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2nd International Conference on Frontiers of Intelligent Manufacturing and Automation, CFIMA 2024

作者： Xu, Zeyu Sheng, Li Feng, Yang Shandong Qingdao China

ISBN: (纸本)9798400710681

Continuous wave mud pulse transmission system is widely exploited in the field of measurement while drilling for its prompt data transmission and good anti-interference ability. However, transmission quality of signals is generally subject to a variety of noises, resulting in a very low signal-to-noise ratio. As a consequence, the problem of continuous wave mud pulse signal denoising is investigated in this paper. To reconstruct and remove mud pump noise accurately under the condition of unknown noise covariance, an adaptive Kalman filter based on expectation maximization algorithm is introduced here by virtue of the state space model of mud pump noise. In addition, the remaining random noise in the signal still exists and accordingly, variational mode decomposition combined with wavelet threshold is developed. In the experiments, several groups of continuous wave mud pulse simulation signals and actual signals are denoised and analyzed to verify the effectiveness of the proposed method. © 2024 Copyright held by the owner/author(s).

关键词： expectation maximization algorithm

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A novel aerodynamic parameter estimation algorithm via sigma point Rauch-Tung-Striebel smoother using expectation maximization

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第3-Sup期22卷 S6795-S6806页

作者： Zhang, Wei Wang, Hongwei Liu, Yilei Zuo, Junyi Wang, Heping Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China Expt Aircraft Design & Flight Testing Lab Shaanxi Xian 710072 Shaanxi Peoples R China AVIC Aircraft Co Ltd Xian 710089 Shaanxi Peoples R China

We consider the problem of aerodynamic parameter estimation for aircraft dynamics modeled by a state space model where the statistic information of both the process and measurement noises are missing. To deal with the missing statistics, we propose in this work a new approach in which an augmented sigma point Rauch-Tung-Striebel (RTS) Kalman smoother is integrated with the expectation maximization (EM) algorithm. We define a new state vector by combining the original states and the unknown aerodynamic parameters. In addition, we impose a Gaussian random walk model for the unknown aerodynamic parameters and then build the extended state space model for the augmented RTS Kalman smoother. The expectation terms in the EM algorithm are approximated by the sigma point rule which is also applied in the augmented RTS Kalman smoother. Moreover, the non-convex optimization problem involved in the EM is solved in analytical forms rather than in numerical approaches. A comparative study of identifying the aerodynamic parameters of the flight test platform HFB-320 shows that the proposed approach achieves a substantial performance improvement over the existing ones, especially in terms of the convergence rate.

关键词： Aircraft system identification Parameter estimation expectation maximization algorithm Rauch-Tung-Striebel smoother Third-degree cubature rule

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A modified double-latent variable probabilistic model for monitoring of dynamic processes with multiple sampling rates

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2025年第0期

作者： Ying, Ze Chang, Yuqing Wang, Fuli He, Yuchen Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China China Jiliang Univ Coll Mech & Elect Engn Hangzhou Zhejiang Peoples R China

The monitoring of quality-correlated aspects in industrial production processes has become a crucial task in recent years. However, the challenges posed by multiple sampling rates and dynamic issues make it arduous to construct an efficient monitoring model. To address these issues, the present paper proposes a modified double-latent variable probabilistic (MDLVP) model that can deal with the measurement correlations across different sampling rates. Firstly, the MDLVP introduces two types of latent variables with minimum sample spacing for capturing quality-correlated and quality-uncorrelated information respectively. Secondly, a first-order Markov chain is utilized to describe the autocorrelation of the latent variables, thereby elucidating the dynamics of the multi-sampling rate process. The expectation-maximization (EM) algorithm is employed for the model training in an incomplete data collection. Finally, the model is adopted to develop a fault detection method, which is subsequently applied in two industrial cases. The experimental results demonstrate the superiority of the proposed model in handling dynamic in multi-sampling rate processes.

关键词： expectation maximization algorithm latent variable model multi-sampling rates process dynamics quality-related monitoring

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Multivariate degradation modeling and reliability evaluation using gamma processes with hierarchical random effects

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2025年 465卷

作者： Song, Kai Chinese Acad Sci Acad Math & Syst Sci State Key Lab Math Sci Beijing Peoples R China

Degradation data analysis provides an effective way to perform reliability evaluation for highly reliable products. In engineering practice, multiple performance characteristics are usually monitored simultaneously to reflect products' health status comprehensively, resulting in the multivariate degradation data. Analyzing such data for reliability modeling and evaluation is of great interest but challenging. In this paper, by means of hierarchical random effects, a novel multivariate gamma degradation model is proposed. The developed model takes the temporal randomness of degradation processes, the non-linearity of degradation, the unit-to-unit heterogeneity and the dependence among marginal degradation processes into consideration simultaneously. Then, the reliability function is derived analytically. Subsequently, unknown model parameters are estimated by integrating the expectation maximization algorithm and the variational inference technique, where the latter is employed to derive tractable conditional distributions of latent variables. Meanwhile, a procedure that provides plausible guesses of parameters is developed to initialize this estimation method. Further, approximate confidence intervals are constructed for uncertainty quantification. Finally, the proposed model and methods are illustrated and verified by simulation and case studies.

