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Expectation-maximizing network reconstruction and most applicable network types based on binary time series data

PHYSICA D-NONLINEAR PHENOMENA 2023年第1期454卷

作者： Liu, Kaiwei Lu, Xing Gao, Fei Zhang, Jiang Beijing Jiaotong Univ Dept Math Beijing 100044 Peoples R China Beijing Jiaotong Univ Beijing Lab Natl Econ Secur Early warning Engn Beijing 100044 Peoples R China Beijing Normal Univ Sch Syst Sci Beijing 100875 Peoples R China Swarma Res Beijing 102399 Peoples R China

Based on the binary time series data of social infection dynamics, we propose a general framework to reconstruct the 2-simplicial complexes with two-body and three-body interactions by combining the maximum likelihood estimation in statistical inference and introducing the expectation maximization. In order to improve the code running efficiency, the whole algorithm adopts vectorization expressions. Through the inference of maximum likelihood estimation, the vectorization expression of the edge existence probability can be obtained, and through the probability matrix, the adjacency matrix of the network can be estimated. The framework has been tested on different types of complex networks. Among them, four kinds of networks achieve high reconstruction effectiveness. Finally, we analyze which type of network is more suitable for this framework, and propose methods to improve the effectiveness of the experimental results. Complex networks are presented in the form of simplicial complexes. In this paper, focusing on the differences in the effectiveness of simplicial complexes reconstruction after the same number of iterations, we innovatively propose that simplex reconstruction based on maximum likelihood estimation is more suitable for small-world networks and three indicators to judge the structural similarity between a network and a small-world network are given. The closer the network structure to the small-world network is, the higher efficiency in a shorter time can be obtained.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Expectation maximization binary time series data Vectorization expression Simplex complexes Small-world network

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Anomaly Detection in binary time series data: An unsupervised Machine Learning Approach for Condition Monitoring 5

Anomaly Detection in Binary Time Series Data: An unsupervise...

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5th International Conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Princz, Gabor Shaloo, Masoud Erol, Selim Univ Appl Sci Wiener Neustadt Inst Ind Engn & Management Johannes Gutenberg Str 3 A-2700 Wr Neustadt Austria

A key element of smart manufacturing is condition monitoring and heath controlling of production machines. In today's rapidly evolving landscape of industrial machinery and equipment, optimizing the operation of production lines is critical to ensure high productivity and product quality. timely detection and prevention of faults in the production process plays a crucial role in minimizing downtime, reducing costs, and ensuring optimal performance. The scientific challenge here is that the increasing number of sensors and actuators with digital input and output signals in the production machines creates different patterns, which are difficult to evaluate using conventional statistical methods. Another difficulty is identifying the cause of the failure to be able to intervene rapidly and in a focused manner in the event of irregularities. For this reason, this research study presented a comprehensive analysis of anomaly detection in binary time series data using various machine learning models. The study included preprocessing of the dataset, normalizing the data, and evaluation of the anomaly detection performance of the different models. The accuracy, detection rate, and F1-score are used as evaluation measures. The execution time of each model is also analyzed. In addition, the identification of sensors that cause anomalies is investigated and the impact of false detections is discussed. Experimental results show the strengths and weaknesses of each model and provide valuable insights for selecting the appropriate anomaly detection approach. The Isolation Forest, Local Outlier Factor, DBSCAN, and kMeans models show high precision and detection, while the Autoencoder and Variational Autoencoder models show high precision but lower detection. The one-class Support Vector Machine model achieves balanced performance. AutoML shows excellent results in recognition rate but is not real-time capable. The results highlight the trade-offs between performance and computation

关键词： Smart Manufacturing data-driven Maintenance Anomaly Detection Unsupervised Machine Learning binary time series data

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Anomaly Detection in binary time series data: An unsupervised Machine Learning Approach for Condition Monitoring

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Procedia Computer Science 2024年 232卷 1065-1078页

作者： Gábor Princz Masoud Shaloo Selim Erol

关键词： Smart Manufacturing data-driven Maintenance Anomaly Detection Unsupervised Machine Learning binary time series data

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