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ASSET LIFE PREDICTION USING MULTIPLE DEGRADATION INDICATORS AND FAILURE EVENTS: A CONTINUOUS STATE SPACE MODEL APPROACH

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EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY 2009年第4期44卷 72-81页

作者： Zhou, Yifan Ma, Lin Mathew, Joseph Sun, Yong Wolff, Rodney Queensland Univ Technol CRC Integrated Engn Asset Management Sch Engn Syst Brisbane Qld 4001 Australia Queensland Univ Technol Sch Math Sci Brisbane Qld 4001 Australia

Two practical issues are involved in asset life prediction using degradation indicators: (1) identifying uncertain failure thresholds of degradation indicators and (2) fusing multiple degradation indicators extracted from condition monitoring data. The state space degradation model provides an effective approach to address these two issues. However, existing research on the state space degradation model largely adopts a discrete time or states assumption which requires equal inspection intervals or discretising continuous degradation indicators. To remove the discrete time and states assumptions, this paper proposes a Gamma-based state space model. The Gamma process has a monotonically increasing property that is consistent with the irreversible degradation processes of engineering assets within a single maintenance cycle. The monotonically increasing property also makes the establishment of the likelihood function more straightforward when failure times are considered In this paper, parameter estimation and lifetime prediction algorithms for the Gamma-based state space model are developed. In addition, an effectiveness evaluation approach for indicators in degradation modelling is established. The proposed Gamma-based state space model and algorithms are validated using both simulated data and afield dataset from a liquefied natural gas company.

关键词： Asset life prediction Degradation model expectation-maximisation algorithm State space model

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Unscented Blind Image De-blurring Using Camera with Inertial Measurement Unit

Unscented Blind Image De-blurring Using Camera with Inertial...

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IEEE International Conference on Robotics and Biomimetics (ROBIO)

作者： Tseng, Chin-Yuan Chen, Jian-An Hu, Jwu-Sheng Natl Chiao Tung Univ Dept Elect & Control Engn Hsinchu Taiwan

ISBN: (纸本)9781467321273;9781467321259

Image blur resulting from camera motion is an annoying factor for robotic vision, especially for high-speed applications. This work proposes a sensor fusion model for blind image de-blurring using inertial measurement unit. The model attempts to observe the camera motion, estimate the point spread function and de-convolute the image simultaneously. To solve the problem, an iterative estimation procedure using Maximum A-Posteriori expectation-Maximization (MAP-EM) algorithms and Unscented Kalman Filter are proposed. Simulation results show the feasibility of the proposed formulation to blindly de-blurring the image under camera motion.

关键词： Kalman filters cameras deconvolution expectation-maximisation algorithm image motion analysis image restoration nonlinear filters optical transfer function robot vision sensor fusion

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Disentangling the impact of nested sources of variability on species growth processes: A mixture of multilevel mixed model approach.

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STAT 2024年第2期13卷

作者： Leumbe, Fabrice Moudjieu Mortier, Frederic Takam, Patrice Soh Picard, Nicolas Felix, Allah-Barem Fidele, Baya Tadesse, Mahlet G. Rossi, Vivien Univ Yaounde I Natl Adv Sch Engn Yaounde Yaounde Cameroon CIRAD Forets & Soc Yaounde Cameroon CIRAD Forets & Soc Montpellier France Univ Montpellier Forets & Soc CIRAD Montpellier France Georgetown Univ Georgetown Environm Justice Program Washington DC USA Univ Yaounde I Dept Math Yaounde Cameroon GIP ECOFOR Paris France Inst Centrafricain Rech Agron Bangui Cent Afr Republ Minist Eaux Forets Chasse & Peche Bangui Cent Afr Republ Georgetown Univ Dept Math & Stat Washington DC USA

The understanding of tree growth processes is crucial for promoting sustainable forest management strategies. This is a challenging task in highly biodiverse ecosystems where many tree species are observed on very few individuals and the small sample sizes hinder a good fit of species-specific models. We propose the use of finite mixture of random coefficient regression models with multilevel nested random effects to infer guild specific fixed and random effects while evaluating the relative importance of the nested sources of variability on goodness-of-fit. This approach extends finite mixture of linear mixed model used for longitudinal or single group structured data contexts. A dedicated expectation-maximisation algorithm is introduced for parameter estimation. Simulations are performed for the evaluation of the misspecification of nested-grouping structures. This work has been motivated by data collected biennially in Central African rainforests from 1986 to 2010. We show the accuracy of the proposed approach in successfully reproducing individual growth processes and classifying tree species into well-differentiated clusters with clear ecological interpretations. Moreover, results confirm that interindividual variability appears as the most important factor to explain tropical tree species growth process variability from Central Africa forests.