关键词： expectation maximization algorithm Gamma process Hierarchical random effects Multivariate degradation data Variational inference

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Modulation Parameters Recognition algorithm Based on expectation maximization for Continuous Phase Modulation

Modulation Parameters Recognition Algorithm Based on Expecta...

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20th International Conference on Electronics, Information, and Communication (ICEIC)

作者： Xue, Rui Wu, Kangran Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China

ISBN: (纸本)9781728191614

Continuous phase modulation (CPM) is widely used in short wave communications because of its high power efficiency and constant envelope characteristic. The modulation parameters recognition of the continuous phase modulated signals in non-cooperative communications is beneficial to restore information and impose interference. In order to overcome the disadvantage of slow convergence speed of expectation maximization (EM) algorithm for CPM parameters recognition, an improved EM algorithm is proposed to solve this problem. The improved algorithm employ Newton-Raphson (NR) iterative method to estimate the frequency offset, the channel gain is determined by the value of frequency offset, the variance of the complex additive white noise is acquired by the frequency offset and the channel gain. The simulation results show that the improved method has a faster convergence speed under the same iteration termination conditions. Moreover, the proposed algorithm has a better probability of correct recognition when the number of iterations is the same.

关键词： short wave communication continuous phase modulation modulation parameters recognition expectation maximization algorithm

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Machine Learning-Based Blind Signal Detection for Ambient Backscatter Communication Systems

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2025年第2期11卷 1172-1183页

作者： Zhu, Han Zhan, Jiamiao Lam, Chan-Tong Chen, Bidong Ng, Benjamin K. Macao Polytech Univ Fac Appl Sci Macau Peoples R China Baosight Software Nanchang Co Ltd Prod Res & Dev Dept Nanchang 330199 Peoples R China

Ambient backscatter communication (AmBC) is emerging as a promising energy-saving and spectrum-efficient passive Internet of Things (IoT) technology that can be used for battery-less communication devices due to its low power consumption and cost constraints. However, in AmBC systems, recovering tag information at the reader is a challenging problem due to the difficulty in obtaining relevant channel state information (CSI). In this paper, we propose a variational Bayesian inference and machine learning (VBI-ML) based blind signal detection method, which can automatically recover tag information in AmBC systems. Firstly, two known labels are transmitted from the tag to the reader before valid data is transmitted, thus eliminating the need for CSI estimation. Secondly, we use the VBI approach with the Gaussian mixture model to obtain the real constellation information, which does not require a priori signal modulation technology to recover the tag information at the reader automatically. Using real constellation information, the signal detection problem is converted into a clustering problem. Finally, we cluster all the received signals using an improved expectation maximization algorithm in ML to learn the parameters in labeled and unlabeled signals and recover the signals. Thorough simulation results demonstrate that our proposed method performs similarly to the optimal detector with perfect CSI and outperforms traditional constellation learning methods. More critically, ML algorithms can mitigate direct link interference and simplify the number of estimated parameters.

关键词： Radio frequency Signal detection Detectors Backscatter Clustering algorithms Internet of Things Modulation Ambient backscatter communication blind signal detection expectation maximization algorithm Gaussian mixture model variational Bayesian inference

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BRIDGE THE INFERENCE GAPS OF NEURAL PROCESSES VIA expectation maximization

arXiv

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arXiv 2025年

作者： Wang, Qi Federici, Marco van Hoof, Herke AMLab University of Amsterdam Amsterdam1098XH Netherlands

The neural process (NP) is a family of computationally efficient models for learning distributions over functions. However, it suffers from under-fitting and shows suboptimal performance in practice. Researchers have primarily focused on incorporating diverse structural inductive biases, e.g. attention or convolution, in modeling. The topic of inference suboptimality and an analysis of the NP from the optimization objective perspective has hardly been studied in earlier work. To fix this issue, we propose a surrogate objective of the target loglikelihood of the meta dataset within the expectation maximization framework. The resulting model, referred to as the Self-normalized Importance weighted Neural Process (SI-NP), can learn a more accurate functional prior and has an improvement guarantee concerning the target log-likelihood. Experimental results show the competitive performance of SI-NP over other NPs objectives and illustrate that structural inductive biases, such as attention modules, can also augment our method to achieve SOTA performance. Our code is available at https://***/hhq123gogogo/SI_NPs. Copyright © 2025, The Authors. All rights reserved.

