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检索条件"主题词=Expectation maximization algorithm"

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Research on DOA estimation method of millimeter wave radar based on sparse reconstruction 4

Research on DOA estimation method of millimeter wave radar b...

引用

2024 4th International Conference on Optics and Communication Technology, ICOCT 2024

作者： Lv, Chao Liu, Dongqi Li, Guozheng Huang, Xun Changchun University of Science and Technology China

ISBN: (纸本)9781510685734

In this paper, an adaptive grid DOA estimation algorithm based on sparse Bayesian learning (LMSBL) is proposed. Compared with the traditional off-grid EM-SBL algorithm, the proposed algorithm overcomes the problem of insufficient estimation caused by excessive grid spacing, improves the estimation accuracy and shorens the program running time under the condition of low SNR. In this algorithm, the grid position is regarded as variable, and the fast factor maximization method is combined to estimate the grid position coarse by dichotomous method, and then fine-estimated by root-updating grid algorithm. Experimental results show that the proposed algorithm is more accurate and faster than the off-grid EM-SBL algorithm under the condition of low SNR and large grid spacing. © 2024 SPIE.

关键词： expectation maximization algorithm

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Failure Mode Analysis and Residual Life Assessment of Prefabricated Target Neutron Tubes 6

Failure Mode Analysis and Residual Life Assessment of Prefab...

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6th International Conference on System Reliability and Safety Engineering, SRSE 2024

作者： Lei, Bai-Mao Jiang, Chen-Yu Chen, Qiang Zhu, Jia-Wei Guangzhou China Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology Guangzhou China Key Laboratory of Active Medical Devices Quality & Reliability Management and Assessment Guangzhou China

ISBN: (纸本)9798350356083

The failure mechanism as well as the life prediction of neutron tubes have attracted much attention for the wide application of neutron tubes in many domains. In this paper, the failure modes of prefabricated deuterium-tritium neutron tubes were firstly studied. Degradation models considering the time-varying uncertainties, individual differences and measurement uncertainties were used to investigate the random degradation process of neutron tubes. Life tests of neutron tubes were conducted at room temperature and 175 C in laboratory. A logarithmic form function was proposed to describe the nonlinear degradation characteristics of neutron tubes. Recursive parameter estimation was performed using the expectation maximization algorithm and the genetic algorithm. Finally, the residual life distribution of each neutron tube was obtained by the proposed models and estimated parameters. The results showed that the nonlinear degradation process modelling and residual life assessment method could be applicable for the stable operation period of prefabricated neutron tubes when the neutron yield decreases continuously. © 2024 IEEE.

关键词： expectation maximization algorithm

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Consistent Submodular maximization 41

Consistent Submodular Maximization

引用

41st International Conference on Machine Learning, ICML 2024

作者： Dütting, Paul Fusco, Federico Lattanzi, Silvio Norouzi-Fard, Ashkan Zadimoghaddam, Morteza Google Research United States Sapienza University of Rome Rome Italy

Maximizing monotone submodular functions under cardinality constraints is a classic optimization task with several applications in data mining and machine learning. In this paper we study this problem in a dynamic environment with consistency constraints: elements arrive in a streaming fashion and the goal is maintaining a constant approximation to the optimal solution while having a stable solution (i.e., the number of changes between two consecutive solutions is bounded). We provide algorithms in this setting with different trade-offs between consistency and approximation quality. We also complement our theoretical results with an experimental analysis showing the effectiveness of our algorithms in real-world instances. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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The Generalized Laws of Total Variance and Total Covariance 1st

The Generalized Laws of Total Variance and Total Covariance

引用

1st Southern Georgia Mathematics Conference, SGMC 2021

作者： Champ, Charles W. Sills, Andrew V. Department of Mathematical Sciences Georgia Southern University StatesboroGA United States

ISBN: (纸本)9783031697050

The law of total variance states that the unconditional variance of a random variable Y is the sum of (a) the variance of the conditional expectation of Y given X and (b) the expectation of the conditional variance of Y given the random variable X. We show that the total variance of Y can be partitioned by using the relationship between Y and one or more random variables X1,…,Xk, where k≥1. An application in multivariate analysis is given. Further, we generalize the total law of total covariance and show that the generalized law of total variance is a special case. Some examples are referenced. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： expectation maximization algorithm

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Blind Detection Based on Asymmetric Constellations for MIMO Systems 7

Blind Detection Based on Asymmetric Constellations for MIMO ...

