square-integral norm. We specialize and modify this result, and show that RFVL networks can provide functional approximations that converge in Kullback-Leibler divergence, when the target function is a probability density function. Expanding on the approximation results, we demonstrate the RFVL networks lead to a simple randomized mixture model (MM) construction for density estimation from sample data. An expectation-maximization (EM) algorithm is derived for the maximum likelihood estimation of our randomized MM. The EM algorithm is proved to be globally convergent and the maximum likelihood estimator is proved to be consistent. A set of simulation studies is given to provide empirical evidence towards our approximation and density estimation results. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Density estimation expectation-maximization algorithm Functional approximation Mixture models Neural networks Random vector functional-link networks

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Remaining Useful Life Prediction for a Nonlinear Heterogeneous Wiener Process Model With an Adaptive Drift

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IEEE TRANSACTIONS ON RELIABILITY 2015年第2期64卷 687-700页

作者： Huang, Zeyi Xu, Zhengguo Wang, Wenhai Sun, Youxian Zhejiang Univ Dept Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

Nonlinear degradation trajectories are encountered frequently, and not all of them evolve homogeneously in practical systems. To take nonlinearity, heterogeneity, and the entire historical degradation data into account, we propose a nonlinear heterogeneous Wiener process model with an adaptive drift to characterize degradation trajectories. A state-space based method is employed to delineate our model. Due to the introduction of the adaptive drift, it is difficult to directly apply Kalman filter methods to update the distribution of the estimated degradation drift. To address this issue, we develop an online filtering algorithm based on Bayes' theorem. The expectation-maximization (EM) algorithm, as well as a novel Bayes'-theorem-based smoother, are adopted to estimate the unknown parameters in our model. Moreover, the distribution of the predicted remaining useful life (RUL) incorporating the complete distribution of the estimated degradation drift is achieved analytically. Finally, a simulation, and a case study are provided to validate the proposed approach.

关键词： Adaptive drift Bayes' theorem-based filter Bayes' theorem-based smoother expectation-maximization algorithm nonlinear degradation trajectory remaining useful life prediction

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Development and industrial application of soft sensors with on-line Bayesian model updating strategy

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JOURNAL OF PROCESS CONTROL 2013年第3期23卷 317-325页

作者： Deng, Jing Xie, Li Chen, Lei Khatibisepehr, Shima Huang, Biao Xu, Fangwei Espejo, Aris Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada Syncrude Canada Ltd Ft Mcmurray AB Canada

This paper deals with the issues associated with the development of data-driven models as well as model update strategy for soft sensor applications. A practical yet effective solution is proposed. Key process variables that are difficult to measure are commonly encountered in practice due to limitations of measurement techniques. Even with appropriate instruments, some measurements are only available through off-line laboratory analysis with typical sampling intervals of several hours. Soft sensors are inferential models that can provide continuous on-line prediction of hidden variables;such models are capable of combining real-time measurements with off-line lab data. Due to the prevalence of plant-model mismatch, it is important to update the model using the latest reference data. In this paper, parameters of data-driven models are estimated using particle filters under the framework of expectation-maximization (EM) algorithms. A Bayesian methodology for model calibration strategy is formulated. The proposed framework for soft sensor development is applied to an industrial process to provide on-line prediction of a quality variable. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Soft sensor Process identification Bayesian methods expectation-maximization algorithm Particle filtering

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Parametric hidden Markov models for gesture recognition

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1999年第9期21卷 884-900页

作者： Wilson, AD Bobick, AF MIT Media Lab Vis & Modeling Grp Cambridge MA 02139 USA Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

A new method for the representation, recognition, and interpretation of parameterized gesture is presented. By parameterized gesture we mean gestures that exhibit a systematic spatial variation;one example is a point gesture where the relevant parameter is the two-dimensional direction. Our approach is to extend the standard hidden Markov model method of gesture recognition by including a global parametric variation in the output probabilities of the HMM states, Using a linear model of dependence, we formulate an expectation-maximization (EM) method for training the parametric HMM. During testing;a similar EM algorithm simultaneously maximizes the output likelihood of the PHMM for the given sequence and estimates the quantifying parameters. Using visually derived and directly measured three-dimensional hand position measurements as input, we present results that demonstrate the recognition superiority of the PHMM over standard HMM techniques, as well as greater robustness in parameter estimation with respect to noise in the input features. Last, we extend the PHMM to handle arbitrary smooth (nonlinear) dependencies. The nonlinear formulation requires the use of a generalized expectation-maximization (GEM) algorithm for both training and the simultaneous recognition of the gesture and estimation of the value of the parameter. We present results on a pointing gesture, where the nonlinear approach permits the natural spherical coordinate parameterization of pointing direction.

关键词： gesture recognition hidden Markov models expectation-maximization algorithm time-series modeling computer vision

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Maximum-likelihood-based multipath channel estimation for code-division multiple-access systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2001年第2期49卷 290-302页

作者： Ertin, E Mitra, U Siwamogsatham, S Battelle Mem Inst Cognit Syst Grp Columbus OH 43201 USA Ohio State Univ Dept Elect Engn Columbus OH 43210 USA

In this paper, the problem of estimating the channel parameters of a new user in a multiuser code-division multiple-access (CDMA) communication system is addressed, It is assumed that the new user transmits training data over a slowly fading multipath channel, The proposed algorithm is based on maximum-likelihood estimation of the channel parameters. First, an asymptotic expression for the likelihood function of channel parameters is derived and a re-parametrization of this likelihood function is proposed. In this re-parametrization, the channel parameters are combined into a discrete time channel filter of symbol period length. Then, expectation-maximization algorithm and alternating projection algorithm-based techniques are considered to extract channel parameters from the estimated discrete channel filter, to maximize the derived asymptotic likelihood function. The performance of the proposed algorithms is evaluated through simulation studies. In addition, the proposed algorithms are compared to previously suggested subspace techniques for multipath channel estimation.

关键词： alternating projections asymptotic statistics channel estimation DS-CDMA expectation-maximization algorithm maximum-likelihood methods multipath channels multiuser systems

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