Results 41 to 50 of about 257 (142)

A Session‐Based Song Recommendation Approach Involving User Characterization along the Play Power‐Law Distribution

Complexity, Volume 2020, Issue 1, 2020., 2020
In recent years, streaming music platforms have become very popular mainly due to the huge number of songs these systems make available to users. This enormous availability means that recommendation mechanisms that help users to select the music they like need to be incorporated.
Diego Sánchez-Moreno, Vivian F. López Batista, M. Dolores Muñoz Vicente, Ana B. Gil González, María N. Moreno-García, Átila Bueno   +5 more
wiley   +1 more source

Sub-Bottom Sediment Classification Using Reliable Instantaneous Frequency Calculation and Relaxation Time Estimation

Remote Sensing, 2021
The shift in IF (instantaneous frequency) series and the corresponding relaxation time have the potential to characterize sediment properties. However, these attributes derived from SBP (sub-bottom profiler) data are seldom used for offshore site ...
Shaobo Li, Jianhu Zhao, Hongmei Zhang, Siheng Qu   +3 more
doaj   +1 more source

Fault feature extraction method for rolling bearing based on MVMD and complex Fourier transform

Journal of Vibroengineering, 2022
The vibration signals caused by rolling bearing defects in different directions may be different, and the fault diagnosis based on single channel vibration signals may be made incorrectly, and the observation results may be understood wrong. To avoid it, a new rolling bearing fault feature extraction method based on multivariate variational mode ...
Chuanjin Huang, Haijun Song
openaire   +1 more source

Advanced ADHD Detection Using Multivariate Variational Mode Decomposition and Deep Learning: A Novel EEG-Based Framework [PDF]

InfoScience Trends
This study proposes a novel framework for detecting Attention Deficit Hyperactivity Disorder (ADHD) using electroencephalography (EEG) signals, integrating multivariate variational mode decomposition (MVMD) with machine learning techniques.
Parastou Shahmohamadi, Shiva Amini, Shiva Khanbabaee, Negin Tavakoli, Sedigheh Molaei, Niloofar Teimoori   +5 more
doaj   +1 more source

Multichannel Signal Denoising Using Multivariate Variational Mode Decomposition With Subspace Projection

IEEE Access, 2020
This paper describes a novel multichannel signal denoising approach based on multivariate variational mode decomposition (MVMD). MVMD is the extended version of the variational mode decomposition (VMD) algorithm for multichannel data sets.
Peipei Cao, Huali Wang, Kaijie Zhou
doaj   +1 more source

GSVMD: A High‐Performance Method for Denoising Surface‐Electromyography Signals With Generalized Successive Variational Mode Decomposition

IET Signal Processing, Volume 2025, Issue 1, 2025.
Surface electromyography (sEMG) has been used for decades to diagnose movement and neuromuscular disorders; however, sEMG signals are noisy and interfered with, and the nonstationary, nonlinear nature of sEMG signals complicates their use for diagnostic purposes.
Seyyed Ali Zendehbad, Abdollah PourMottaghi, Hamid Reza Kobravi, Elias Mazrooei Rad, Athena Sharifi-Razavi, Zahra Sedaghat, Hadi Dehbovid, Richard Dansereau   +7 more
wiley   +1 more source

Short-Term Load Forecasting for Residential Buildings Based on Multivariate Variational Mode Decomposition and Temporal Fusion Transformer

Energies
Short-term load forecasting plays a crucial role in managing the energy consumption of buildings in cities. Accurate forecasting enables residents to reduce energy waste and facilitates timely decision-making for power companies’ energy management.
Haoda Ye, Qiuyu Zhu, Xuefan Zhang
doaj   +1 more source

SALF: A Self‐Adaptive Learning Framework for Short‐Term Load Forecasting in Smart Grid

International Journal of Energy Research, Volume 2025, Issue 1, 2025.
The energy sector’s rapid expansion necessitates accurate, dependable, and computationally efficient short‐term load forecasting (STLF) models to assure real‐time balance between energy supply and demand. However, the stochastic nature of the energy usage and its reliance on changing weather conditions make accurate forecasting difficult.
Muhammad Sajid Iqbal, Muhammad Adnan, Muhammad Ali Akbar, Amine Bermak, Sharanabasava Ganachari   +4 more
wiley   +1 more source

Multiscale New Energy Price Forecasting Integrating Feature Selection and Parallel Parameter Optimization of IMVMD

International Journal of Energy Research, Volume 2025, Issue 1, 2025.
The article proposes a feature selection framework that integrates principal component analysis (PCA) and random forest (RF) to identify the key factors influencing fluctuations in China’s new energy prices. Based on this, a parallel optimization comparison mechanism is constructed by integrating the enhanced whale optimization algorithm (EWOA ...
JingYe Lyu, Chong Li, Huaiyu Wang
wiley   +1 more source

Research on Novel Bearing Fault Diagnosis Method Based on Improved Krill Herd Algorithm and Kernel Extreme Learning Machine

Complexity, Volume 2019, Issue 1, 2019., 2019
In this paper, a novel bearing intelligent fault diagnosis method based on a novel krill herd algorithm (NKH) and kernel extreme learning machine (KELM) is proposed. Firstly, multiscale dispersion entropy (MDE) is used to extract fault features of bearings to obtain a set of fault feature vectors composed of dispersion entropy.
Zhijian Wang, Likang Zheng, Junyuan Wang, Wenhua Du, Danilo Comminiello   +4 more
wiley   +1 more source