Results 101 to 110 of about 1,556 (171)
Diagnostic Accuracy of Entropy Based Image Analysis of Cervical Precancerous Cells
Journal of Biophotonics, Volume 19, Issue 6, June 2026.This work studies the correlation between the Bragg‐Williams order parameter (S2), as measured from a digital slide image, and the cytopathologic diagnoses of squamous cervical cytology and demonstrates the diagnostic accuracy of the S2 classification model. This image is an example of this approach for a “carcinoma in situ” cell image and demonstrates
Jennifer Makin +3 more
wiley +1 more source
MeasurementAlzheimer's Disease (AD) is a progressive neurodegenerative condition causing memory, attention, and language decline. Current AD diagnostic methods lack objectivity and non-invasiveness. While electroencephalography (EEG) holds promise for AD research, conventional EEG analysis methods have proven unsatisfactory.
Cataldo A. +6 more
openaire +3 more sources
Neural network with signal parameters featuring for near‐surface velocity model building
Near Surface Geophysics, Volume 24, Issue 3, Page 289-299, June 2026.Abstract This study presents a method that integrates spectral recomposition (SR) with a neural network to improve near‐surface seismic analysis. The approach incorporates SR‐derived wavelet‐timing attributes into a fully convolutional network (FCN) to enhance the characterization of shallow subsurface structures. Field evaluation was conducted using S‐
Nelson Ricardo Coelho Flores Zuniga
wiley +1 more source
Drone‐Based Inspection of Wind Turbine Blades: A Comparative Study of Deep Learning Models
Wind Energy, Volume 29, Issue 6, June 2026.ABSTRACT Maintaining wind turbine blades is a challenging task, often marked by high costs, safety risks, time inefficiency, and the possibility of incorrect diagnosis. A promising approach to support preventive maintenance involves the use of drones and deep learning for inspection and early fault detection.
Lakhdar Laib +5 more
wiley +1 more source
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.Abstract Diffusion models, a class of generative models renowned for producing realistic images, hold significant promise for reconstructing complex three‐dimensional (3D) porous media. Nevertheless, existing approaches predominantly generate stochastic microstructures visually resembling the training data but often struggle to accurately recover ...
Yinquan Meng +5 more
wiley +1 more source
Scientific ReportsThe mode mixing problem and inherent mode function selection bias in Fast Ensemble Empirical Mode Decomposition (FEEMD) result in ineffective extraction of fault components during the denoising stage, the loss of coarse-grained information in Multiscale ...
Min Mao +7 more
doaj +1 more source
Zhendong Ceshi yu Zhenduan针对齿轮故障领域识别率低和识别时间长的问题，基于多尺度加权排列熵（multiscale weighted permutation entropy，简称MWPE）、蜣螂算法（dung beetle optimizer，简称DBO）与支持向量机（support vector machine，简称SVM）的原理，提出基于MWPE和DBO结合SVM的故障识别方法。首先，由于MWPE的嵌入维数难以确定且对结果影响较大，通过MWPE熵值分析引入变异系数（coefficient of variation，简称CV ...
doaj +1 more source
Locomotive bearing fault diagnosis using hybrid entropy and combined forecasting model
机车电传动Remaining useful life (RUL) estimation for bearings plays a key role in reducing unplanned maintenance and improving locomotive reliability and safety. The accuracy of RUL estimation depends on the chosen diagnostic index and forecasting model.
ZHAO Qingguo, YANG Jiangtian
doaj
Emotion Recognition based on Multivariate Multiscale Fuzzy Entropy Analysis of EEG Recordings
Journal of KIISE, 2020 Dae-Young Lee, Young-Seok Choi
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A Diagnosis Method for Noise and Intermittent Faults in Analog Circuits Based on the Fusion of Multiscale Fuzzy Entropy Features and Amplitude Features. [PDF]
Sensors (Basel)Shi J, Hou Y, Wang Z, Yang Z, Lv Z.
europepmc +1 more source