Results 71 to 80 of about 4,609 (240)

Comparative Analysis of the Permutation and Multiscale Entropies for Quantification of the Brain Signal Variability in Naturalistic Scenarios

Brain Sciences, 2020
As alternative entropy estimators, multiscale entropy (MSE) and permutation entropy (PE) are utilized for quantification of the brain function and its signal variability. In this context, their applications are primarily focused on two specific domains: (
Soheil Keshmiri
doaj   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

Advanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang, Adrian Ehrenhofer, Thomas Wallmersperger   +2 more
wiley   +1 more source

Applying Improved Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

Applied Sciences, 2017
Electroencephalography (EEG) is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG) not only has a close correlation with the human imagination and movement intention but ...
Ming-ai Li, Hai-na Liu, Wei Zhu, Jin-fu Yang   +3 more
doaj   +1 more source

Rectifier Fault Diagnosis Based on Euclidean Norm Fusion Multi-Frequency Bands and Multi-Scale Permutation Entropy

With the emphasis on energy conversion and energy-saving technologies, the single-phase pulse width modulation (PWM) rectifier method is widely used in urban rail transit because of its advantages of bidirectional electric energy conversion and higher ...
Xiangde Mao, Jinping Liang
core   +1 more source

Permutation Entropy Based on Non-Uniform Embedding

, 2018
A novel visualization scheme for permutation entropy is presented in this paper. The proposed scheme is based on non-uniform attractor embedding of the investigated time series.
Kristina Poskuviene, Nizar Faisal Alkayem, Mei Tao, Nizar Faisal Alkayem, Tao, Mei,, Ragulskis, Minvydas,, Poskuviene, Kristina,, Cao, Maosen,, Maosen Cao, Minvydas Ragulskis, Alkayem, Nizar Faisal,   +10 more
core   +1 more source

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

, 2020
The aim of this study was to develop an integrated system of non-contact sleep stage detection and sleep disorder treatment for health monitoring. Hence, a method of brain activity detection based on microwave scattering technology instead of scalp ...
Lixia Zheng, Daoguo Yang, Daoshuang Geng, Miao Cai   +3 more
core   +1 more source

Compatibility of Methacrylate Based Resins Controls Interfacial Failure and Toughness in 3D‐Printed Multimaterial Composites

Advanced Engineering Materials, EarlyView.
This work shows that the mechanical performance of multimaterial digital light processing (DLP) printed thermoset composites is governed by resin compatibility and interfacial design rather than spatial patterning alone. Brittle and ductile resin combinations produced premature interfacial failure, while graded interfaces and mechanically compatible ...
Ahmed M. H. Ibrahim, Li Meng, Ahmad R. Najafi, Nicolas J. Alvarez   +3 more
wiley   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

Recognition of denatured biological tissue based on variational mode decomposition and multi-scale permutation entropy

Acta Physica Sinica, 2019
It is an important practical problem to accurately recognize whether biological tissue is denatured during high intensity focused ultrasound (HIFU) treatment. Ultrasonic scattering echo signals are related to some physical properties of biological tissues.
Bei Liu, Wei-Peng Hu, Xiao Zou, Ya-Jun Ding, Sheng-You Qian   +4 more
openaire   +1 more source