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7th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2024

作者： Li, Xing School of Biomedical Engineering Guangdong Medical University Dongguan523808 China

ISBN: (纸本)9798350370782

In this paper, we propose a blind detection method based on asymmetric constellations for multiple-input multiple-output (MIMO) systems. In block fading channels, the transmission information sequence is randomly and uniformly generated from the constellation set, which results in signals classification at the receiver. We exploit the system constrained Gaussian mixture model (SCGMM) to detect information sequence. Specifically, we exploit the a posteriori probabilities (APPs) calculated by the expectation-maximization (EM) algorithm to recognize signals. For blind detection, we develop an unsupervised learning scheme based on asymmetric constellations, which solves the permutation ambiguity problem by asymmetrically designing traditional constellations. To obtain robust initial centroids, we propose an asymmetric constellations-based initialization algorithm, which can reduce the estimated error and accelerate convergence of the SCGMM clustering. The simulation results show that the bit-error-rate (BER) performance of the proposed method can approach the performance with perfect channel state information (CSI) in the high signal-to-noise (SNR) regions. © 2024 IEEE.

关键词： expectation maximization algorithm

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A Real-Valued Off-Grid DOA Estimator Via Sparse Bayesian Learning Exploiting Wishart prior 7

A Real-Valued Off-Grid DOA Estimator Via Sparse Bayesian Lea...

引用

7th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2024

作者： Li, Ninghui Zhang, Xiaokuan Feng, Weike Lv, Fan Wang, Zhaolong Zong, Binfeng Air Force Engineering University Graduate School Xi'an China Air Force Engineering University Air Defense and Antimissile School Xi'an China

ISBN: (纸本)9798350384437

For sparse Direction-of-Arrival (DOA) estimation problems, sparse Bayesian learning (SBL) has achieved excellent performance. As a combination of observation and priors, SBL-based methods exploit priors as regularization constraint to promote sparsity. Unfortunately, canonical Gaussian priors cannot enhance sparsity well, while hierarchical priors show satisfactory sparsity performance. However, most developed methods based on hierarchical priors use Gamma priors, which cannot model the correlation between different variables to be solved and cannot promote sparsity performance to a high level. On this basis, the paper uses Wishart prior to model the correlation and enhance sparsity further, yielding a novel Bayesian framework. Based on the framework, we rigorously deduce the corresponding SBL iterative algorithm by an expectation-maximization (EM) procedure. Moreover, to avoid the unrobust complex operations and intractable off-grid gaps, a real-valued transformation is adopted and the off-grid compensation is completed. Extensive simulations are presented to illustrate the superiority of the proposed method beyond state-of-the-art methods. © 2024 IEEE.

关键词： expectation maximization algorithm

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Residual Neural Networks for the Prediction of the Regularization Parameters in PET Reconstruction 46

Residual Neural Networks for the Prediction of the Regulariz...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Moussaoui, Younes Mateus, Diana Moussaoui, Saïd Carlier, Thomas Stute, Simon Nantes Université École Centrale Nantes LS2N Cnrs Umr 6004 NantesF-44300 France Chu Nantes Service de Médecine Nucléaire NantesF-44000 France Nantes Université CRCI2NA NantesF-44000 France

ISBN: (纸本)9798350371499

Positron Emission Tomography (PET) is a medical imaging modality relying on numerical methods that integrate the statistical properties of the measurements and prior assumptions about the images. In order to maximize the computed image quality, PET reconstruction algorithms require the setting of hyperparameters that balance data fidelity with regularization. However, their optimal tuning depends on the statistical properties of the raw data and on the clinical objectives. To address this issue, we propose a supervised deep learning strategy based on a residual neural network that takes the raw measured data (sinogram) as input and automatically predicts the optimal value of the regularization parameter of the modified block Sequential Regularized expectation maximization (BSREM) algorithm. The proposed strategy is trained on a synthetic dataset consisting of 2D sinograms and their corresponding optimal regularization parameters. Our results demonstrate the feasibility of the approach leading to improved image reconstruction compared to classical manual tuning methods. © 2024 IEEE.

