Results 11 to 20 of about 781,500 (170)
Refined composite multivariate generalized multiscale fuzzy entropy: A tool for complexity analysis of multichannel signals [PDF]
Physica A: Statistical Mechanics and Its Applications, 2017 Abstract Multiscale entropy (MSE) is an appealing tool to characterize the complexity of time series over multiple temporal scales. Recent developments in the field have tried to extend the MSE technique in different ways. Building on these trends, we propose the so-called refined composite multivariate multiscale fuzzy entropy (RCmvMFE) whose coarse-
Hamed Azami, Javier Escudero
exaly +5 more sources
Entropy, 2021 The fuzzy-entropy-based complexity metric approach has achieved fruitful results in bearing fault diagnosis. However, traditional hierarchical fuzzy entropy (HFE) and multiscale fuzzy entropy (MFE) only excavate bearing fault information on different ...
Xiaoan Yan, Yadong Xu, Minping Jia
doaj +3 more sources
Effects of repetitive SSVEPs on EEG complexity using multiscale inherent fuzzy entropy [PDF]
Neurocomputing, 2020 Multiscale inherent fuzzy entropy is an objective measurement of electroencephalography (EEG) complexity, reflecting the habituation of brain systems. Entropy dynamics are generally believed to reflect the ability of the brain to adapt to a visual stimulus environment.
Chin-Teng Lin +2 more
exaly +7 more sources
Entropy, 2023 Rotary machines often exhibit nonlinear behavior due to factors such as nonlinear stiffness, damping, friction, coupling effects, and defects. Consequently, their vibration signals display nonlinear characteristics.
Mostafa Rostaghi +3 more
doaj +5 more sources
EntropyEfficient extraction and classification of fault features remain critical challenges in planetary gearbox fault diagnosis. A fault diagnosis framework is proposed that integrates hierarchical refined composite multiscale fuzzy entropy (HRCMFE) for ...
Xin Xia, Xiaolu Wang
doaj +3 more sources
Entropy, 2018 Multiscale entropy (MSE), as a complexity measurement method of time series, has been widely used to extract the fault information hidden in machinery vibration signals.
Xiaolong Zhu +4 more
doaj +4 more sources
Bearing fault diagnosis method based on refined composite multiscale fuzzy entropy [PDF]
Entropy, 2022 In this paper, a new fuzzy C-mean (FCM) clustering method based on refined composite multiscale fuzzy entropy (RCMFE) is proposed and applied to the bearing fault diagnosis of rotating machinery.
Ying-Li Li +5 more
semanticscholar +4 more sources
Multiscale fuzzy entropy based on local mean decomposition and Fisher rule for EEG feature extraction in human motion analysis [PDF]
EAI Endorsed Transactions on Scalable Information Systems, 2022 Electroencephalogram (EEG) is a nonlinear, non-stationary, and random weak signal generated by a large number of neurons. It has great research value and practical significance in artificial intelligence, biomedical engineering
Huili He
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Complexity, 2021 Refined composite multivariate multiscale fractional fuzzy entropy (RCmvMFFE), which aims to sensitively discriminate different short noisy multichannel financial data, is proposed as a new measure to quantify the complexity dynamics of multichannel time
Huiqin Chu, Zhiyong Wu, Wei Zhang
doaj +2 more sources
Characterizing Network Anomaly Traffic with Euclidean Distance-Based Multiscale Fuzzy Entropy [PDF]
Security and Communication Networks, 2021 The prosperity of mobile networks and social networks brings revolutionary conveniences to our daily lives. However, due to the complexity and fragility of the network environment, network attacks are becoming more and more serious.
Renjie Zhou +4 more
semanticscholar +3 more sources