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QUEUEING SYSTEMS 2017年第3-4期85卷 337-359页

作者： Buchholz, Peter Kriege, Jan TU Dortmund Informat 4 D-44221 Dortmund Germany

In this paper, we consider a queue where the inter-arrival times are correlated and, additionally, service times are also correlated with inter-arrival times. We show that the resulting model can be interpreted as an MMAP[K]/PH[K]/1 queue for which matrix geometric solution algorithms are available. The major result of this paper is the presentation of approaches to fit the parameters of the model, namely the MMAP, the PH distribution and the parameters introducing correlation between inter-arrival and service times, according to some trace of inter-arrival and corresponding service times. Two different algorithms are presented. The first algorithm is based on available methods to compute a MAP from the inter-arrival times and a PH distribution from the service times. Afterward, the correlation between inter-arrival and service times is integrated by solving a quadratic programming problem over some joint moments. The second algorithm is of the expectation maximization type and computes all parameters of the MAP and the PH distribution in an iterative way. It is shown that both algorithms yield sufficiently accurate results with an acceptable effort.

关键词： Markovian arrival process Marked Markovian arrival processes Phase type distributions Multi-class queues expectation maximization algorithm

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Bayesian perspective for the registration of multiple 3D views

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COMPUTER VISION AND IMAGE UNDERSTANDING 2014年 118卷 84-96页

作者： Mateo, X. Orriols, X. Binefa, X. Univ Autonoma Barcelona Dept Comp Sci E-08193 Barcelona Spain Microsoft Search Technol Ctr Asia Beijing Peoples R China Univ Pompeu Fabra Dept Informat & Commun Technol Barcelona Spain

The registration of multiple 3D structures in order to obtain a full-side representation of a scene is a longtime studied subject. Even if the multiple pairwise registrations are almost correct, usually the concatenation of them along a cycle produces a non-satisfactory result at the end of the process due to the accumulation of the small errors. Obviously, the situation can still be worse if, in addition, we have incorrect pairwise correspondences between the views. In this paper, we embed the problem of global multiple views registration into a Bayesian framework, by means of an expectation-maximization (EM) algorithm, where pairwise correspondences are treated as missing data and, therefore, inferred through a maximum a posteriori (MAP) process. The presented formulation simultaneously considers uncertainty on pairwise correspondences and noise, allowing a final result which outperforms, in terms of accuracy and robustness, other state-of-the-art algorithms. Experimental results show a reliability analysis of the presented algorithm with respect to the percentage of a priori incorrect correspondences and their consequent effect on the global registration estimation. This analysis compares current state-of-the-art global registration methods with our formulation revealing that the introduction of a Bayesian formulation allows reaching configurations with a lower minimum of the global cost function. (C) 2013 Elsevier Inc. All rights reserved.

关键词： 3D registration Pairwise correspondences expectation maximization algorithm

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Multiple model approach to nonlinear system identification with an uncertain scheduling variable using EM algorithm

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JOURNAL OF PROCESS CONTROL 2013年第10期23卷 1480-1496页

作者： Chen, Lei Tulsyan, Aditya Huang, Biao Liu, Fei Jiangnan Univ Inst Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada

This paper deals with system identification of general nonlinear dynamical systems with an uncertain scheduling variable. A multi model approach is developed;wherein, a set of local auto regressive exogenous (ARX) models are first identified at different process operating points, and are then combined to describe the complete dynamics of a nonlinear system. An expectation-maximization (EM) algorithm is used for simultaneous identification of local ARX models, and for computing the probability associated with each of the local ARX models taking effect. A smoothing algorithm is used to estimate the distribution of the hidden scheduling variables in the EM algorithm. If the dynamics of the scheduling variables are linear, Kalman smoother is used;whereas, if the dynamics are nonlinear, sequential Monte-Carlo (SMC) method is used. Several simulation examples, including a continuous stirred tank reactor (CSTR) and a distillation column, are considered to illustrate the efficacy of the proposed method. Furthermore, to highlight the practical utility of the developed identification method, an experimental study on a pilot-scale hybrid tank system is also provided. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： System identification Nonlinear process Multiple models expectation maximization algorithm Kalman smoother Particle smoother

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Metro passenger's path choice model estimation with travel time correlations derived from smart card data

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TRANSPORTATION PLANNING AND TECHNOLOGY 2020年第2期43卷 141-157页

