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检索条件"主题词=Expectation-Maximisation algorithm"

共 493 条记录，以下是351-360 订阅

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Knocking Detection in Gasoline Engines Based on Probability Density Functions: A Mixed Gaussian Distribution Approach

Knocking Detection in Gasoline Engines Based on Probability ...

引用

IEEE Annual Conference on Decision and Control

作者： Tatsuya Ibuki Yasuhiro Awai Yoshihiro Sakayanagi Mitsuji Sampei Junichi Kako Department of Mechanical and Control Engineering Tokyo Institute of Technology Tokyo 152-8550 JAPAN Advanced Engine Management System Development Div. Unit Center Toyota Motor Corporation Shizuoka 410-1193 JAPAN

ISBN: (纸本)9781479978878

This paper proposes an engine knocking detection approach based on probability density functions (PDFs). In this work, we suppose that the PDF of knocking intensity distribution can be approximated by a mixture of two Gaussian functions due to normal combustion and abnormal one. We first apply EM (expectation-Maximization) algorithm to actual engine data to show that the knocking intensity probability distribution can be successfully estimated by the mixed Gaussian distribution. We next try to apply the existing online EM algorithm in view of the actual implementation by an engine control unit. However, since the existing algorithm is built for the estimation of a fixed PDF, the effect of new input data is gradually attenuated and vanishes after long time. We thus newly propose a modified online EM algorithm so that the effect of the new input data is not attenuated. Finally, we perform simulations and an actual engine bench test for the validity of the present approach.

关键词： expectation-maximisation algorithm gasoline engine Gaussian distribution incoming data Probability density function engine pedestal

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Bin-EM-CEM algorithms of spherical parsimonious Gaussian mixture models for binned data clustering

Bin-EM-CEM algorithms of spherical parsimonious Gaussian mix...

引用

IEEE International Conference on Intelligent Engineering Systems

作者： Hani Hamdan Jingwen Wu SUPELEC Department of Signal Processing and Electronic Systems

ISBN: (纸本)9781479908271

EM algorithm is widely used in clustering domain because of its easy implementation and small storage space. CEM algorithm, which is considered as a classification version of EM algorithm, is another common used clustering algorithm. With the development of technology, we obtain more and more data. This results in slow computation of EM and CEM algorithms. Binning data seems to be efficient in gaining computation time by reducing the number of observations to the number of bins. Thus, EM and CEM algorithms applied to binned data were proposed: binned-EM and bin-EM-CEM algorithms. Moreover, fourteen parsimonious Gaussian mixture models, generated according to eigenvalue decomposition of the variance matrices of the mixture components, have less parameters than the most general model. By applying the EM and CEM algorithms of parsimonious models, estimation process is simplified and then accelerated. In this paper, to combine the advantages of binned data and parsimonious Gaussian mixture models, we develop bin-EM-CEM algorithms of spherical parsimonious Gaussian mixture models.

关键词： expectation-maximisation algorithm algorithms data clustering Sphericity Clustering algorithms

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LEARNING IN GAUSSIAN MARKOV RANDOM FIELDS

LEARNING IN GAUSSIAN MARKOV RANDOM FIELDS

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Thomas J. Riedl Andrew C. Singer Jun Won Choi University of Illinois at Urbana-Champaign

ISBN: (纸本)9781424442959

This paper addresses the problem of state estimation in the case where the prior distribution of the states is not perfectly known but instead is parameterized by some unknown parameter. Thus in order to support the state estimator with prior information on the states and improve the quality of the state estimates, it is necessary to learn this unknown parameter first. Here we assume a parameterized Gaussian Markov random field to model the prior distribution of the states and propose an algorithm that is able to learn its parameters from given observations on these states. The effectiveness of this approach is proven experimentally by simulations.

关键词： Gaussian Markov random field EM algorithm unsupervised learning sum-product algorithm factor graph Sum-product algorithm Factor Graph State estimation Unsupervised learning Markov random fields expectation-maximisation algorithm Prior distribution Gauss Unknown parameter Learning

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Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data Is Continuous and Weakly Labelled

Deep Hand: How to Train a CNN on 1 Million Hand Images When ...

