Results 11 to 20 of about 340 (123)

Fault Diagnosis of Gearbox Multi-channel Vibration Signal based on Improved Multivariate Multiscale Dispersion Entropy

Jixie chuandong, 2021
When the gearbox fails， its vibration signal is unstable and nonlinear. The commonly applied gearbox fault diagnosis methods are almost all based on the single-channel vibration signal analysis，which is easy to cause the loss of fault information，so the ...
Fuming Zhou, Jinxing Shen, Xiaoqiang Yang, Wuqiang Liu, Xiaolin Liu   +4 more
doaj   +1 more source

Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis

IEEE Transactions on Biomedical Engineering, 2023
Multivariate entropy quantification algorithms are becoming a prominent tool for the extraction of information from multi-channel physiological time-series. However, in the analysis of physiological signals from heterogeneous organ systems, certain channels may overshadow the patterns of others, resulting in information loss.
Evangelos Kafantaris, Tsz-Yan Milly Lo, Javier Escudero   +2 more
openaire   +3 more sources

A Multivariate Method for Dynamic System Analysis: Multivariate Detrended Fluctuation Analysis Using Generalized Variance

Topics in Cognitive Science, EarlyView., 2023
Abstract Fractal fluctuations are a core concept for inquiries into human behavior and cognition from a dynamic systems perspective. Here, we present a generalized variance method for multivariate detrended fluctuation analysis (mvDFA). The advantage of this extension is that it can be applied to multivariate time series and considers intercorrelation ...
Sebastian Wallot, Julien Patrick Irmer, Monika Tschense, Nikita Kuznetsov, Andreas Højlund, Martin Dietz   +5 more
wiley   +1 more source

Graph-Based Multivariate Multiscale Dispersion Entropy: Efficient Implementation and Applications to Real-World Network Data

CoRR
We introduce Multivariate Multiscale Graph-based Dispersion Entropy (mvDEG), a novel, computationally efficient method for analyzing multivariate time series data in graph and complex network frameworks, and demonstrate its application in real-world data.
Fabila-Carrasco, John Stewart, Tan, Chao, Escudero, Javier; id_orcid 0000-0002-2105-8725   +2 more
openaire   +4 more sources

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

An Integrated NLP‐ML Framework for Property Prediction and Design of Steels

Advanced Science, EarlyView.
This study presents a data‐driven framework that uses language‐processing techniques to interpret steel processing descriptions and machine‐learning models to predict mechanical properties. By organising complex process histories into meaningful groups and enabling rapid property forecasts, the work supports faster, more informed steel design through ...
Kiran Devraju, Adithya Umanath Rai, Jose Thomson, Marzie Ghorbani, Shujing Zhao, Nick Birbilis   +5 more
wiley   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Physics‐Constrained Constitutive Learning of Rate‐Limiting Timescales for Efficient Hydrogen‐Based Direct Reduction for Green Steel Making

Advanced Science, EarlyView.
A conversion‐resolved constitutive framework is developed for the hydrogen‐based direct reduction of iron oxide pellets. Effective reaction and transport timescales are inferred directly from measured trajectories and mapped against operating conditions, pellet architecture, and composition. The analysis reveals how late‐stage transport control emerges
Anurag Bajpai, Barak Ratzker, Pasquale Cavaliere, Dierk Raabe   +3 more
wiley   +1 more source

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

Frontiers in Physics
To achieve a comprehensive and accurate diagnosis of faults in rolling bearings, a method for diagnosing rolling bearing faults has been proposed.
Min Mao, Chengjiang Zhou, Bingwei Xu, Dongjin Liao, Jingzong Yang, Shuangyao Liu, Yiqing Li, Tong Tang   +7 more
doaj   +1 more source

Beyond d‐Band Catalysis: A Critical Review and Descriptor Framework for Rare‐Earth Engineering in Lithium–Sulfur Batteries

Advanced Science, EarlyView.
Rare‐earth catalysts regulate lithium–sulfur battery chemistry through f‐orbital–mediated interactions, enabling simultaneous polysulfide adsorption and catalytic conversion on conductive carbon hosts. This synergistic control suppresses the shuttle effect, accelerates redox kinetics, and guides stable Li2S nucleation, providing a mechanistic framework
Fan Wang, Shihzad Shakil, Guozhi Wu, Jiarui Huang, Hanhui Lei, Terence Xiaoteng Liu   +5 more
wiley   +1 more source