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Multimodality medical image fusion using directional total variation based linear spectral clustering in NSCT domain. [PDF]
Sci RepKhan MZ +6 more
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AdjLeafGNN: a hybrid deep learning and graph neural network framework for probabilistic modeling of adjacent leaf disease spread in precision agriculture. [PDF]
Sci RepSurekha B, Subha Mastan Rao T.
europepmc +1 more source
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
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Chaos, Solitons and FractalsMultivariate multiscale sample entropy (mvMSE) is a popular method to quantify the irregularity of multi-channel time series. However, mvMSE generates either undefined or unreliable results for short-length signals and has a high computational cost. To address these issues, multivariate multiscale dispersion entropy (mvMDE) was recently developed, but ...
Hamed Azami, Anne Humeau-Heurtier
exaly +3 more sources
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Rolling Bearing Fault Diagnosis Based on CEEMDAN and Refined Composite Multiscale Fuzzy Entropy
IEEE Transactions on Instrumentation and Measurement, 2021Considering the nonlinear and nonstationary characteristics of rolling bearing vibration signals, we propose a rolling bearing fault diagnosis method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), refined composite multiscale fuzzy entropy (RCMFE), Laplace score (LS), and the particle swarm optimization ...
Shuzhi Gao, Yimin Zhang, Quan Wang
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ISA Transactions, 2022
Extracting the failure related information from vibration signals is a very important aspect of vibration-based fault detection for rolling bearing Multiscale entropy and its improvement, multiscale fuzzy entropy (MFE), are significant complexity measure tools of time series. They have been successfully applied to extract vibration failure features for
Jinde Zheng, Qingyun Liu, Jinyu Tong
exaly +3 more sources
Extracting the failure related information from vibration signals is a very important aspect of vibration-based fault detection for rolling bearing Multiscale entropy and its improvement, multiscale fuzzy entropy (MFE), are significant complexity measure tools of time series. They have been successfully applied to extract vibration failure features for
Jinde Zheng, Qingyun Liu, Jinyu Tong
exaly +3 more sources
Journal of Central South University, 2019
Combining refined composite multiscale fuzzy entropy (RCMFE) and support vector machine (SVM) with particle swarm optimization (PSO) for diagnosing roller bearing faults is proposed in this paper. Compared with refined composite multiscale sample entropy (RCMSE) and multiscale fuzzy entropy (MFE), the smoothness of RCMFE is superior to that of those ...
Fan Xu, Peter W Tse
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Combining refined composite multiscale fuzzy entropy (RCMFE) and support vector machine (SVM) with particle swarm optimization (PSO) for diagnosing roller bearing faults is proposed in this paper. Compared with refined composite multiscale sample entropy (RCMSE) and multiscale fuzzy entropy (MFE), the smoothness of RCMFE is superior to that of those ...
Fan Xu, Peter W Tse
exaly +4 more sources