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A human-robot cooperative learning system for easy installation of assistant robots in new working environments

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2004年第3期40卷 233-265页

作者： López, ME Barea, R Bergasa, LM Escudero, MS Univ Alcala De Henares Dept Elect Alcala De Henares Spain

One of the applications of service robots with a greater social impact is the assistance to elderly or disabled people. In these applications, assistant robots must robustly navigate in structured indoor environments such as hospitals, nursing homes or houses, heading from room to room to carry out different nursing or service tasks. Among the main requirements of these robotic aids, one that will determine its future commercial feasibility, is the easy installation of the robot in new working domains without long, tedious or complex configuration steps. This paper describes the navigation system of the assistant robot called SIRA, developed in the Electronics Department of the University of Alcala, focusing on the learning module, specially designed to make the installation of the robot easier and faster in new environments. To cope with robustness and reliability requirements, the navigation system uses probabilistic reasoning (POMDPs) to globally localize the robot and to direct its goal-oriented actions. The proposed learning module fast learns the Markov model of a new environment by means of an exploration stage that takes advantage of human - robot interfaces ( basically speech) and user - robot cooperation to accelerate model acquisition. The proposed learning method, based on a modification of the EM algorithm, is able to robustly explore new environments with a low number of corridor traversals, as shown in some experiments carried out with SIRA.

关键词： probabilistic navigation partially observable Markov decision processes learning under uncertainty expectation-maximization algorithm assistant robots

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A Bernoulli-Gaussian mixture model of donation likelihood and monetary value: An application to alumni segmentation in a university setting

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COMPUTERS & INDUSTRIAL ENGINEERING 2013年第4期66卷 1085-1095页

作者： Durango-Cohen, Pablo L. Durango-Cohen, Elizabeth J. Torres, Ramon L. Northwestern Univ Evanston IL 60208 USA Northwestern Univ Transportat Ctr Evanston IL 60208 USA IIT Stuart Sch Business Chicago IL 60661 USA

Advances in computational power and enterprise technology, e.g., Customer Relationship Management (CRM) software and data warehouses, allow many businesses to collect a wealth of information on large numbers of consumers. This includes information on past purchasing behavior, demographic characteristics, as well as how consumers interact with the organization, e.g., in events, on the web. The ability to mine such data sets is crucial to an organization's ability to deliver better customer service, as well as manage its resource allocation decisions. To this end, we formulate a Bernoulli-Gaussian mixture model that jointly describes the likelihood and monetary value of repeat transactions. In addition to presenting the model, we derive an instance of the expectation-maximization algorithm to estimate the associated parameters, and to segment the consumer population. We apply the model to an extensive dataset of donations received at a private, Ph.D.-granting university in the Midwestern United States. We use the model to assess the effect of individual traits on their contribution likelihood and monetary value, discuss insights stemming from the results, and how the model can be used to support resource allocation decisions. For example, we find that participation in alumni-oriented activities, i.e., reunions or travel programs, is associated with increased donation likelihood and value, and that fraternity/sorority membership magnifies this effect. The presence/characterization of unobserved, cross-sectional heterogeneity in the data set, i.e., unobserved/unexplained systematic differences among individuals, is, perhaps, our most important finding. Finally, we argue that the proposed segmentation approach is more appealing than alternatives appearing in the literature that consider donation likelihood and monetary value separately. Among them and as a benchmark, we compare the proposed model to a segmentation that builds on a multivariate Normal mixture model, and c

关键词： Market segmentation Finite mixture models Alumni giving Fundraising Bernoulli-Gaussian Distribution expectation-maximization algorithm

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Moments of Mixture Periodic Autoregressive Models

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2011年第22期40卷 3937-3947页

作者： Bentarzi, M. Merzougui, M. Univ Sci & Technol USTHB Algiers Algeria Univ MHamed Bouguera Boumerdes Algeria

This article deals with the study of some properties of a mixture periodically correlated autoregressive (MPAR(S)) time series model, which extends the mixture time invariant parameter autoregressive (MAR) model, that has recently received a considerable interest from many economic time series analysts, to mixture periodic parameter autoregressive model. The aim behind this extension is to make the model able to capture, in addition to all features captured by the classical MAR model, the periodicity feature exhibited by the autocovariance structure of many encountered financial and environmental time series with eventual multimodal distributions. Our main contribution here is obtaining of the second moment periodically stationary condition for a MPAR(S) (K;2, ... , 2) model, furthermore the closed-form of the second moment is obtained.

