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Adaptive Soft Sensor Modeling Based on Weighted Supervised Latent Factor Analysis with Selectively Integrated Moving Windows

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IFAC-PapersOnLine 2017年第1期50卷 10778-10783页

作者： Yao L. Ge Z. Yuan X. Wang P. State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University Hangzhou 310027 China School of Information Science and Engineering Central South University Changsha 410083 China School of Engineering Huzhou University Huzhou 313000 China

An adaptive soft sensor modeling method based on weighted supervised latent factor analysis is proposed. In conventional moving window based adaptive soft sensor, predictive model is constructed only with the latest process information. To fully take advantage of the past windows, a set of recent local models are integrated by the Bayes’ rule for quality estimation. However, the former built models may contain similar information about the process, and the redundancy would increase the calculation with a low-efficient accuracy improvement. Then a selecting method is proposed through a statistical hypothesis testing to determine whether a window dataset should be retained or not. In this way, the mostly informative models are left to integrate an efficient predictive model. A real industrial case demonstrates the feasibility and efficiency of the proposed adaptive soft sensor. © 2017

关键词： Adaptive soft sensor expectation maximization algorithm Model integration Moving window Quality prediction Supervised latent factor analysis Weighted model

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An image threshold selection method based on the Burr distribution

An image threshold selection method based on the Burr distri...

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Conference on Optoelectronic Imaging and Multimedia Technology II

作者： Xie, Xiaohong Wu, Rongteng Minjiang Univ Dept Teaching Affairs Fuzhou 350108 Fujian Peoples R China Minjiang Univ Dept Comp Sci Fuzhou 350108 Peoples R China

ISBN: (纸本)9780819493132

It is important to accurately fit the unknown probability density functions of background or object. To solve this problem, the Burr distribution is introduced. Three-parameter Burr distribution can cover a wide range of distribution. The expectation maximization algorithm is used to deal with the estimation difficulty in the Burr distribution model. The expectation maximization algorithm starts from a set of selected appropriate parameters' initial values, and then iterates the expectation-step and maximization-step until convergence to produce result parameters. The experiment results show that the Burr distribution could depicts quite successfully the probability density function of a significant class of image, and comparatively the method has low computing complexity.

关键词： image segmentation threshold Burr distribution histogram expectation maximization algorithm probability density function distribution family computer vision

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Load Pseudomeasurements in Distribution System State Estimation

Load Pseudomeasurements in Distribution System State Estimat...

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PowerTech, 2015 IEEE Eindhoven

作者： Pieri, Georgia Asprou, Markos Kyriakides, Elias Univ Cyprus Dept Elect & Comp Engn KIOS Ctr Intelligent Syst & Networks Nicosia Cyprus

ISBN: (纸本)9781479976959

Distribution system state estimation (DSSE) is an essential tool in active distribution grids in order to enhance the awareness of the power system operators regarding the state of the distribution system. One of the peculiarities of the DSSE is the lack of adequate real-time measurements. Thus, in this paper a three-phase DSSE algorithm enhanced with an advanced approach for modeling pseudomeasurements is proposed. The major innovation of the proposed methodology is the development of a DSSE algorithm based on limited real-time measurements. The proposed DSSE algorithm takes into account the unbalanced nature of the distribution systems and the presence of virtual measurements. The pseudomeasurements are obtained from historical data, through a Gaussian Mixture Model (GMM) methodology, improved by the Calinski & Harabasz (CH) criterion and the expectation maximization (EM) algorithm. The performance of the proposed methodology is illustrated on the IEEE 13 node test feeder and compared with the case where the pseudomeasurements are modeled through a normal distribution.

关键词： Calinski & Harabasz criterion Distribution System State Estimation expectation maximization algorithm Gaussian Mixture Model pseudomeasurements

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THE PERTURBED PROX-PRECONDITIONED SPIDER algorithm FOR EM-BASED LARGE SCALE LEARNING 21

THE PERTURBED PROX-PRECONDITIONED SPIDER ALGORITHM FOR EM-BA...