作者： Zhang, Yongsheng Yao, Enjian Zheng, Kangning Xu, Hao Beijing Jiaotong Univ Sch Traff & Transportat 3 Shangyuancun Beijing 100044 Peoples R China Univ Nevada Dept Civil & Environm Engn Reno NV 89557 USA

Smart card data provides a new perspective for estimating a metro passenger's path choice model in a large-scale urban rail transit network with multiple alternative paths between origin-destination pairs. However, existing research does not consider correlations of path travel times among alternative paths when using smart card data for estimation purposes, leading to biased estimations. This paper proposes an approach to estimating the path choice model considering path travel time correlations. In particular, a simplified form of measuring path travel time correlations caused by shared links is proposed to improve estimation efficiency. Then a framework for a linking path choice model and smart card data is developed based on a Gaussian mixture model;an expectation maximization-based estimation algorithm is also provided. Finally, taking the Guangzhou Metro in China as an example, the superiority of estimations based on smart card data considering correlations is observed in both statistical terms and predictions.

关键词： Urban rail transit path choice model smart card data travel time correlation expectation maximization algorithm application

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Decomposition optimization method for switching models using EM algorithm

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NONLINEAR DYNAMICS 2023年第10期111卷 9361-9375页

作者： Chen, Jing Mao, Yawen Hu, Manfeng Guo, Liuxiao Zhu, Quanmin Jiangnan Univ Sch Sci Wuxi 214122 Peoples R China Univ West England Dept Engn Design & Math Bristol BS16 1QY England

This study proposes a decomposition optimization-based expectation maximization algorithm for switching models. The identities of each sub-model are estimated in the expectation step, while the parameters are updated using the decomposition optimization method in the maximization step. Compared with the traditional expectation maximization algorithm and the gradient descent expectation maximization algorithm, the decomposition optimization-based expectation maximization algorithm avoids the matrix inversion and eigenvalue calculation;thus, it can be extended to complex nonlinear models and large-scale models. Convergence analysis and simulation examples are given to show the effectiveness of the proposed algorithm.

关键词： Parameter estimation Switching model expectation maximization algorithm Decomposition optimization method Least squares algorithm

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Likelihood Inference Based on Left Truncated and Right Censored Data From a Gamma Distribution

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IEEE TRANSACTIONS ON RELIABILITY 2013年第3期62卷 679-688页

作者： Balakrishnan, Narayanaswamy Mitra, Debanjan McMaster Univ Dept Math & Stat Hamilton ON L8S 4K1 Canada King Abdulaziz Univ Dept Stat Jeddah 21413 Saudi Arabia Indian Inst Technol Guwahati Dept Math Gauhati 781039 Assam India

The gamma distribution is used as a lifetime distribution widely in reliability analysis. Lifetime data are often left truncated, and right censored. The EM algorithm is developed here for the estimation of the scale and shape parameters of the gamma distribution based on left truncated and right censored data. The Newton-Raphson method is also used for the same purpose, and then these two methods of estimation are compared through an extensive Monte Carlo simulation study. The asymptotic variance-covariance matrix of the MLEs under the EM framework is obtained by using the missing information principle (Louis, 1982). Then, the asymptotic confidence intervals for the parameters are constructed. The confidence intervals based on the EM algorithm and the Newton-Raphson method are then compared empirically in terms of coverage probabilities. Finally, all the methods of inference discussed here are illustrated through a numerical example.

关键词： Asymptotic variance-covariance matrix coverage probability expectation maximization algorithm gamma distribution information matrix left truncation lifetime data maximum likelihood estimates missing information principle Monte Carlo simulation right censoring

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Semi-supervised learning via manifold regularization

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The Journal of China Universities of Posts and Telecommunications 2012年第6期19卷 79-88页

作者： MAO Yu ZHOU Yan-quan LI Rui-fan WANG Xiao-jie ZHONG Yi-xin School of Telecommunication Engineering Beijing University of Posts and Telecommunications

This paper proposes a novel graph-based transductive learning algorithm based on manifold regularization. First, the manifold regularization was introduced to probabilistic discriminant model for semi-supervised classification task. And then a variation of the expectation maximization （EM） algorithm was derived to solve the optimization problem, which leads to an iterative algorithm. Although our method is developed in probabilistic framework, there is no need to make assumption about the specific form of data distribution. Besides, the crucial updating formula has closed form. This method was evaluated for text categorization on two standard datasets, 20 news group and Reuters-21578. Experiments show that our approach outperforms the state-of-the-art graph-based transductive learning methods.

