Article
Management
Young H. Chun
Summary: Researchers have proposed various multiple inspection plans to minimize the total cost of inspection and misclassifications. In this study, a new Markovian inspection plan is introduced, where items are repeatedly tested until a sufficient number of positive or negative results are obtained. The use of the expectation-maximization (EM) algorithm in estimating model parameters is also discussed, showing the superior performance of the new inspection plan over previous ones in numerical analysis.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2023)
Article
Engineering, Mechanical
Jing Chen, Yawen Mao, Manfeng Hu, Liuxiao Guo, Quanmin Zhu
Summary: This study proposes a decomposition optimization-based expectation maximization algorithm for switching models. The identities of each sub-model are estimated in the expectation step, while the parameters are updated using the decomposition optimization method in the maximization step. Compared with the traditional expectation maximization algorithm and the gradient descent expectation maximization algorithm, the decomposition optimization-based expectation maximization algorithm avoids the matrix inversion and eigenvalue calculation; thus, it can be extended to complex nonlinear models and large-scale models. Convergence analysis and simulation examples are given to show the effectiveness of the proposed algorithm.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Electrical & Electronic
Mohammed Rashid, Mort Naraghi-Pour
Summary: This paper studies the problem of downlink channel estimation in multi-user massive MIMO systems. Bayesian compressive sensing approach is employed to reduce the pilot overhead by utilizing the clustered sparse structure of the channel in the angular domain. An EP algorithm is developed to approximate the joint distribution of the sparse vector and its support, and it is used for direct estimation of the channel. EM algorithm is used to estimate the unknown model parameters. Simulation results show that the proposed EM-EP algorithm outperforms several recently-proposed algorithms.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Ying Liu, Long Chen, Yunchong Li, Jun Zhao, Wei Wang
Summary: An echo state network (ESN) model with input noise is proposed to predict time series with noise. The states in the ESN are approximated by linearizing it through an extended Kalman filter (EKF) due to the introduction of input noise. The expectation maximization algorithm (EM) is used to iteratively update all uncertain parameters for model learning.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Rahul Singh, Qinsheng Zhang, Yongxin Chen
Summary: In this paper, an algorithm is proposed for estimating the parameters of a time-homogeneous hidden Markov model (HMM) from aggregate observations. The algorithm is built upon the expectation-maximization algorithm and the aggregate inference algorithm, and it exhibits convergence guarantees for both discrete and continuous observations. When the population size is 1, the algorithm is equivalent to the standard Baum-Welch learning algorithm.
Article
Engineering, Chemical
Ruijing Han, Yousef Salehi, Biao Huang, Vinay Prasad
Summary: The approach presented integrates slow measurements with variable time delays and fast measurements to improve parameter estimation accuracy. By utilizing a particle filter-based approach within the expectation maximization algorithm framework, the method demonstrates effectiveness and applicability in various scenarios such as simulation examples, hybrid tank experiments, and industrial case studies.
Article
Mathematics, Applied
Mohamed S. Eliwa, Essam A. Ahmed
Summary: This study investigates point and interval estimations of the Lomax distribution in constant stress partially accelerated life tests (ALTs) using progressive first failure type-II censored samples. The point estimates of unknown parameters and acceleration factor are obtained through maximum likelihood and Bayesian approaches. The results are reported using the Markov Chain Monte Carlo (MCMC) technique for both symmetric and asymmetric loss functions. Monte Carlo simulation studies are performed to compare results under different sample sizes. The proposed methods are then applied to oil breakdown time data in insulating fluid under two high-test voltage stress levels.
Article
Mathematics
Xiaowei Dong, Feng Sun, Fangchao Xu, Qi Zhang, Ran Zhou, Liang Zhang, Zhongwei Liang
Summary: This study effectively addressed the issue of multi-parameter estimation in the reliability analysis of electromechanical products by establishing a hybrid Weibull distribution model and designing an optimization algorithm.
Article
Computer Science, Information Systems
Carlo Mari, Cristiano Baldassari
Summary: An efficient initialization method for the Expectation-Maximization algorithm is proposed in this paper to estimate mixture models. By mapping time series to complex networks using the Markov Transition Field, a fully unsupervised network-based initialization technique is provided. Experimental results on financial time series with diverse characteristics demonstrate the effectiveness of the proposed method.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Florian Hirschberger, Dennis Forster, Joerg Luecke
Summary: This research presents a clustering algorithm that uses a variational approach to optimize Gaussian mixture models. The algorithm achieves efficient operation on large-scale datasets by approximating expectation maximization through truncated posteriors and coreset-based partial E-steps. The algorithm demonstrates sublinear scaling and significant speed improvements compared to existing methods.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Biochemical Research Methods
Yi Yang, Xingjie Shi, Wei Liu, Qiuzhong Zhou, Mai Chan Lau, Jeffrey Chun Tatt Lim, Lei Sun, Cedric Chuan Young Ng, Joe Yeong, Jin Liu
Summary: Spatial transcriptomics is a powerful technique for analyzing gene expression and spatial information in tissues. This study presents a method called SC-MEB for identifying cell clusters based on spatial information, and demonstrates its superiority through simulations and real data analysis.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Mathematics, Applied
Zeljko Kereta, Robert Twyman, Simon Arridge, Kris Thielemans, Bangti Jin
Summary: This study investigates two classes of algorithms for accelerating OSEM based on variance reduction, resulting in significantly improved efficiency and accuracy for penalized PET reconstructions.
