Results 91 to 100 of about 35,860 (209)
, 2011 In this paper we present an extended version of Hilbert-Huang transform, namely arbitrary-order Hilbert spectral analysis, to characterize the scale-invariant properties of a time series directly in an amplitude-frequency space. We first show numerically
Gagne, Y. +5 more
core +3 more sources
Distribution shifts in trustworthy machine learning
AI Magazine, Volume 47, Issue 1, Spring 2026.Abstract This article investigates the impact of distribution shifts in trustworthy machine learning. To this end, we start by summarizing fine‐grained types of distribution shifts commonly studied in machine learning communities. To tackle distribution shifts across domains, we present our research across various learning scenarios by enforcing ...
Jun Wu
wiley +1 more source
Advanced Intelligent Systems, Volume 8, Issue 3, March 2026.This work presents a deep learning model to autonomously recognize and classify the secretion retention into three levels for patients receiving invasive mechanical ventilation, achieving 89.08% accuracy. This model can be implemented to ventilators by edge computing, whose feasibility is approved.
Shuai Wang +6 more
wiley +1 more source
Comparative analysis of series fault arc detection methods
Gong-kuang zidonghua, 2017 For uncertainty of line fault location, current series fault arc detection methods are mainly based on current signal analysis. By comparing current waveforms before and after series arc fault under different loads, characteristics and regularities of ...
GUO Fengyi +4 more
doaj +1 more source
cScience, Volume 2, Issue 1, March 2026.ABSTRACT The use of informatics for materials design has long promised revolutionary advances through data‐driven discovery; but the untrustworthiness of the available data continues to undermine progress. Indeed, materials knowledge remains fragmented across disciplines and organizations; collaboration faces structural barriers, and the gap between ...
Shuichi Iwata
wiley +1 more source
Analyzing nonstationary financial time series via hilbert-huang transform (HHT) [PDF]
, 2008 An apparatus, computer program product and method of analyzing non-stationary time varying phenomena. A representation of a non-stationary time varying phenomenon is recursively sifted using Empirical Mode Decomposition (EMD) to extract intrinsic mode ...
Huang, Norden E.
core +1 more source
The slow-flow method of identification in nonlinear structural dynamics [PDF]
, 2007 The Hilbert-Huang transform (HHT) has been shown to be effective for characterizing a wide range of nonstationary signals in terms of elemental components through what has been called the empirical mode decomposition.
Bergman, Lawrence A. +4 more
core
Noise Corruption of Empirical Mode Decomposition and Its Effect on Instantaneous Frequency
, 2010 Huang's Empirical Mode Decomposition (EMD) is an algorithm for analyzing nonstationary data that provides a localized time-frequency representation by decomposing the data into adaptively defined modes.
Kaslovsky, Daniel N., Meyer, Francois G.
core +1 more source
THE APPLICATION OF HILBERT–HUANG TRANSFORMS TO METEOROLOGICAL DATASETS [PDF]
Journal of Atmospheric and Oceanic Technology, 2004 Recently a new spectral technique as been developed for the analysis of aperiodic and nonlinear signals - the Hilbert-Huang transform. This paper shows how these transforms can be used to discover synoptic and climatic features: For sea level data, the transforms capture the oceanic tides as well as large, aperiodic river outflows. In the case of solar
openaire +1 more source
Retracted: Analysis of Bioelectrical Impedance Spectrum for Elbow Stiffness Based on Hilbert-Huang Transform. [PDF]
Contrast Media Mol Imaging, 2023 Imaging CMM.
europepmc +1 more source