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IEEE Statistical Signal Processing Workshop (SSP)

作者： Fort, G. Moulines, E. Univ Toulouse IMT F-31062 Toulouse France CNRS F-31062 Toulouse France Ecole Polytech CMAP Route Saclay F-91128 Palaiseau France

ISBN: (纸本)9781728157672

Incremental expectation maximization (EM) algorithms were introduced to design EM for the large scale learning framework by avoiding the full data set to be processed at each iteration. Nevertheless, these algorithms all assume that the conditional expectations of the sufficient statistics are explicit. In this paper, we propose a novel algorithm named Perturbed Prox-Preconditioned SPIDER (3P-SPIDER), which builds on the Stochastic Path Integral Differential EstimatoR EM (SPIDER-EM) algorithm. The 3P-SPIDER algorithm addresses many intractabilities of the E-step of EM;it also deals with non-smooth regularization and convex constraint set. Numerical experiments show that 3P-SPIDER outperforms other incremental EM methods and discuss the role of some design parameters.

关键词： Statistical Learning Large Scale Learning expectation maximization algorithm Finite-sum Optimization Accelerated Stochastic Approximation Control Variates

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A direct method for the estimation of the sparse and latent variable components of a Gaussian graphical model 10

A direct method for the estimation of the sparse and latent ...

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10th International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Ondrusi, Martin Cribben, Ivor Alberta Sch Business Edmonton AB Canada

ISBN: (纸本)9798350373981;9798350373974

Graphical models have been extensively used in, and provided novel insights across, a wide variety of research areas. Most current graph estimation methods assume a sparse structure. However, a sparse-only assumption is limiting and unrealistic in practice, hence sparse and low-rank forms have been suggested. In this work, we propose a new estimation method called sparse low-rank inverse covariance estimation (SLICE), which is a direct approach to jointly estimate the sparse and latent components of a Gaussian graphical model. SLICE utilizes a simple regularization term to ensure accurate estimates that are congruent with the covariance matrix. We derive an efficient and generalizable pseudo expectation maximization (EM) algorithm to solve for this estimator. Simulation and neuroimaging studies demonstrate the improvement of our SLICE approach over current state-of-the-art methods.

关键词： expectation maximization algorithm

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Developing a Significant Nearest Neighbor Search Method for Effective Case Retrieval in a CBR System

Developing a Significant Nearest Neighbor Search Method for ...

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Spring Conference of the International-Association-of-Computer-Science-and-Information-Technology

作者： Tsai, Chieh-Yuan Chiu, Chuang-Cheng Yuan Ze Univ Dept Ind Engn & Management Chungli Taiwan

ISBN: (纸本)9780769536538

Case-based reasoning is a problem-solving technique commonly seen in artificial intelligence. A successful CBR system highly depends on how to design an effective case retrieval mechanism. The K-nearest neighbor (KNN) search method which selects the K most similar prior cases for a new case has been extensively used in the case retrieval phase of CBR. Although KNN can be simply implemented, the choice of the K value is quite subjective and wit] influence the performance of a CBR system. To eliminate the disadvantage, this research proposes a significant nearest neighbor (SNN) search method. In SNN, the probability density function of the dissimilarity distribution is estimated by the expectation maximization algorithm. Accordingly, the case selection can be conducted by determining whether the dissimilarity between a prior case and the new case is significant low based on the estimated dissimilarity distribution. The SNN search avoids human involvement in deciding the number of retrieved prior cases and makes the retrieval result objective and meaningful in statistics. The performance of the proposed SNN search method is demonstrated through a set of experiments.

关键词： nearest neighbor search statistical inference expectation maximization algorithm case-based reasoning

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No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change maximization for Active Learning 37

No Change, No Gain: Empowering Graph Neural Networks with Ex...