Article
Chemistry, Analytical
Ossi Kaltiokallio, Roland Hostettler, Huseyin Yigitler, Mikko Valkama
Summary: This paper introduces an Expectation-Maximization (EM) algorithm based on Gaussian smoothing to estimate unknown RSS model parameters for device-free localization and tracking (DFLT) without the need for supervised training or calibration periods. The proposed system improves the accuracy of existing DFLT methods, is computationally efficient, and outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.
Article
Mathematics
Omar M. Abou Al-Ola, Ryosuke Kasai, Yusaku Yamaguchi, Takeshi Kojima, Tetsuya Yoshinaga
Summary: This article introduces a new algorithm that combines the advantages of different iterative schemes by combining ordered-subsets EM and MART with weighted geometric or hybrid means, achieving a decrease in the objective function with each iteration and outperforming OS-EM and OS-MART alone. The algorithm shows excellent performance in image reconstruction experiments, especially in dealing with noise issues.
Article
Engineering, Electrical & Electronic
Sinan Yildirim, Mohammad Khalafi, Tayyar Guzel, Halil Satik, Murat Yilmaz
Summary: We propose a new time series model for short-term electricity market supply curves that considers the contribution of different resources and external factors. The model is equipped with a unified Monte Carlo methodology for tracking latent variables and forecasting. We introduce a sequential Markov chain Monte Carlo algorithm for tracking latent variables and two stochastic variants of the expectation-maximization algorithm for hyperparameter estimation. The proposed framework is applied to the Turkish electricity market with demonstrated performance on real market data.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Automation & Control Systems
Yue Cao, Nabil Magbool Jan, Biao Huang, Yalin Wang, Zhuofu Pan, Weihua Gui
Summary: The primary goal of multimodal process monitoring is to detect abnormalities or occurrence of faults. This paper proposes a Gaussian mixture model based variational Bayesian principal component analysis (GMM-VBPCA) method that combines global GMM and local VBPCA models to better characterize normal multimodal processes. The monitoring statistics of KL divergence and model residuals are used to detect fault occurrences.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yu Hui, Ronghu Chi, Biao Huang, Zhongsheng Hou
Summary: This study addresses the challenges of heterogeneous dynamics, strongly non-linear and non-affine structures, and cooperation-antagonism networks in multi-agent systems output consensus. It introduces a heterogeneous linear data model and an adaptive learning consensus protocol to improve system performance effectively.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Yan Qin, Chau Yuen, Xunyuan Yin, Biao Huang
Summary: To address the data discrepancy across batteries, researchers propose a transferable multistage SOH estimation model that outperforms its competitors in various transfer tasks. By using stage information and an updating scheme to compensate for estimation errors, the model significantly improves estimation accuracy.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Engineering, Electrical & Electronic
Xiuli Zhu, Seshu Kumar Damarla, Kuangrong Hao, Biao Huang, Hongtian Chen, Yicun Hua
Summary: The polymerization process is crucial in industry, but the lack of real-time measurement of quality variables poses challenges in monitoring and control. To address this, a novel multioutput soft sensor algorithm based on canonical correlation analysis (CCA) and deep learning techniques is proposed for estimating quality variables in the industrial polymerization process. The proposed soft sensor outperforms state-of-the-art machine learning algorithms in terms of prediction accuracy and offers advantages such as complex feature extraction, handling of overfitting, and quick estimations.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Computer Science, Interdisciplinary Applications
Junyao Xie, Oguzhan Dogru, Biao Huang, Chris Godwaldt, Brett Willms
Summary: Data-driven soft sensors have been widely used in the process industry for quality variable estimation. However, building reliable soft sensors for complex industrial processes under limited data conditions is challenging. To address this issue, we propose a reinforcement learning framework that leverages samples from source domains to solve the cross-domain soft sensor problem. The proposed framework incorporates a method for sample selection and soft sensor design, taking into account correlation and estimation error metrics.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Automation & Control Systems
Wentao Bai, Fan Guo, Lei Chen, Kuangrong Hao, Biao Huang
Summary: This article proposes a robust variational Bayesian algorithm for identifying piecewise autoregressive exogenous systems with time-varying time-delays. To mitigate the effects of outliers, a $t$-distribution is used to model the noise probability distribution. A solution strategy for accurately classifying undecidable data points is proposed, using support vector machines to determine the hyperplanes for data splitting. Maximum-likelihood estimation is employed for re-estimating unknown parameters based on the classification results. The time-delay is treated as a hidden variable and identified through the variational Bayesian algorithm. The effectiveness of the algorithm is demonstrated through two simulation examples.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Huaying Li, Ronghu Chi, Zhongsheng Hou, Biao Huang