关键词： expectation maximization algorithm

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Massively Parallel expectation maximization For Approximate Posteriors

arXiv

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arXiv 2025年

作者： Heap, Thomas Bowyer, Sam Aitchison, Laurence University of Bristol United Kingdom

Bayesian inference for hierarchical models can be very challenging. MCMC methods have difficulty scaling to large models with many observations and latent variables. While variational inference (VI) and reweighted wake-sleep (RWS) can be more scalable, they are gradient-based methods and so often require many iterations to converge. Our key insight was that modern massively parallel importance weighting methods (Bowyer et al., 2024) give fast and accurate posterior moment estimates, and we can use these moment estimates to rapidly learn an approximate posterior. Specifically, we propose using expectation maximization to fit the approximate posterior, which we call QEM. The expectation step involves computing the posterior moments using high-quality massively parallel estimates from Bowyer et al. (2024). The maximization step involves fitting the approximate posterior using these moments, which can be done straightforwardly for simple approximate posteriors such as Gaussian, Gamma, Beta, Dirichlet, Binomial, Multinomial, Categorical, etc. (or combinations thereof). We show that QEM is faster than state-of-the-art, massively parallel variants of RWS and VI, and is invariant to reparameterizations of the model that dramatically slow down gradient based methods. © 2025, CC BY.

关键词： expectation maximization algorithm

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Bivariate degradation modeling and reliability analysis based on generalized nonlinear Wiener processes and a shared frailty factor

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2025年第2期41卷 854-871页

作者： Li, Lu Wang, Zhihua Wu, Qiong Mao, Zelong Liu, Chengrui Beihang Univ Sch Aeronaut Sci & Engn Natl Key Lab Strength & Struct Integr Beijing Peoples R China China Acad Space Technol Inst Spacecraft Syst Engn Beijing Peoples R China China State Shipbuilding Corp Ltd Nav Instrument Res Inst Tianjin Peoples R China China Acad Space Technol Beijing Inst Control Engn Beijing Peoples R China

The complexity of product functionality and composition often involves at least two or more interrelated performance characteristics jointly reflecting the degradation. At the same time, the intricacy of the underlying failure mechanisms often results in degradation processes exhibiting intricate nonlinear patterns, where the mean and variance can display different nonlinear behaviors. Additionally, the random effect of the degradation process should not be overlooked. Therefore, an accurate depiction of degradation regulation is a crucial prerequisite for reasonable model construction and effective reliability analysis. To settle the challenges posed by complex nonlinear scenarios, this paper proposes a bivariate degradation model based on generalized nonlinear Wiener processes, incorporating a shared frailty factor to characterize the correlation between performance features. An approximate analytical expression for the cumulative distribution function of the first hitting time is derived for reliability analysis. Furthermore, unknown parameter estimation is carried out through a combination of the expectation-maximization algorithm and the genetic algorithm. Finally, the effectiveness of the proposed model and reliability analysis method is validated through a comprehensive simulation investigation and a practical case study involving fatigue crack propagation.

关键词： bivariate degradation expectation maximization algorithm frailty factor first hitting time generalized nonlinear Wiener process

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An Improved Gaussian Clustering algorithm to Accelerate the Solving of Integrated Optimization of Crude Oil Procurement and Blending

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2025年第13期64卷 7070-7084页

作者： Zheng, Wanpeng Gao, Xiaoyong Pan, Hui Zuo, Xin Huang, Dexian Zhang, Xintong Huang, Fuyu China Univ Petr Dept Automat Beijing 102249 Peoples R China PetroChina Dushanzi Petrochem Branch Co Xinjiang 833699 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

The crude oil procurement and blending process is the core link of production and operation in oil refining enterprises, and its integrated optimization is essential to guarantee blended oil property stabilization. However, the integrated model will be difficult or even impossible to solve if there are too many alternative crude oils. To handle this problem, an improved Gaussian clustering algorithm is proposed to select the appropriate crude oils for integrated optimization. Through this preselection, the search space is highly reduced, and thus the solving will be accelerated. First, an improved Gaussian Mixture Model (GMM) algorithm for crude oil clustering is detailed, and the expectation-maximization (EM) algorithm with price compensation function is proposed to reduce the influence of crude oil price fluctuation on the clustering results. Then, the effectiveness of the improved GMM algorithm was verified through actual cases and comparative experiments. The results showed that the improved GMM algorithm reduced the solution time by about 90%. The improved GMM algorithm can significantly improve the solving efficiency, and the EM algorithm with a price compensation function can effectively reduce the impact of crude oil price fluctuations on the clustering results. Finally, the proposed crude oil preselection-based optimization method lays the foundation and possibility for field application use.

关键词： expectation maximization algorithm

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