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Oscar Koller Hermann Ney Richard Bowden Human Language Technology & Pattern Recog. RWTH Aachen University Centre for Vision Speech & Signal Processing University of Surrey

ISBN: (纸本)9781467388528

This work presents a new approach to learning a frame-based classifier on weakly labelled sequence data by embedding a CNN within an iterative EM algorithm. This allows the CNN to be trained on a vast number of example images when only loose sequence level information is available for the source videos. Although we demonstrate this in the context of hand shape recognition, the approach has wider application to any video recognition task where frame level labelling is not available. The iterative EM algorithm leverages the discriminative ability of the CNN to iteratively refine the frame level annotation and subsequent training of the CNN. By embedding the classifier within an EM framework the CNN can easily be trained on 1 million hand images. We demonstrate that the final classifier generalises over both individuals and data sets. The algorithm is evaluated on over 3000 manually labelled hand shape images of 60 different classes which will be released to the community. Furthermore, we demonstrate its use in continuous sign language recognition on two publicly available large sign language data sets, where it outperforms the current state-of-the-art by a large margin. To our knowledge no previous work has explored expectation maximization without Gaussian mixture models to exploit weak sequence labels for sign language recognition.

关键词： expectation-maximisation algorithm Hand frame level sign language recognition images Classifiers

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Anti-ship missile target tracking based on guidance law estimation

Anti-ship missile target tracking based on guidance law esti...

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International Conference on Information Fusion

作者： Gaoru Xue Xiaoxu Wang Wenchao Zhan Yan Liang Chunhui Zhao Ministry of Education Key Laboratory of Information Fusion Technology Xi'an Shaanxi P.R.China

A novel guidance law estimation filter (GLEF) based on expectation maximization (EM) algorithm to track anti-ship missile target with mode switching is proposed in this paper. Firstly, joint estimation and identification using EM is to minimize the parameters uncertainty and improve estimation accuracy, which is used only in the special environments in order to decrease the calculation burden. Secondly, a new filter framework just like “electric circuit fault detection” is designed to detect mode change or parameters change. Finally, anti-ship missile's threat index such as the time of flight remaining and the impact angle is estimated. The effectiveness of the proposed method and the performance of GLEF are examined through the numerical simulation.

关键词： target tracking estimation theory expectation-maximisation algorithm missile guidance missiles expectation-maximisation algorithm guided missile guidance Target tracking missiles estimation theory ANTISHIP MISSILES Impact angle parameter uncertainty State Change Defect detection

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A Global Structural EM algorithm for a Model of Cancer Progression 11

A Global Structural EM Algorithm for a Model of Cancer Progr...

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Annual Conference on Neural Information Processing Systems

作者： Ali Tofigh Erik Sjoelund Mattias Hoglund Jens Lagergren School of Computer Science McGill Centre for Bioinformatics McGill University Canada Stockholm Bioinformatics Center Stockholm University Sweden Department of Oncology Lund University Sweden Center Stockholm Bioinformatics Center School of Computer Science and Communication KTH Royal Institute of Technology Sweden

ISBN: (纸本)9781618395993

Cancer has complex patterns of progression that include converging as well as diverging progressional pathways. Vogelstein's path model of colon cancer was a pioneering contribution to cancer research. Since then, several attempts have been made at obtaining mathematical models of cancer progression, devising learning algorithms, and applying these to cross-sectional data. Beerenwinkel et al. provided, what they coined, EM-like algorithms for Oncogenetic Trees (OTs) and mixtures of such. Given the small size of current and future data sets, it is important to minimize the number of parameters of a model. For this reason, we too focus on tree-based models and introduce Hidden-variable Oncogenetic Trees (HOTs). In contrast to OTs, HOTs allow for errors in the data and thereby provide more realistic modeling. We also design global structural EM algorithms for learning HOTs and mixtures of HOTs (HOT-mixtures). The algorithms are global in the sense that, during the M-step, they find a structure that yields a global maximum of the expected complete log-likelihood rather than merely one that improves it. The algorithm for single HOTs performs very well on reasonable-sized data sets, while that for HOT-mixtures requires data sets of sizes obtainable only with tomorrow's more cost-efficient technologies.