Summary: This article proposes a higher order indirect adaptive iterative learning control scheme for nonlinear nonaffine systems, which improves the control performance by using a P-type controller and iterative learning to update set points, and introduces an iterative dynamic linearization method to transform into a linear parametric learning controller.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Energy & Fuels
Stefan Jespersen, Zhenyu Yang, Dennis Severin Hansen, Mahsa Kashani, Biao Huang
Summary: To reduce the environmental impact of offshore oil and gas, stricter regulations on hydrocarbon discharge are being implemented. One approach to reducing oil discharge is by improving control systems through the introduction of new oil-in-water sensing technologies and advanced control methods. However, obtaining valid control-oriented models for de-oiling hydrocyclones has proven to be challenging, as existing models are often based on droplet trajectory analysis and do not account for the dynamics or require the measurement of droplet size distribution.
Article
Automation & Control Systems
Hongtian Chen, Biao Huang
Summary: This study develops three novel data-driven approaches for the development of fault-tolerant soft sensors in automation systems. The approaches, namely MSaS, SSaS, and IMSaS, aim to address the issue of unpredictable faults and their impact on soft sensor performance. MSaS constructs an optimal estimator of faults, SSaS removes influences from unknown sensor faults using a constructed subspace, and IMSaS is an improved version of MSaS that eliminates the effects of past prediction errors. These fault-tolerant soft sensors rely solely on system measurements and are evaluated through performance analysis and case studies.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Vamsi Krishna Puli, Biao Huang
Summary: Extraction of underlying patterns from measured variables is important for data-driven control applications. The proposed model can separate oscillating patterns and nonstationary variations. The methodology is applied to solve a fouling monitoring problem for an industrial oil production process.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Automation & Control Systems
Qingyang Dai, Chunhui Zhao, Biao Huang
Summary: Due to frequent changes in operating conditions, industrial processes often exhibit time-varying behaviors, resulting in shifting data distributions. Conventional adaptive methods struggle to distinguish normal shifts from real faults when the distribution shifts widely. This study proposes an incremental variational Bayesian Gaussian mixture model (IncVBGMM) for adaptive monitoring to accommodate the changing data distribution caused by different degrees of time-varying behaviors. The proposed method effectively differentiates various types of faults from normal shifts and adapts to the time-varying dynamics.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Na Lin, Huaying Li, Ronghu Chi, Zhongsheng Hou, Biao Huang
Summary: In this work, a data-driven virtual reference setting learning (DDVRSL) method is proposed to enhance the PD feedback controller of the repetitive nonlinear system. The method utilizes an ideal nonlinear learning law and an iterative adaptation law to estimate parameters and improve robustness against uncertainties. The proposed method does not require exact mechanistic model knowledge and its convergence is proven through mathematical analysis and simulations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Automation & Control Systems
Ronghu Chi, Xiaolin Guo, Na Lin, Biao Huang
Summary: This article addresses the challenges of data-driven control design in the presence of strong uncertainties, hard nonlinearities, and model dependency. It proposes a dynamic linearization (DL) method and an extended state observer (ESO) to handle an unknown nonlinear nonaffine system. The article presents a modified linear data model (mLDM) that accurately captures the input-output dynamics, including both linear parameter increments and unmodeled uncertainties and disturbances. The theoretical results are mathematically proven and validated through simulations.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Huimin Zhang, Ronghu Chi, Biao Huang
Summary: A novel data-driven internal model learning control strategy is proposed for a nonlinear nonaffine system. The strategy reformulates the nonlinear plant into an iterative linear data model using an iterative dynamic linearization approach, and estimates the model parameters using only input-output data. The controller design is based on the inversion of the internal model using the equivalent feedback principle, achieving perfect tracking of the target output and compensating for uncertainties.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Wenxin Sun, Weili Xiong, Hongtian Chen, Ranjith Chiplunkar, Biao Huang
Summary: This study presents a novel regression modeling approach based on the CVAE framework to address the challenges of long-term prediction biases and reliability assessment in quality variable prediction. The method achieves multistep soft measurement prediction by simulating system state trajectories, and demonstrates lower prediction biases compared to traditional methods.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
