A Novel Fault Diagnosis Method for Diesel Engine Based on MVMD and Band Energy
Shock and Vibration, Volume 2020, Issue 1, 2020., 2020 Vibration signal, as an important means for diesel engine condition detection and fault diagnosis, has attracted attention for many years. In traditional vibration signal analysis, most processing methods are for single‐channel data. However, single‐channel vibration signal cannot reflect the operating information of the diesel engine comprehensively ...
Cheng Gu +4 more
wiley +1 more source
Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection [PDF]
, 2022 Accurate ahead forecasting of reference evapotranspiration (ETo) is crucial for effective irrigation scheduling and management of water resources on a regional scale.
Mehdi Jamei +11 more
core +3 more sources
Multivariate enhanced adaptive empirical Fourier decomposition and its application in rolling bearing fault diagnosis [PDF]
, 2023 Enhanced adaptive empirical Fourier decomposition EAEFD is a recently developed single channel signal separation algorithm which has attracted increasing attention for diagnosing localized rolling bearing failures Even though the EAEFD approach can ...
Cao, S +11 more
core +1 more source
A high dimensional features-based cascaded forward neural network coupled with MVMD and Boruta-GBDT for multi-step ahead forecasting of surface soil moisture [PDF]
, 2023 The objective of this study is to develop a novel multi-level pre-processing framework and apply it for multi-step (one and seven days ahead) daily forecasting of Surface soil moisture (SSM) based on the NASA’s Soil Moisture Active Passive (SMAP ...
Jamei, Mozhdeh +6 more
core +1 more source
Advanced ADHD Detection Using Multivariate Variational Mode Decomposition and Deep Learning: A Novel EEG-Based Framework [PDF]
InfoScience TrendsThis 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 +5 more
doaj +1 more source
Short-term wave power forecasting with hybrid multivariate variational mode decomposition model integrated with cascaded feedforward neural networks [PDF]
Wave power is an emerging renewable energy technology that has not reached its full potential. For wave power plants, a reliable forecast system is crucial to managing intermittency. We propose a novel robust short-term wave power (Pw) forecasting method,
Deo, Ravinesh C. +8 more
core +1 more source
A Novel Enhancement Approach Following MVMD and NMF Separation of Complex Snoring Signals [PDF]
, 2023 Snoring is a prominent characteristic of sleep-disordered breathing, and its detection is critical for determining the severity of the upper airway obstruction and improving daily quality of life. Home snoring analysis is a highly invasive method, but it
Mariam Al Mawla +5 more
core +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 +4 more
wiley +1 more source
A new perspective on air quality index time series forecasting: A ternary interval decomposition ensemble learning paradigm [PDF]
, 2023 Accurate forecasting of the air quality index (AQI) plays a crucial role in taking precautions against upcoming air pollution risks. However, air quality may fluctuate greatly in a certain period.
Zicheng Wang +7 more
core +1 more source
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