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11th Frontier Academic Forum of Electrical Engineering, FAFEE 2024

作者： Qi, Yongzhen Jiang, Pin Zhu, Hefei Duan, Saisai College of Electricity and Information Engineering Lanzhou University of Technology Gansu Lanzhou730050 China Ultra High Voltage Company of State Grid Gansu Electric Power Company Lanzhou730000 China

ISBN: (纸本)9789819787791

The quality of wind power data affects wind power prediction and WTG output modeling. Due to mechanical failures, sensor errors and other reasons, the data collected contain abundant outliers, and it is particularly important to identify the outliers. In this paper, we propose an adaptive identification method, which includes the physical characteristics method to deal with outliers stacked up at the bottom along the direction of wind speed, the EM clustering (expectation-maximization algorithm, EM) algorithm based on the profile coefficients to identify discrete outliers, and the Doseresp function based on the identification of outliers to identify discrete outliers. On the basis of identification, the Doseresp function fitting curve is used as a benchmark to reject the outliers, and finally the effectiveness of the proposed method is verified by taking a cluster wind farm in Gansu as an example. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： expectation maximization algorithm

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Comparing the Performance of Recurrent Neural Network and Some Well-Known Statistical Methods in the Case of Missing Multivariate Time Series Data 9th

Comparing the Performance of Recurrent Neural Network and...

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9th International Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2024

作者： Zahmatkesh, Samira Zech, Philipp Department of Computer Science University of Innsbruck Innsbruck Austria

ISBN: (纸本)9783031770654

Missing values in multivariate time series data, often caused by network disruptions, device or power outages, and bad weather, can pose challenges for future analysis. Common statistical methods like multiple imputation and expectation-maximization are often used to impute missing time series data. However, these methods assume that the data is missing at random and may struggle with more complex missing data mechanisms and higher missing ratios. In these cases, advanced techniques like neural networks may offer improved imputation. This study assess the effectiveness of two recurrent neural network methods LSTM and GRU, enhanced with a time decay function, named LSTM-D and GRU-D, for analyzing missing multivariate time series. Their performance is compared with three well-known statistical methods: Bootstrapped-EM, EM-ARIMA, and MICE, across different missing data scenarios. Results indicate that LSTM-D and GRU-D perform better than traditional statistical methods for two different datasets, particularly when the missing data is not random. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： expectation maximization algorithm

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Online Disjoint Set Covers: Randomization Is Not Necessary 42

Online Disjoint Set Covers: Randomization Is Not Necessary

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42nd International Symposium on Theoretical Aspects of Computer Science, STACS 2025

作者： Bienkowski, Marcin Byrka, Jaroslaw Jeż, Lukasz University of Wroclaw Poland

ISBN: (纸本)9783959773652

In the online disjoint set covers problem, the edges of a hypergraph are revealed online, and the goal is to partition them into a maximum number of disjoint set covers. That is, n nodes of a hypergraph are given at the beginning, and then a sequence of hyperedges (subsets of [n]) is presented to an algorithm. For each hyperedge, an online algorithm must assign a color (an integer). Once an input terminates, the gain of the algorithm is the number of colors that correspond to valid set covers (i.e., the union of hyperedges that have that color contains all n nodes). We present a deterministic online algorithm that is O(log2 n)-competitive, exponentially improving on the previous bound of O(n) and matching the performance of the best randomized algorithm by Emek et al. [ESA 2019]. For color selection, our algorithm uses a novel potential function, which can be seen as an online counterpart of the derandomization method of conditional probabilities and pessimistic estimators. There are only a few cases where derandomization has been successfully used in the field of online algorithms. In contrast to previous approaches, our result extends to the following new challenges: (i) the potential function derandomizes not only the Chernoff bound, but also the coupon collector’s problem, (ii) the value of Opt of the maximization problem is not bounded a priori, and (iii) we do not produce a fractional solution first, but work directly on the input. © Marcin Bienkowski, Jaroslaw Byrka, and Lukasz Jeż

关键词： expectation maximization algorithm

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Microgrid Control Latency Prediction with PMU Data from Hardware-in-the-Loop Experiments

Microgrid Control Latency Prediction with PMU Data from Hard...

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2025 IEEE PES Grid Edge Technologies Conference and Exposition, Grid Edge 2025

作者： Ma, Zhihao Cui, Qiushi Mai, Lihao Weng, Yang Arizona State University Department of Electrical Computer and Energy Engineering Tempe United States

ISBN: (纸本)9798350352528

The commission of a real microgrid may encounter unexpected time latency if no hardware experiment is conducted. Meanwhile, it is difficult to estimate the control signal latency of a microgrid's energy management system (EMS) without an efficient prediction algorithm. Thus, this paper proposes a time-latency prediction method utilizing the grid edge technology of PMU and hardware-in-the-Ioop (HIL) experiments. The latency problem has the following challenges based on unique properties of data: (1) Spatially, PMU and SCADA sensors have different locations and latency. Thus, it's required to approximate the data correlations to estimate the missing data accurately. (2) Temporally, the dynamic transitions in SCADA samples are scarce, urging efficient utilization of the SCADA data to approximate the dynamics. For challenge (1), we employ Deep Neural Networks (DNNs) with high capacities to capture spatial-temporal information to predict dynamic states. For challenge (2), we develop a new mechanism to utilize SCADA data efficiently and eliminate the latency by estimating the latency distribution. The results show that our EM (expectation-maximization) algorithm in time synchronization offers superior performance over other methods in detecting and fixing time latency. This advanced algorithm effectively identifies discrepancies in time data by iteratively estimating a statistical model's parameters to ensure synchronization's precision and reliability. The proposed method shows its potential in tackling time latency and data scarcity under 2 different systems in 4 different scenarios in our HIL real-world setup. © 2025 IEEE.