关键词： Tumor Progression expectation-maximisation algorithm cancer research algorithms Dataset Global Maximum Representation of data mixtures learning algorithms Cancer of Colon

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Learning Temporally Consistent Rigidities

Learning Temporally Consistent Rigidities

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IEEE Conference on Computer Vision and Pattern Recognition

作者： Jean-Sebastien Franco Edmond Boyer LJK INRIA Grenoble Rhone-Alpes

ISBN: (纸本)9781457703942

We present a novel probabilistic framework for rigid tracking and segmentation of shapes observed from multiple cameras. Most existing methods have focused on solving each of these problems individually, segmenting the shape assuming surface registration is solved, or conversely performing surface registration assuming shape segmentation or kinematic structure is known. We assume no prior kinematic or registration knowledge except for an over-estimate k of the number of rigidities in the scene, instead proposing to simultaneously discover, adapt, and track its rigid structure on the fly. We simultaneously segment and infer poses of rigid subcomponents of a single chosen reference mesh acquired in the sequence. We show that this problem can be rigorously cast as a likelihood maximization over rigid component parameters. We solve this problem using an expectation Maximization algorithm, with latent observation assignments to reference vertices and rigid parts. Our experiments on synthetic and real data show the validity of the method, robustness to noise, and its promising applicability to complex sequences.

关键词： stiffness expectation-maximisation algorithm Rigid RIGID STRUCTURES segmentation Registration Flies Solve Noise robustness kinematics

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SCALABLE BAYESIAN NONPARAMETRIC DICTIONARY LEARNING

SCALABLE BAYESIAN NONPARAMETRIC DICTIONARY LEARNING

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European Signal Processing Conference

作者： Sarper Sertoglu John Paisley Department of Computer Science Columbia University Department of Electrical Engineering Columbia University

ISBN: (纸本)9781479988518

We derive a stochastic EM algorithm for scalable dictionary learning with the beta-Bernoulli process, a Bayesian nonparametric prior that learns the dictionary size in addition to the sparse coding of each signal. The core EM algorithm provides a new way for doing inference in nonparametric dictionary learning models and has a close similarity to other sparse coding methods such as K-SVD. Our stochastic extension for handling large data sets is closely related to stochastic variational inference, with the stochastic update for one parameter exactly that found using SVI. We show our algorithm compares well with K-SVD and total variation minimization on a denoising problem using several images.

关键词： expectation-maximisation algorithm Sparse coding Dictionaries as Topic one-parameter Stochastic Sludge volume index Learning Dataset Variational Inference

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A Novel Approach to Model Error Modelling using the expectation-Maximization algorithm

A Novel Approach to Model Error Modelling using the Expectat...

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IEEE Annual Conference on Decision and Control

作者： Ramon A. Delgado Graham C. Goodwin Rodrigo Carvajal Juan C. Aguero School of Electrical Engineering and Computer Science The University of Newcastle

ISBN: (纸本)9781467320658

In this paper we develop a novel approach to model error modelling. There are natural links to others recently developed ideas. However, here we make several key departures, namely (i) we focus on relative errors;(ii) we use a broad class of model error description which includes, inter alia, the earlier idea of stochastic embedding;(iii) we estimate both, the nominal model and undermodelling simultaneously using the expectation-Maximization (EM) algorithm. Simulation studies illustrate the performance of the proposed technique.

关键词： Model error modelling System identification EM algorithm expectation-maximisation algorithm System identification model error Modeling

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MATCHING NON-ALIGNED OBJECTS USING A RELATIONAL STRING-GRAPH

MATCHING NON-ALIGNED OBJECTS USING A RELATIONAL STRING-GRAPH

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IEEE International Conference on Image Processing

作者： Nicholas Dahm Yongsheng Gao Terry Caelli Horst Bunke Queensland Research Laboratory National ICT Australia School of Engineering Griffith University Brisbane Australia Victoria Research Laboratory National ICT Australia Institute of Computer Science and Applied Mathematics University of Bern Switzerland

ISBN: (纸本)9781479923427

Localising and aligning objects is a challenging task in computer vision that still remains largely unsolved. Utilising the syntactic power of graph representation, we define a relational string-graph matching algorithm that seeks to perform these tasks simultaneously. By matching the relations between vertices, where vertices represent high-level primitives, the relational string-graph is able to overcome the noisy and inconsistent nature of the vertices themselves. For each possible relation correspondence between two graphs, we calculate the rotation, translation, and scale parameters required to transform a relation into its counterpart. We plot these parameters in 4D space and use Gaussian mixture models and the expectation-maximisation algorithm to estimate the underlying parameters. Our method is tested on face alignment and recognition, but is equally (if not more) applicable for generic object alignment.

关键词： image alignment face recognition graph matching image registration Graph matching Facial Recognition images computer vision expectation-maximisation algorithm Graph representations vertex head scale parameter

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