关键词： expectation-maximization algorithm Mixture periodic autoregressive models Periodic autoregressive models Periodically correlated process

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Gaussian mixture learning via adaptive hierarchical clustering

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SIGNAL PROCESSING 2018年 150卷 116-121页

作者： Li, Jichuan Nehorai, Arye Washington Univ Dept Elect & Syst Engn St Louis MO 63130 USA

In signal processing, a large number of samples can be generated by a Monte Carlo method and then encoded as a Gaussian mixture model for compactness in computation, storage, and communication. With a large number of samples to learn from, the computational efficiency of Gaussian mixture learning becomes important. In this paper, we propose a new method of Gaussian mixture learning that works both accurately and efficiently for large datasets. The proposed method combines hierarchical clustering with the expectation-maximization algorithm, with hierarchical clustering providing an initial guess for the expectation-maximization algorithm. We also propose adaptive splitting for hierarchical clustering, which enhances the quality of the initial guess and thus improves both the accuracy and efficiency of the combination. We validate the performance of the proposed method in comparison with existing methods through numerical examples of Gaussian mixture learning and its application to distributed particle filtering. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Gaussian mixture learning Adaptive hierarchical clustering Adaptive splitting expectation-maximization algorithm Distributed particle filtering

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Mathematical characterization of spatiotemporal congested traffic patterns: mixed speed data analysis in the greater Toronto and Hamilton area, Canada

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TRANSPORTATION PLANNING AND TECHNOLOGY 2016年第3期39卷 318-328页

作者： Kyriakopoulou, Natalia Photis, Yorgos N. Kanaroglou, Pavlos Natl Tech Univ Athens Sch Rural & Surveying Engn 9 Iroon PolytechZographou Campus Athens 15780 Greece McMaster Univ Sch Geog & Earth Sci Ctr Spatial Anal CSpA Hamilton ON L8S 4K1 Canada

This paper formulates a comprehensive methodology for analyzing, quantifying and identifying congestion characteristics based on speed distribution. Utilizing vehicle speed data, a mathematical approach is applied, in order to characterize roadway segments, in terms of travel reliability, congestion severity and duration. We argue that the Gaussian mixture model (GMM) and its parameter combination is the appropriate tool if we are to obtain quantitative congestion measures and rank roadway performance. A significant contribution of our approach is that it is based on assumptions regarding mixed components as well as speed distribution and can be applied to large databases. We test our framework on the greater Toronto and Hamilton area in Ontario, Canada, and conclude that congestion quantification through the application of the GMM can be successfully accomplished. Results indicate that speed patterns differ significantly between counties as well as days of the week.

关键词： bimodal distribution expectation-maximization algorithm Traffic congestion Gaussian mixture model

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Semiparametric empirical likelihood inference for abundance from one-inflated capture-recapture data

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BIOMETRICAL JOURNAL 2022年第6期64卷 1040-1055页

作者： Liu, Yang Li, Pengfei Liu, Yukun Zhang, Riquan East China Normal Univ Sch Stat KLATASDS MOE Shanghai 200062 Peoples R China Univ Waterloo Dept Stat & Actuarial Sci Waterloo ON Canada

Abundance estimation from capture-recapture data is of great importance in many disciplines. Analysis of capture-recapture data is often complicated by the existence of one-inflation and heterogeneity problems. Simultaneously taking these issues into account, existing abundance estimation methods are usually constructed on the basis of conditional likelihood under one-inflated zero-truncated count models. However, the resulting Horvitz-Thompson-type estimators may be unstable, and the resulting Wald-type confidence intervals may exhibit severe undercoverage. In this paper, we propose a semiparametric empirical likelihood (EL) approach to abundance estimation under one-inflated binomial and Poisson regression models. To facilitate the computation of the EL method, we develop an expectation-maximization algorithm. We also propose a new score test for the existence of one-inflation and prove its asymptotic normality. Our simulation studies indicate that compared with existing estimators, the proposed score test is more powerful and the maximum EL estimator has a smaller mean square error. The advantages of our approaches are further demonstrated by analyses of prinia data from Hong Kong and drug user data from Bangkok.