关键词： expectation maximization algorithm

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GAEM: Genetic algorithm based expectation-maximization for inferring Gene Regulatory Networks from incomplete data

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Computers in Biology and Medicine 2024年 183卷 109238-109238页

作者： Niloofar, Parisa Aghdam, Rosa Eslahchi, Changiz Mærsk Mc-Kinney Møller Institute University of Southern Denmark Campusvej 55 Odense5230 Denmark Wisconsin Institute for Discovery University of Wisconsin-Madison WI Madison United States Department of Computer and Data Sciences Faculty of Mathematical Sciences Shahid Beheshti University Iran Iran

In Bioinformatics, inferring the structure of a Gene Regulatory Network (GRN) from incomplete gene expression data is a difficult task. One popular method for inferring the structure GRNs is to apply the Path Consistency algorithm based on Conditional Mutual Information (PCA-CMI). Although PCA-CMI excels at extracting GRN skeletons, it struggles with missing values in datasets. As a result, applying PCA-CMI to infer GRNs, necessitates a preprocessing method for data imputation. In this paper, we present the GAEM algorithm, which uses an iterative approach based on a combination of Genetic algorithm and expectation-maximization to infer the structure of GRN from incomplete gene expression datasets. GAEM learns the GRN structure from the incomplete dataset via an algorithm that iteratively updates the imputed values based on the learnt GRN until the convergence criteria are met. We evaluate the performance of this algorithm under various missingness mechanisms (ignorable and nonignorable) and percentages (5%, 15%, and 40%). The traditional approach to handling missing values in gene expression datasets involves estimating them first and then constructing the GRN. However, our methodology differs in that both missing values and the GRN are updated iteratively until convergence. Results from the DREAM3 dataset demonstrate that the GAEM algorithm appears to be a more reliable method overall, especially for smaller network sizes, GAEM outperforms methods where the incomplete dataset is imputed first, followed by learning the GRN structure from the imputed data. We have implemented the GAEM algorithm within the GAEM R package, which is accessible at the following GitHub repository: https://***/parniSDU/GAEM. © 2024

关键词： expectation maximization algorithm

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A Physical Cluster-based Passive Scatterer Detection Method in ISAC Systems 16

A Physical Cluster-based Passive Scatterer Detection Method ...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Wang, Yu Zhou, Tao Liu, Xiangping Liu, Liu School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9781510689251

This paper proposes a novel passive scatterer detection method in integrated sensing and communication (ISAC) systems. The proposed scatterer detection method employs Subspace Alternating Generalized expectation maximization (SAGE) to estimate the parameters of the multipath components (MPCs), which are used to determine the locations of scattering points (SPs). Subsequently, a joint clustering and tracking algorithm is applied to identify the clusters which the SPs belong to. As a result, valuable information of scatterer is obtained, including location, quantity and shape. The proposed method leverages the relationship between physical clusters and scatterers in the environment, which is validated according to channel simulation based on ray-tracer. Compared to other scatterer detection methods, the proposed approach can detect scatterers with high accuracy, low false alarm rate and miss alarm rate, and low mean center distance error with real-time detection capability. © 2025 SPIE.

关键词： expectation maximization algorithm

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EM algorithm for Cluster of Multicore Nodes Using Level 3 BLAS Operations to Learn Gaussian Mixture Models 15th

EM Algorithm for Cluster of Multicore Nodes Using Level 3 ...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Kwedlo, Wojciech Faculty of Computer Science Bialystok University of Technology Wiejska 45A Bialystok15-351 Poland

ISBN: (纸本)9783031857027

In this paper, we explore the problem of learning the parameters of Gaussian mixture models using the expectation-maximization (EM) algorithm. We propose a new parallel formulation of the EM algorithm that utilizes a static decomposition and distributes the learning set and the matrix storing posterior probabilities among MPI processes. During both the E-step and M-step of an EM iteration, each MPI process spawns a team of OpenMP threads that process data in blocks sized to fit the last-level cache. The calculations necessary for obtaining weighted Gaussian densities in the E-step and mixture parameters in the M-step are conducted using optimized level 3 BLAS operations. Hierarchical all-reduce operations are employed to compute the sums required in the M-step, initially among the OpenMP threads within each MPI process and subsequently among all the MPI processes in the parallel application. In our computational experiments, we compared this proposed approach with the traditional method that employs level 2 BLAS operations and assessed its strong scaling on 64 nodes of a compute cluster. The results demonstrate that the proposed method is 1.6 to 4.7 times faster than the conventional approach utilizing level 2 BLAS. The parallel efficiency of our approach on 64 nodes ranges from 60% to 73%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： expectation maximization algorithm

