Article
Engineering, Multidisciplinary
Yunsheng Jiang, Zhiqian Xu, Ping Wu, Liuhong Huang, Cui Meng
Summary: A time-domain calibration method for transient field sensors is proposed in this article, using TEM cells and monocone TEM cells as the calibration devices, divided into amplitude sensitivity and rising edge calibration. By introducing the process and presenting an example of calibrating a D-Dot sensor, the method is proven to be efficient and practical.
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
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, 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
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
Hugo Savill Russell, Louise Boge Frederickson, Szymon Kwiatkowski, Ana Paula Mendes Emygdio, Prashant Kumar, Johan Albrecht Schmidt, Ole Hertel, Matthew Stanley Johnson
Summary: The calibration of low-cost gas sensors is currently a time-consuming and challenging process. The Enhanced Ambient Sensing Environment (EASE) method combines the advantages of laboratory and field calibration, providing increased accuracy in a shorter period of time. This method has been proven to outperform traditional calibration methods in terms of both speed and performance.
Article
Green & Sustainable Science & Technology
Yingkui Jiao, Zhiwei Li, Junchao Zhu, Bin Xue, Baofeng Zhang
Summary: This paper proposes a scheme called ABIDE for parameter estimation of ultrasonic echo signals, which integrates CEEMD-SSWT and EM algorithm to identify and remove noise in the echo signal. The results on synthetic and real-world data show that ABIDE outperforms previous methods in parameter estimation and convergence time.
Article
Automation & Control Systems
Yang You, Tobias J. Oechtering
Summary: This article presents data-driven self-calibration algorithms for low-cost gas sensors. The imperfect compensation for the variation of sensor component behavior caused by changing environmental factors leads to sensor measurement errors. To overcome this, the hidden Markov model (HMM) is utilized to model the statistical dependency between environmental factors and sensor behavior variation. A time-adaptive learning framework is designed to update the HMM and track the time-varying drift process over a long term. Experimental results using real data demonstrate the effectiveness of the proposed approach in achieving long-term stable calibration performance.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(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
Automation & Control Systems
Manfred Jaeger
Summary: This paper presents a thorough analysis of the theoretical properties of the adaptive imputation and maximization (AIM) algorithm and its relationship with the expectation maximization (EM) algorithm. The study identifies conditions under which AIM and EM are equivalent and shows that AIM can produce consistent estimates in non-ignorable incomplete data scenarios where EM becomes inconsistent. The paper also develops a general theory of the AIM algorithm for discrete data settings and a general discretization approach for incomplete continuous data.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
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
Engineering, Electrical & Electronic
Xianqiang Yang, Xinpeng Liu, Chao Xu
Summary: This article introduces a soft sensor based on a robust mixture probabilistic partial least squares (RMPPLS) model, which is designed to handle the complex non-Gaussian and multimodal characteristics of data collected in modern industrial processes. By introducing two different types of hidden variables and employing the multivariate Laplace distribution for robust modeling, the proposed algorithm shows effectiveness in addressing the challenges posed by industrial data. The unknown parameters are estimated using the expectation-maximization (EM) scheme, leading to the construction of a robust soft sensor.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Engineering, Electrical & Electronic
Saikat Majumder
Summary: This article proposes a novel spectrum sensing technique for cognitive radio using eigen-values of the covariance matrix of received signal as features. The technique involves approximating decision vectors as Gaussian mixture model (GMM) and extracting distribution parameters using expectation-maximization (EM) algorithm. Simulation results show that the proposed method outperforms existing techniques for detection of primary user signal using uncalibrated antennas.
Article
Engineering, Electrical & Electronic
Shun Liu, Yan Liang, Linfeng Xu, Tiancheng Li, Xiaohui Hao
Summary: This paper introduces a novel extended object tracking problem, which accurately identifies object extension quantities and reduces uncertainty by depicting semi-axis lengths and orientation more realistically. The proposed method outperforms state-of-the-art ones in simulation results.
Article
Engineering, Biomedical
B. Biswal, Geetha P. Pavani, T. Prasanna, Prakash Kumar Karn
Summary: This paper proposes a novel approach for the automatic segmentation of exudates using an encoder-decoder style network termed as deep M-CapsNet, which reduces the memory allocation problems in semantic segmenting of objects. Experimental results demonstrate that M-CapsNet outperformed previous networks in detecting exudates.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)