关键词： expectation-maximization algorithm empirical likelihood one-inflated capture-recapture data analysis score test

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An information geometric approach to ML estimation with incomplete data: Application to semiblind MIMO channel identification

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2007年第8期55卷 3975-3986页

作者： Zia, Amin Reilly, James P. Manton, Jonathan Shirani, Shahram McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4K1 Canada Australian Natl Univ Res Sch Informat Sci & Engn Dept Informat Engn Canberra ACT 0200 Australia

In this paper, we cast the stochastic maximum-likelihood estimation of parameters with incomplete data in an information geometric framework. In this vein, we develop the information geometric identification (IGID) algorithm. The algorithm consists of iterative alternating projections on two sets of probability distributions (PDs);i.e., likelihood PDs and data empirical distributions. A Gaussian assumption on the source distribution permits a closed-form low-complexity solution for these projections. The method is applicable to a wide range of problems;however, in this paper, the emphasis is on semiblind identification of unknown parameters in a multiple-input multiple-output (MIMO) communications system. It is shown by simulations that the performance of the algorithm [in terms of both estimation error and bit-error rate (BER)] is similar to that of the expectation-maximization (EM)-based algorithm proposed previously by Aldana et al., but with a substantial improvement in computational speed, especially for large constellations.

关键词： expectation-maximization algorithm information geometry maximum-likelihood estimation multiple-input multiple-output (MIMO) systems semiblind identification

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An EM algorithm-Based Disintegrated Channel Estimator for OFDM AF Cooperative Relaying

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IEICE TRANSACTIONS ON COMMUNICATIONS 2013年第1期E96B卷 254-262页

作者： Sheu, Jeng-Shin Sheen, Wern-Ho Natl Yunlin Univ Sci & Technol Dept Comp Sci & Informat Engn Touliu Yunlin Taiwan Chaoyang Univ Technol Dept Informat & Commun Engn Taichung Taiwan

The cooperative orthogonal frequency-division multiplexing (OFDM) relaying system is widely regarded as a key design for future broadband mobile cellular systems. This paper focuses on channel estimation in such a system that uses amplify-and-forward (AF) as the relaying strategy. In the cooperative AF relaying, the destination requires the individual (disintegrated) channel state information (CSI) of the source-relay (S-R) and relay-destination (R-D) links for optimum combination of the signals received from source and relay. Traditionally, the disintegrated CSIs are obtained with two channel estimators: one at the relay and the other at the destination. That is, the CSI of the S-R link is estimated at relay and passed to destination, and the CSI of the R-D link is estimated at destination with the help of pilot symbols transmitted by relay. In this paper, a new disintegrated channel estimator is proposed;based on an expectation-maximization (EM) algorithm, the disintegrated CSIs can be estimated solely by the estimator at destination. Therefore, the new method requires neither signaling overhead for passing the CSI of the S-R link to destination nor pilot symbols for the estimation of the R-D link. Computer simulations show that the proposed estimator works well under the signal-to-noise ratios of interest.

关键词： channel estimation orthogonal frequency-division multiplexing amplify-and-forward relaying expectation-maximization algorithm

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Poles tracking of weakly nonlinear structures using a Bayesian smoothing method

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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

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Weibull mixture estimation based on censored data with applications to clustering in reliability engineering

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第8期40卷 4247-4261页

作者： Lamalle, Florian Feuillard, Vincent Sabourin, Anne Clemencon, Stephan Renault Technoctr Guyancourt France Univ Paris Cite MAP5 Paris France Inst Polytech Paris Telecom Paris LTCI Paris France

It is the purpose of this paper to propose a novel clustering technique tailored to randomly censored data in reliability/survival analysis. It is based on an underlying mixture model of Weibull distributions and consists in estimating its parameters by means of a variant of the expectation-maximization method in the presence of random censorship. Beyond the description of the algorithm, model selection issues are addressed and we investigate its performance from an empirical perspective by applying it to possibly strongly censored (synthetic and real) survival data. The experiments carried out provide strong empirical evidence that our algorithm performs better than alternative methods standing as natural competitors in this framework.

关键词： censored data clustering expectation-maximization algorithm mixture model reliability/survival analysis

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