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2013 International Conference on Future Software Engineering and Multimedia Engineering(ICFM 2013)

作者： Ranran Liu Zhengming Li School of Electrical and Information Engineering Jiangsu University Changzhou Liu Guojun Vocational Technology College

Based on expectation-maximization algorithm, parameter estimation was proposed for data-driven nonlinear models in this work. On this basis, particle filters were used to approximately calculate integrals, deriving EM algorithm based on particle filter. And the effectiveness of using the proposed algorithm for the soft sensor of CO content in tail gas of PX oxidation side reactions was verified through simulation results.

关键词： expectation-maximization algorithm particle filtering data-driven soft sensor

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AN EFFICIENT ADAPTIVE ARITHMETIC CODING FOR BLOCK-BASED LOSSLESS IMAGE COMPRESSION USING MIXTURE MODELS

AN EFFICIENT ADAPTIVE ARITHMETIC CODING FOR BLOCK-BASED LOSS...

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IEEE International Conference on Image Processing

作者： Atef MASMOUDI Afif MASMOUDI William PUECH Sfax Preparatory Engineering Institute University of Sfax LIRMM UMR CNRS 5506 University of Montpellier II

ISBN: (纸本)9781479957521

In this paper, we investigate finite mixture models (FMM) and adaptive arithmetic coding (AAC) for block-based lossless image compression. The AAC performance depends on how well the model fits the source symbols' statistics. In addition, when encoding small block, the number of source symbols is considerably large by comparison with the number of samples in that block, which results in a loss of compression efficiency. To this end, we propose to model each block with an appropriately FMM by maximizing the probability of samples that belong to that block. The mixture parameters are estimated through maximum likelihood using the expectation-maximization (EM) algorithm in order to maximize the arithmetic coding efficiency. The comparative studies of some particular test images prove the efficiency of the mixture models for lossless image compression. The experimental results show significant improvements over conventional adaptive arithmetic encoders and the state-of-the-art lossless image compression standards and algorithms.

关键词： Arithmetic coding Finite mixture models Lossless image compression expectation-maximization algorithm

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A Globally-Variant Locally-Constant Model for Fusion of Labels from Multiple Diverse Experts without Using Reference Labels

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2013年第4期35卷 769-783页

作者： Audhkhasi, Kartik Narayanan, Shrikanth (Shri) Univ So Calif Dept Elect Engn SAIL Los Angeles CA 90089 USA

Researchers have shown that fusion of categorical labels from multiple experts-humans or machine classifiers-improves the accuracy and generalizability of the overall classification system. Simple plurality is a popular technique for performing this fusion, but it gives equal importance to labels from all experts, who may not be equally reliable or consistent across the dataset. Estimation of expert reliability without knowing the reference labels is, however, a challenging problem. Most previous works deal with these challenges by modeling expert reliability as constant over the entire data (feature) space. This paper presents a model based on the consideration that in dealing with real-world data, expert reliability is variable over the complete feature space but constant over local clusters of homogeneous instances. This model jointly learns a classifier and expert reliability parameters without assuming knowledge of the reference labels using the expectation-maximization (EM) algorithm. Classification experiments on simulated data, data from the UCI Machine Learning Repository, and two emotional speech classification datasets show the benefits of the proposed model. Using a metric based on the Jensen-Shannon divergence, we empirically show that the proposed model gives greater benefit for datasets where expert reliability is highly variable over the feature space.

关键词： Multiple diverse experts label fusion label reliability expectation-maximization algorithm human annotation emotion recognition

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Identifying driver heterogeneity in car-following based on a random coefficient model

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2013年 36卷 35-44页

作者： Kim, Ikki Kim, Taewan Sohn, Keemin Hanyang Univ Dept Transportat & Logist Engn Ansan Gyunggi Do South Korea Chung Ang Univ Dept Urban Engn Seoul 156756 South Korea

As computing capabilities have advanced, random coefficient models have emerged as the mainstream method of dealing with traveler behaviors in transport studies. Car-following models with random coefficients, however, are rarely used, although many kinds of car-following models have been attempted. For the present study, we proposed a rigorous methodology to calibrate a GM-type car-following model with random coefficients, which could account for the heterogeneity across drivers who respond differently to stimuli. To avert both the curse of dimensionality and the lack of empirical identification, which can be a part of dealing with a simulated likelihood, a robust algorithm called the expectation-maximization (EM) was adopted. The calibration results confirmed that random coefficients of the model fluctuated considerably across drivers, and were correlated with each other. The exclusion of these facts might be a potential reason for the difficulty in simulating real traffic situations based on a single car-following model with constant coefficients.