关键词： manifold regularization semi-supervised learning transductive learning expectation maximization algorithm classification text categorization

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Statistical Trilateration With Skew-t Distributed Errors in LTE Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2016年第10期15卷 7114-7127页

作者： Muller, Philipp del Peral-Rosado, Jose A. Piche, Robert Seco-Granados, Gonzalo Tampere Univ Technol Dept Automat Sci & Engn FIN-33101 Tampere Finland Univ Autonoma Barcelona Dept Telecommun & Syst Engn Cerdanyola Del Valles 08193 Spain

Localization accuracy of trilateration methods in long term evolution (LTE) cellular networks, which are based on time-of-arrival, may be highly degraded due to multipath and non-line of sight conditions in urban and indoor environments. Multipath mitigation techniques usually involve a high computational burden and require wideband signals to be effective, which limit their adoption in certain low-cost and low-power mobile applications using narrow-band signals. As an alternative to these conventional techniques, this paper analyzes an expectation maximization (EM) localization algorithm that considers the skewness introduced by multipath in the LTE ranging error distribution. The EM algorithm is extensively studied with realistic emulated LTE signals of 1.4-MHz bandwidth. The EM method is compared with a standard nonlinear least squares (NLS) algorithm under ideal simulated conditions and using realistic outdoor measurements from a laboratory testbed. The EM method outperforms the NLS method when the ranging errors in the training and test stages have similar distributions.

关键词： Statistical trilateration skew-t distribution localization LTE expectation maximization algorithm

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Optimal planning of step-stress accelerated degradation test based on Tweedie exponential dispersion process with random effects

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第6期40卷 2976-3000页

作者： He, Qian Yan, Weian Liu, Weidong Bigaud, David Xu, Xiaofan Lei, Zitong East China Jiaotong Univ Sch Transportat Engn Nanchang Jiangxi Peoples R China Nanchang Univ Sch Adv Mfg Nanchang Jiangxi Peoples R China Univ Angers LARIS SFR MATHST Angers France

The step stress accelerated degradation test (SSADT) is an effective tool for assessing the reliability of highly reliable products. However, conducting an SSADT is expensive and time consuming, and the obtained SSADT data has an impact on the accuracy of the subsequent product reliability index estimations. Consequently, devising a cost-constrained SSADT plan that yields high-precision reliability estimates poses a significant challenge. This paper focuses on the optimal design of SSADT for the Tweedie exponential dispersion process with random effect (TEDR), a general degradation model capable of describing product heterogeneity. Under given budget and boundary constraints, the optimal sample size, observation frequency and observation times at each stress level are obtained by minimizing the asymptotic variance of the estimated quantile life at normal operating conditions. The sensitivity and stability of the SSADT plan are also studied, and the results indicate the robustness of the optimal plan against slight parameters fluctuations. We use the expectation maximization (EM) algorithm to estimate TEDR parameters and reliability indicators under SSADT, providing a systematic method for obtaining the optimal SSADT plan under budget constraints. The proposed framework is illustrated using the case of LED chips data, showcasing its potential for practical application.

关键词： expectation maximization algorithm optimal design random effects step stress accelerated degradation test Tweedie exponential dispersion process

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Online auto-calibration of triaxial accelerometer with time-variant model structures

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SENSORS AND ACTUATORS A-PHYSICAL 2017年 266卷 294-307页

作者： Ye, L. Argha, A. Celler, B. G. Nguyen, H. T. Su, S. W. Univ Technol Fac Engn & Informat Technol Sydney NSW Australia Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW Australia

In this paper, an online auto-calibration method for MicroElectroMechanical Systems (MEMS) triaxial accelerometer (TA) is proposed, which can simultaneously identify the time-dependent model structure and its parameters during the changes of the operating environment. Firstly, the model as well as its associated cost function is linearized by a new proposed linearization approach. Then, exploiting an online sparse recursive least square (SPARLS) estimation, the unknown parameters are identified. In particular, the online sparse recursive method is based on an L-1-norm penalized expectation-maximum (EM) algorithm, which can amend the model automatically by penalizing the insignificant parameters to zero. Furthermore, this method can reduce computational complexity and be implemented in a low-cost Micro-Controller-Unit (MCU). Based on the numerical analysis, it can be concluded that the proposed recursive algorithm can calculate the unknown parameters reliably and accurately for most MEMS triaxial accelerometers available in the market. Additionally, this method is experimentally validated by comparing the output estimations before and after calibration under various scenarios, which further confirms its feasibility and effectiveness for online TA calibration. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Parameter estimation Tri-axial accelerometer Online calibration Model linearization expectation maximization algorithm

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