关键词： African rainforest expectation-maximisation algorithm joint models multilevels random coefficient regression tree growth models

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Multivariate mixture model for small area estimation of poverty indicators

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY 2022年第Sup2期185卷 S724-S755页

作者： Bikauskaite, Agne Molina, Isabel Morales, Domingo Eurostat Business Stat Div Luxembourg Germany Univ Carlos III Madrid Dept Stat Madrid Spain Univ Miguel Hernandez Elche Operat Res Ctr Elche Spain

When disaggregation of national estimates in several domains or areas is required, direct survey estimators, which use only the domain-specific survey data, are usually design-unbiased even under complex survey designs (at least approximately) and require no model assumptions. Nevertheless, they are appropriate only for domains or areas with sufficiently large sample size. For example, when estimating poverty in a domain with a small sample size (small area), the volatility of a direct estimator might make that area seems like very poor in one period and very rich in the next one. Small area (or indirect) estimators have been developed in order to avoid such undesired instability. Small area estimators borrow strength from the other areas so as to improve the precision and therefore obtain much more stable estimators. However, the usual model-based assumptions, which include some kind of area homogeneity, may not hold in real applications. A more flexible model based on multivariate mixtures of normal distributions that generalises the usual nested error linear regression model is proposed for estimation of general parameters in small areas. This flexibility makes the model adaptable to more general situations, where there may be areas with a different behaviour from the other ones, making the model less restrictive (hence, more close to nonparametric) and more robust to outlying areas. An expectation-maximisation (E-M) method is designed for fitting the proposed mixture model. Under the proposed mixture model, two different new predictors of general small area indicators are proposed. A parametric bootstrap method is used to estimate the mean squared errors of the proposed predictors. Small sample properties of the new predictors and of the bootstrap procedure are analysed by simulation studies and the new methodology is illustrated with an application to poverty mapping in Palestine.

关键词： empirical best estimator expectation-maximisation algorithm nested error model normal mixture model parametric bootstrap

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A probabilistic approach to time delay estimation

A probabilistic approach to time delay estimation

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International Conference on New Trends in Information and Service Science, NISS

作者： Yihan Gao Institute for Interdisciplinary Information Science Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781467308762

Time delay estimation is a very general problem with wide range of applications. When noisy repetitive signals are observed, the noise cancellation is achieved by averaging perfectly aligned signals. A time delay estimator is developed for determining time delay between signals received on different trials in the presence of uncorrelated noise. The estimator is based on a probabilistic generative model for delayed signals, and tries to find the delay and the source signal simultaneously so that maximum likelihood is achieved. An iterative method based on the expectation-Maximization algorithm is used for finding maximum likelihood estimate of parameters. The estimator has been tested on three types of synthetic signals. The result shows that it can tolerate 5 to 10dB more noise while achieving the same performance as cross-correlation estimator.

关键词： Noise cancellation time delay signals delay estimation delayed signal expectation-maximisation algorithm Probability Noise iterative methods trees (mathematics) maximum likelihood

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Optimization of Subcarrier Allocation in Highly Dynamic Cellular Relay Networks

Optimization of Subcarrier Allocation in Highly Dynamic Cell...

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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Han, Xue Mao, Guoqiang Liu, Zichen Huang, Liang Qian, Manli Shi, Jinglin Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia Beijing Key Lab Mobile Comp & Pervas Device Beijing Peoples R China Chinese Acad Sci Grad Univ Beijing Peoples R China

ISBN: (纸本)9781467359399;9781467359382

Cellular relay networks prove to be a cost-effective approach that offers significant performance benefits in coverage extension, cell-edge throughput enhancement and increased spectral efficiency. Radio resource management for cellular relay networks, where the network topology is highly dynamic, is particularly challenging. The highly dynamic topology may be caused by an ever increasing number of users accessing Internet and other multimedia services using mobile devices carried by pedestrians, in a train or in vehicles. This paper studies the subcarrier allocation problem in highly dynamic cellular relay networks with an objective to maximize the overall throughput. This optimization problem is formulated and solved as an expectation maximization problem. Statistical multiplexing gain is explicitly explored to further improve the channel utilization and spectral efficiency. Numerical evaluations are performed which demonstrate that the proposed scheme offers higher system throughput and improved radio resource utilization compared with existing schemes.

关键词： cellular radio expectation-maximisation algorithm relay networks (telecommunication) resource allocation telecommunication network topology

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MAP Estimation for Graphical Models by Likelihood Maximization 10

MAP Estimation for Graphical Models by Likelihood Maximizati...

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Annual Conference on Neural Information Processing Systems

作者： Akshat Kumar Shlomo Zilberstein Department of Computer Science University of Massachusetts Amherst MA

ISBN: (纸本)9781617823800

Computing a maximum a posteriori (MAP) assignment in graphical models is a crucial inference problem for many practical applications. Several provably convergent approaches have been successfully developed using linear programming (LP) relaxation of the MAP problem. We present an alternative approach, which transforms the MAP problem into that of inference in a mixture of simple Bayes nets. We then derive the expectation Maximization (EM) algorithm for this mixture that also monotonically increases a lower bound on the MAP assignment until convergence. The update equations for the EM algorithm are remarkably simple, both conceptually and computationally, and can be implemented using a graph-based message passing paradigm similar to max-product computation. Experiments on the real-world protein design dataset show that EM's convergence rate is significantly higher than the previous LP relaxation based approach MPLP. EM also achieves a solution quality within 95% of optimal for most instances.