关键词： Car-following model Random coefficient Heterogeneity expectation-maximization algorithm Maximum simulated likelihood

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A CLUSTERING-BASED FUZZY WAVELET NEURAL NETWORK MODEL FOR SHORT-TERM LOAD FORECASTING

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INTERNATIONAL JOURNAL OF NEURAL SYSTEMS 2013年第5期23卷 1350024-1-1350024-19页

作者： Kodogiannis, Vassilis S. Amina, Mahdi Petrounias, Ilias Univ Westminster Sch Elect & Comp Sci London W1W 6UW England Univ Manchester Manchester Business Sch Manchester M15 6PB Lancs England

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the expectation maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models.

关键词： Short-term load forecasting fuzzy wavelet neural networks neural networks expectation-maximization algorithm fuzzy clustering

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Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches

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PATTERN RECOGNITION 2013年第10期46卷 2685-2699页

作者： Rafiee, G. Dlay, S. S. Woo, W. L. Newcastle Univ Sch Elect & Elect Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England

In this paper, a two-stage unsupervised segmentation approach based on ensemble clustering is proposed to extract the focused regions from low depth-of-field (DOE) images. The first stage is to cluster image blocks in a joint contrast-energy feature space into three constituent groups. To achieve this, we make use of a normal mixture-based model along with standard expectation-maximization (EM) algorithm at two consecutive levels of block size. To avoid the common problem of local optima experienced in many models, an ensemble EM clustering algorithm is proposed. As a result, relevant blocks closely conforming to image objects are extracted. In stage two, a binary saliency map is constructed from the relevant blocks at the pixel level, which is based on difference of Gaussian (DOG) and binarization methods. Then, a set of morphological operations is employed to create the region-of-interest (ROI) from the map. Experimental results demonstrate that the proposed approach achieves an F-measure of 91.3% and is computationally 3 times faster than the existing state-of-the-art approach. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Low depth-of-field Difference of Gaussian method Ensemble clustering expectation-maximization algorithm Region-of-interest extraction

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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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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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Joint segmentation of multivariate time series with hidden process regression for human activity recognition

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NEUROCOMPUTING 2013年 120卷 633-644页

作者： Chamroukhi, F. Mohammed, S. Trabelsi, D. Oukhellou, L. Amirat, Y. Univ Toulon & Var CNRS LSIS UMR 7296 F-83957 La Garde France Aix Marseille Univ CNRS ENSAM LSISUMR 7296 F-13397 Marseille France Univ Paris Est Creteil LISSI F-94400 Vitry Sur Seine France Univ Paris Est IFSTTAR GRETTIA F-93166 Noisy Le Grand France

The problem of human activity recognition is central for understanding and predicting the human behavior, in particular in a prospective of assistive services to humans, such as health monitoring, well being, security, etc. There is therefore a growing need to build accurate models which can take into account the variability of the human activities over time (dynamic models) rather than static ones which can have some limitations in such a dynamic context. In this paper, the problem of activity recognition is analyzed through the segmentation of the multidimensional time series of the acceleration data measured in the 3-d space using body-worn accelerometers. The proposed model for automatic temporal segmentation is a specific statistical latent process model which assumes that the observed acceleration sequence is governed by sequence of hidden (unobserved) activities. More specifically, the proposed approach is based on a specific multiple regression model incorporating a hidden discrete logistic process which governs the switching from one activity to another over time. The model is learned in an unsupervised context by maximizing the observed-data log-likelihood via a dedicated expectation-maximization (EM) algorithm. We applied it on a real-world automatic human activity recognition problem and its performance was assessed by performing comparisons with alternative approaches, including well-known supervised static classifiers and the standard hidden Markov model (HMM). The obtained results are very encouraging and show that the proposed approach is quite competitive even it works in an entirely unsupervised way and does not requires a feature extraction preprocessing step. (c) 2013 Elsevier B.V. All rights reserved.

关键词： Human activity recognition Acceleration data Times series segmentation Hidden process regression Unsupervised learning expectation-maximization algorithm

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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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