Results 61 to 70 of about 5,089 (226)
Testing Distributional Granger Causality With Entropic Optimal Transport
Journal of Time Series Analysis, EarlyView.ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley +1 more source
Network traffic anomaly detection using EMD and Hilbert-Huan transform [PDF]
, 2013 Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) provide a means for adaptive data analysis. EMD extracts Intrinsic Mode Functions (IMFs) that represent the frequency and amplitude characteristics of a signal.
NC DOCKS at Western Carolina University +1 more
core
Time-frequency analysis of DC bias vibration of transformer core on the basis of Hilbert–Huang transform [PDF]
, 2016 This paper presents a time–frequency analysis of the vibration of transformer under direct current (DC) bias through Hilbert–Huang transform (HHT). First, the theory of DC bias for the transformer was analyzed. Next, the empirical mode decomposition (EMD)
Shi, Qingyan +7 more
core +1 more source
New Hilbert-Huang transform associated with linear canonical transform
四川大学学报. 自然科学版, 2016 Hilbert-Huang transform is an efficient non-stationary signal analysis tool. In its processing, two aspects are included: (i) empirical mode decomposition; (ii) Hilbert spectral analysis.
LIU Lu-Bo, LUO Mao-Kang, LAI Li
doaj
Signals, 2023 This study investigates the use of empirical mode decomposition (EMD) to extract intrinsic mode functions (IMFs) for the spectral analysis of EEG signals in healthy individuals and its possible biological interpretations. Unlike traditional EEG analysis,
Eduardo Arrufat-Pié +5 more
doaj +1 more source
Sparse Causal Dynamic Linear Regression
Journal of Time Series Analysis, EarlyView.ABSTRACT We develop a sparse causal dynamic regression framework for long multivariate time series. With very long time series, the potentially large number of lags and leads in a dynamic regression model often makes time‐domain estimation numerically unstable or intractable.
Rui Huang, Kung‐Sik Chan
wiley +1 more source
Classification and Identification of Underwater Target based on Sound Propagation
نشریه مهندسی دریا, 2018 This paper investigates an underwater noise target classification algorithm in order to identify vessels in shallow water. To this aim the Hilbert Huang transform has been used to extract features in order to be used in a classifier.
Hassan Sayyaadi +2 more
doaj
Energies, 2017 The analysis of electrical signals is a pressing requirement for the optimal design of power distribution. In this context, this paper illustrates how to use a variety of numerical tools, such as the Fourier, wavelet, and Hilbert-Huang transforms, to ...
Vito Puliafito +2 more
doaj +1 more source
Hilbert–Huang-Transform-Based Seismic Intensity Measures for Rocking Response Assessment
Applied Sciences, 2023 Structures that can uplift and rock under severe seismic excitations present remarkable stability without exhibiting damage. As such, rocking-response-based structural systems constitute a promising design practice.
Kosmas E. Bantilas +3 more
doaj +1 more source
Effectiveness of stacks in the stacked Hilbert-Huang transform
Physical Review DThe Hilbert-Huang transform (HHT) consists of empirical mode decomposition (EMD), which is a template-free method that represents the combination of different intrinsic modes on a time-frequency map (i.e., the Hilbert spectrum). The application of HHT involves introducing trials by imposing white noise on the signal and then calculating the ensemble ...
Lupin Chun-Che Lin +8 more
openaire +2 more sources