关键词： expectation-maximisation algorithm Graphical models

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ESTIMATION OF THE BATTERY STATE OF CHARGE: A SWITCHING MARKOV STATE-SPACE MODEL

ESTIMATION OF THE BATTERY STATE OF CHARGE: A SWITCHING MARKO...

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European Signal Processing Conference

作者： Jana Kalawoun Patrick Pamphile Gilles Celeux Krystyna Biletska Maxime Montaru CEA LIST Laboratoire d'Analyse de Donnees et Intelligence des Systemes INRIA Universite Paris Sud CNRS UMR8628 91405 Orsay CEA LITEN Laboratoire du Stockage ElectroChimique

ISBN: (纸本)9781479988518

An efficient estimation of the State of Charge (SoC) of a battery is a challenging issue in the electric vehicle domain. The battery behavior depends on its chemistry and uncontrolled usage conditions, making it very difficult to estimate the SoC. This paper introduces a new model for SoC estimation given instantaneous measurements of current and voltage using a Switching Markov State-Space Model. The unknown parameters of the model are batch learned using a Monte Carlo approximation of the EM algorithm. Validation of the proposed approach on an electric vehicle real data is encouraging and shows the ability of this new model to accurately estimate the SoC for different usage conditions.

关键词： State of Charge Kalman Filter Switching Markov State-Space Model EM algorithm Particle Filter CHARGE STATES expectation-maximisation algorithm Particle filters Batteries Models, Space Kalman filters switching Electric charge Electric vehicles

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Statistical Phrase/Accent Command Estimation algorithm Utilizing Linguistic Information

Statistical Phrase/Accent Command Estimation Algorithm Utili...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Ryotaro Sato Kunio Kashino Graduate School of Information Science and Technology The University of Tokyo

ISBN: (纸本)9781538646595

The importance of extracting non-linguistic information has been highlighted in a growing variety of applications of speech signal processing. Among the audio features carrying such information, fundamental frequency (F_0) contours are considered primarily important. The Fujisaki model is a physical model that describes a F_0 contour with only a small number of parameters, namely, the timings and magnitudes of the phrase and accent commands, and a stochastic formulation and estimation algorithm have recently been proposed for it. However, the use of linguistic information has so far been limited, while it is known that accent commands are strongly related to linguistic information in many languages, and linguistic information could be obtained from the input audio signals by using speech recognition techniques. Against this background, this paper introduces a novel F_0 command parameter estimation method that incorporates linguistic information with the stochastic framework. Experiments using real speech data show that when linguistic information is appropriately utilized, the estimation accuracy of accent command parameters is improved by 43% under the proposed criteria.

关键词： voice fundamental frequency contour estimation Fujisaki model prosodic information processing EM algorithm speech recognition linguistic data Speech recognition expectation-maximisation algorithm commands Sodium Glutamate Voice Signal

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Joint Maximum Likelihood and expectation Maximization methods for Unsupervised Iterative Soft Bit Error Rate Estimation

Joint Maximum Likelihood and Expectation Maximization method...

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IEEE Vehicular Technology Conference

作者： Samir Saoudi Jia Dong Tarik Ait-Idir Institut Mines-Telecom/Telecom Bretagne/LabSticc/UeB INPT-Rabat

ISBN: (纸本)9781467318808

This paper addresses the problem of unsupervised soft bit error rate (BER) estimation for any communications system, where no prior knowledge either about transmitted information bits, or the transceiver scheme is available. We show that the problem of BER estimation is equivalent to estimating the conditional probability density functions (pdf)s of soft receiver outputs. Assuming that the receiver has no analytical model of soft observations, we propose a non parametric Kernel-based pdf estimation technique, with Maximum Likelihood based smoothing parameter computation. We then introduce an iterative Stochastic expectation Maximization algorithm for the estimation of both a priori and a posteriori probabilities of transmitted information bits, and the classification of soft observations according to transmitted bit values. These inputs serve in the iterative Kernel-based estimation procedure of conditional pdfs. We analyze the performance of the proposed unsupervised BER estimator in the framework of a multiuser code division multiple access (CDMA) system with single user detection, and show that attractive performance are achieved compared with conventional Monte Carlo-aided techniques.

关键词： maximum likelihood Bit error rate COMMUNICATION SYSTEMS expectation-maximisation algorithm a posteriori probability information bit Iterative assessment procedure Receivers Prior Learning Infectious Disease Transmission Code division multiple access

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