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37th Conference on Neural Information Processing Systems (NeurIPS)

作者： Song, Zixing Zhang, Yifei King, Irwin Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781713899921

Graph Neural Networks (GNNs) are crucial for machine learning applications with graph-structured data, but their success depends on sufficient labeled data. We present a novel active learning (AL) method for GNNs, extending the Expected Model Change maximization (EMCM) principle to improve prediction performance on unlabeled data. By presenting a Bayesian interpretation for the node embeddings generated by GNNs under the semi-supervised setting, we efficiently compute the closed-form EMCM acquisition function as the selection criterion for AL without re-training. Our method establishes a direct connection with expected prediction error minimization, offering theoretical guarantees for AL performance. Experiments demonstrate our method's effectiveness compared to existing approaches, in terms of both accuracy and efficiency.

关键词： expectation maximization algorithm

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Alternating minimization multigrid algorithms for transmission tomography

Alternating minimization multigrid algorithms for transmissi...

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Conference on Computational Imaging II

作者： O'Sullivan, JA Benac, J Washington Univ Dept Elect & Syst Engn Elect Syst & Signals Res Lab St Louis MO 63130 USA

ISBN: (纸本)0819452025

The problem of image formation for X-ray transmission tomography is formulated as a statistical inverse problem. The maximum likelihood estimate of the attenuation function is sought. Using convex optimization methods, maximizing the log-likelihood functional is equivalent to a double minimization of I-divergence, one of the minimizations being over the attenuation function. Restricting the minimization over the attenuation function to a coarse grid component forms the basis for a multigrid algorithm that is guaranteed to monotonically decrease the I-divergence at every iteration on every scale.

关键词： expectation maximization algorithm alternating minimization algorithms CT imaging transmission tomography multigrid methods

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Lung Nodule Segmentation using EM algorithm 6

Lung Nodule Segmentation using EM algorithm

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6th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Qian Yiming Weng Guirong Soochow Univ Sch Mech & Elect Engn Suzhou 215021 Peoples R China

ISBN: (纸本)9781479949557

Lung disease is often performed as nodules. Pulmonary nodule is one of important symbols of lung disease. Characteristics of pulmonary nodules always indicate the nature of lung disease. Detection of pulmonary nodules has great significance in diagnosing lung cancer. Study of pulmonary nodules is now a hot research. CT is a new type of medical imaging equipment with a high density resolution and adequate image information. But to detect small pulmonary nodes, radiologists need to read a lot of images. It could easily lead to misdiagnosis and miss-diagnosis. This paper uses EM algorithm in CT images for the lung nodule detection and segmentation. The application shows that the method of this paper is to improve the early detection rate of lung cancer nodules and one of the effective methods using computer-aided analysis of lung nodules. The algorithm used is simple, effective and practical.

关键词： lung nodule mathematical morphology expectation maximization algorithm

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CLASSIFICATION OF COMPLEX PEDESTRIAN ACTIVITIES FROM TRAJECTORIES

CLASSIFICATION OF COMPLEX PEDESTRIAN ACTIVITIES FROM TRAJECT...

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IEEE International Conference on Image Processing

作者： Nascimento, Jacinto C. Marques, Jorge S. Figueiredo, Mario A. T. Inst Super Tecn Inst Sistemas & Robot P-1049001 Lisbon Portugal Inst Super Tecn Inst Telecommun P-1049001 Lisbon Portugal

ISBN: (纸本)9781424479948

We propose a method to classify human trajectories, modeled by a set of motion vector fields, each tailored to describe a specific motion regime. Trajectories are modeled as being composed of segments corresponding to different motion regimes, each generated by one of the underlying motion fields. Switching among the motion fields follows a probabilistic mechanism, described by a field of stochastic matrices. This yields a space-dependent motion model which can be estimated using an expectation-maximization (EM) algorithm. To address the model selection question (how many fields to use?), we adopt a discriminative criterion based on classification accuracy on a held out set. Experiments with real data (human trajectories in a shopping mall) illustrate the ability of the proposed approach to classify complex trajectories into high level classes (client versus non-client).

关键词： complex pedestrian activity expectation maximization algorithm expectation-maximisation algorithm human trajectory classification image classification motion estimation motion vector field probabilistic mechanism space dependent motion model stochastic matrices video surveillance

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