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A robust transfer learning approach for high-dimensional linear regression to support integration of multi-source gene expression data

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PLOS COMPUTATIONAL BIOLOGY 2025年第1期21卷 e1012739页

作者： Pan, Lulu Gao, Qian Wei, Kecheng Yu, Yongfu Qin, Guoyou Wang, Tong Fudan Univ Sch Publ Hlth Dept Biostat Shanghai Peoples R China Shanxi Med Univ Sch Publ Hlth Dept Hlth Stat Taiyuan Peoples R China Shanxi Med Univ Minist Educ Key Lab Coal Environm Pathogen & Prevent Jinzhong Peoples R China Shanghai Inst Infect Dis & Biosecur Shanghai Peoples R China Fudan Univ Natl Commiss Hlth Minist Educ Key Lab Hlth Technol AssessmentKey Lab Publ Hlth Shanghai Peoples R China

Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches. In this paper, we study the transfer learning problem under high-dimensional linear models with t-distributed error (Trans-PtLR), which aims to improve the estimation and prediction of target data by borrowing information from useful source data and offering robustness to accommodate complex data with heavy tails and outliers. In the oracle case with known transferable source datasets, a transfer learning algorithm based on penalized maximum likelihood and expectation-maximization algorithm is established. To avoid including non-informative sources, we propose to select the transferable sources based on cross-validation. Extensive simulation experiments as well as an application demonstrate that Trans-PtLR demonstrates robustness and better performance of estimation and prediction when heavy-tail and outliers exist compared to transfer learning for linear regression model with normal error *** integration, Variable selection, T distribution, expectation maximization algorithm, Genotype-Tissue Expression, Cross validation.

关键词： expectation maximization algorithm

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Remaining useful life prediction method of multi-stage degradation system considering random correlation among multiple components

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Zhendong yu Chongji/Journal of Vibration and Shock 2025年第10期44卷 311-322页

作者： Zhu, Yanjun Li, Ke Wu, Bin Shi, Hui Taiyuan University of Science and Technology College of Electronic Information Engineering Taiyuan030024 China

Degradation between components in a multi-component Systems may have different degrees of mutual influence, which makes the multi-component System often have multi-stage degradation characteristics. In view of the above problem, the influence of the interaction between the components of the multi-component System on the degradation mode was considered, and proposes a multi-stage System degradation modeling and remaining useful life prediction method was proposed based on Wiener process with continuous degradation bidirectional random correlation effect. Firstly, a multi-stage Wiener process degradation model considering the influence of bidirectional random correlation was established by using the mutation point detection to describe the influence of random interaction between components on the degradation process of the multi-component System. Secondly, to reflect the degradation heterogeneity of each component, and consider that the degradation rate of the component is composed of two parts: its own inherent degradation rate and the degradation rate generated by its related components, the drift coefficient and diffusion coefficient of each stage of the System were defined as random parameters, and the expectation maximization algorithm was used to estimate the unknown Parameters. Finally, the Bayesian algorithm was used to update the posterior parameter distribution, predict the location of the mutation point, and derive the expression of the remaining life of the multi-stage degradation System considering the random correlation of degradation among the components according to the first passage time. The effectiveness of the method was verified by the numerical Simulation and commercial modular aero-propulsion System Simulation dataset. © 2025 Chinese Vibration Engineering Society. All rights reserved.

关键词： expectation maximization algorithm

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Modularity-Driven Influence: An Enhanced LPA Framework for Community Detection and maximization in Social Networks

Modularity-Driven Influence: An Enhanced LPA Framework for C...

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2025 International Conference on Computer, Electrical and Communication Engineering, ICCECE 2025

作者： Manoj Jayanth, N. Nithin, M. Kushwanth, M.V. Reddy, K. Akash Shukla, Ankita Nagarjuna College of Engineering and Technology Department of Information Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798331513894

It is quite an uphill task to identify social network communities as well as to diffuse impact because of their complex architecture as well as evolving nature of their relationships. In this paper, we present an advanced version of the Modularity-Driven Label Propagation algorithm, Enhanced Label Propagation algorithm (ELPA) which has provided solutions to these issues through the adoption of modularity optimization and the local influence effectiveness measure. The modularity component ensures accurate and coherent community discovery, while the Local Influence Effectiveness (LIE) metric optimizes effective node selection and network impact propagation. The productivity and the impact distribution of the method have surpassed the results of the Louvain method which is often used on large databases of networks. The findings suggest that this new LPA is of a linearly increasing runtime with better impact diffusion making it appropriate for the use in viral marketing, public health activities and the dissemination of specific information. By integrating computational scale with analytical precision, the methodology advanced in this study offers a robust and efficient strategy for the current evolution of social networks research. © 2025 IEEE.

关键词： expectation maximization algorithm

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