关键词： expectation maximization algorithm

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Multivariate Bounded Support Kotz Mixture Model with Minimum Message Length Criterion

Multivariate Bounded Support Kotz Mixture Model with Minimum...

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2024 International Symposium on Networks, Computers and Communications, ISNCC 2024

作者： Araya, Tsega Weldu Azam, Muhammad Bouguila, Nizar Bentahar, Jamal Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada School of Computer Science and Technology Algoma University Sault Ste. MarieON Canada

ISBN: (纸本)9798350364910

In this paper, we present a multivariate bounded Kotz mixture model (BKMM) for data modeling when the data lies in a bounded support region. In BKMM, parameter estimation is performed by maximizing the log-likelihood through expectation-maximization (EM). Model selection in mixtures is considered essential for determining the optimal number of mixture components. Thus, we propose a model selection criterion for BKMM using minimum message length (MML). Initially, we validate the proposed model selection criteria for identifying the exact number of components using five medical disease diagnosis data. Additionally, to further assess its performance, we examined the model using various image datasets such as Alzheimer, lung tissue, gastrointestinal tract and object categorization. The results of MML are compared with seven different model selection criteria to examine the effectiveness of the proposed model. The experimental results demonstrate the effectiveness of the BKMM and MML. © 2024 IEEE.

关键词： expectation maximization algorithm

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Statistical inference for the generalized inverse exponential distribution under progressive first failure censoring scheme

Statistical inference for the generalized inverse exponentia...

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2024 International Conference on algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Li, Zhaohui Faculty of Social Sciences University of Southampton Southampton United Kingdom

ISBN: (纸本)9781510685840

This article considers the point estimations and interval estimations for a generally inverse exponential distribution on the basis of the progressive first failure censoring. We derive the maximum likelihood estimators along with corresponding asymptotic confidence interval at first. Next, we compute the maximum likelihood through the utilization of the expectation maximization (EM) algorithm. Afterwards, Bayesian inference is applied using both symmetric and asymmetric loss functions in scenarios involving either informative or non-informative priors. The M-H algorithm is utilized to compute point estimation, which leads to the highest posterior density credible intervals simultaneously. Eventually, a numerical simulation test is executed for the sake of evaluating the qualities of estimations mentioned and authentic dataset is investigated. © 2024 SPIE.

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Achieving Margin maximization Exponentially Fast via Progressive Norm Rescaling 41

Achieving Margin Maximization Exponentially Fast via Progres...

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41st International Conference on Machine Learning, ICML 2024

作者： Wang, Mingze Min, Zeping Wu, Lei School of Mathematical Sciences Peking University Beijing China Center for Machine Learning Research Peking University Beijing China

In this work, we investigate the margin-maximization bias exhibited by gradient-based algorithms in classifying linearly separable data. We present an in-depth analysis of the specific properties of the velocity field associated with (normalized) gradients, focusing on their role in margin maximization. Inspired by this analysis, we propose a novel algorithm called Progressive Rescaling Gradient Descent (PRGD) and show that PRGD can maximize the margin at an exponential rate. This stands in stark contrast to all existing algorithms, which maximize the margin at a slow polynomial rate. Specifically, we identify mild conditions on data distribution under which existing algorithms such as gradient descent (GD) and normalized gradient descent (NGD) provably fail in maximizing the margin efficiently. To validate our theoretical findings, we present both synthetic and real-world experiments. Notably, PRGD also shows promise in enhancing the generalization performance when applied to linearly non-separable datasets and deep neural networks. Copyright 2024 by the author(s)

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