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检索条件"主题词=Expectation Maximization Algorithm"

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Vehicle State Estimation algorithm for Intelligent Chassis Control with Noise Statistics Updates

Vehicle State Estimation Algorithm for Intelligent Chassis C...

引用

2024 IEEE International Conference on Unmanned Systems, ICUS 2024

作者： Wang, Yan Zhang, Zhiguo Tian, Feng Sun, Xuan Zhang, Fengjiao Liu, Qingchao Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong School of Mechanical Engineering Southeast University Nanjing China School of Vehicle and Mobility Tsinghua University Beijing China School of Traffic and Transportation Beijing Jiaotong University Beijing China Changzhou Vocational Institute of Mechatronic Technology Changzhou China Automotive Engineering Research Institute Jiangsu University Jiangsu China

ISBN: (纸本)9798350384185

Sideslip angle, yaw rate, and vehicle speed are critical for intelligent chassis control. Existing vehicle state estimation studies seldom simultaneously consider the effects of data loss and noise variations on estimation accuracy. To address these issues, we propose the expectation-maximizing robust extended Kalman filter (EMREKF). First, we develop the robust extended Kalman filter (REKF) to handle the effects of missing measurements. Then, we propose an expectation-maximization-based noise dynamic update method to adjust the noise parameters of the REKF. Simulation results show that the EMREKF outperforms both the conventional REKF and EKF. © 2024 IEEE.

关键词： expectation maximization algorithm

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Maximizing Influence of Nodes with Rapid Global Spread in Social Media Data Strea 22

Maximizing Influence of Nodes with Rapid Global Spread in So...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Tian, Ze-Peng Shi, Lei-Lei Liu, Lu Han, Zi-Xuan Hao, Zhou Daniel Antonopoulos, Nick Jiangsu University School of Computer Science and Communication Engineering China University of Exeter Department of Computer Science United Kingdom University of Leicester School of Computing and Mathematical Science United Kingdom Edinburgh Napier University Principal's Office United Kingdom

ISBN: (纸本)9798331509712

Influence maximization plays a pivotal role in areas such as cybersecurity, public opinion management, and viral marketing. However, conventional methods for influence maximization grapple with challenges such as seed node clustering, information loss during influence propagation, and biases in overlapping influence consideration. In response to these issues, we introduce Fast Unfold Diffusion (FUD)- an influence maximization algorithm capitalizing on the uniform distribution characteristics in node space and the non-overlapping local features of nodes. This approach aims to facilitate swift and extensive global diffusion of node influence. The performance of our algorithm was validated through the execution of two hundred diffusion simulations, employing the Independent Cascade (IC) diffusion model across six real-world datasets. Compared to the latest algorithms, our algorithm offers substantial benefits in expanding the range of influence diffusion, the speed of influence diffusion and the stability of the algorithm. This not only effectively mitigates the problems found in traditional methodologies but also ensures a significant enhancement in the efficiency of influence maximization. © 2024 IEEE.

关键词： expectation maximization algorithm

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Path Re-Planning for Pneumatic Continuum Manipulator Based on GMM/GMR-MPRM 14

Path Re-Planning for Pneumatic Continuum Manipulator Based o...

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14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Cheng, Qiang Chen, Wenlin Zhang, Wei Zhang, Ying Hao, Lina School of Mechanical Engineering and Automation Northeastern University Shenyang110819 China School of Resources and Civil Engineering Northeastern University Shenyang110819 China Changcheng Institute of Measurement & Metrology Beijing100095 China

ISBN: (纸本)9798331506056

The continuum manipulator has the characteristics of flexibility and compliance. However, the path re-planning problem of the continuum manipulator remain challenging. Aiming at the issue of path replanning for continuum manipulator, this study adopts a path re-planning algorithm based on a Gaussian Mixture Model (GMM), Gaussian Mixture Regression (GMR), and a Modified Probabilistic Roadmap (MPRM). This algorithm helps to avoid collisions with obstacles by re-planning the path of the continuum manipulator. Firstly, this algorithm employs the expectation-maximization (EM) algorithm to model the desired path data as a compact GMM model. Subsequently, through GMR, it integrates the data model into a smooth path with variance. Then, the collision points along the path are sampled using a variable density resampling method to generate a sampling space, and a MPRM is constructed based on EPD. Finally, a Dijkstra algorithm is employed to search for feasible paths within this MPRM. Through simulation and physical experiments, it is verified that the GMM/GMR-MPRM path re-planning algorithm can simultaneously ensure tracking of the desired path while avoiding obstacles. © 2024 IEEE.

关键词： expectation maximization algorithm

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Learning a spelling error model from search query logs 05

Learning a spelling error model from search query logs

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Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, HLT/EMNLP 2005, Co-located with the 2005 Document Understanding Conference, DUC and the 9th International Workshop on Parsing Technologies, IWPT

作者： Ahmad, Farooq Kondrak, Grzegorz Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Computing Science University of Alberta Edmonton Canada

Applying the noisy channel model to search query spelling correction requires an error model and a language model. Typically, the error model relies on a weighted string edit distance measure. The weights can be learned from pairs of misspelled words and their corrections. This paper investigates using the expectation maximization algorithm to learn edit distance weights directly from search query logs, without relying on a corpus of paired words. © 2005 Association for Computational Linguistics.

关键词： 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...

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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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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.

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Consistent Submodular maximization 41

Consistent Submodular Maximization

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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)

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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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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.

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