Results 21 to 30 of about 573 (180)

Towards intelligent air quality forecasting using integrated machine learning framework with variational mode decomposition and catboost feature selection [PDF]

Scientific Reports
Predicting air pollution is crucial in improving air quality (AQ), which consequently provides benefits to the ecosystems and human health. AQ predictions often make use of Machine Learning (ML) approaches; nevertheless, these methods are not without ...
Iman Ahmadianfar, Zaher Mundher Yaseen, Haydar Abdulameer Marhoon, Bijay Halder, Mou Leong Tan, Huseyin Cagan Kilinc, Sani I. Abba, Salim Heddam, Leonardo Goliatt, Vahdettin Demir, Ahmed M. Al-Areeq   +10 more
doaj   +2 more sources

Forecasting of interval carbon price in China based on decomposition-reconstruction-ensemble framework [PDF]

Carbon Balance and Management
Accurate prediction of carbon prices is imperative for the effective management of carbon markets and the facilitation of a global transition to green energy.
Beibei Hu, Yunhe Cheng
doaj   +2 more sources

Exploring functional connectivity at different timescales with multivariate mode decomposition. [PDF]

Front Neurosci
This paper explores an alternative way for analyzing static Functional Connectivity (FC) in functional Magnetic Resonance Imaging (fMRI) data across multiple timescales using a class of adaptive frequency-based methods referred to as Multivariate Mode ...
Morante M, Frølich K, Rehman NU.
europepmc   +3 more sources

Motor rolling bearing fault diagnosis based on MVMD energy entropy and GWO-SVM

Journal of Vibroengineering, 2023
For motor rolling bearing fault diagnosis, vibration signal analysis is a common method to extract sensitive fault characteristics. In this paper, a newly signal processing method, multivariate variational mode decomposition (MVMD), is proposed to extract features from motor rolling bearings. The MVMD was carried out on the motor rolling bearings state
Jian Tang, Qiaoni Zhao
exaly   +2 more sources

Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition [PDF]

Sensors, 2018
Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis.
Zhijian Wang, Junyuan Wang, Wenhua Du
doaj   +2 more sources

Seismic Random Noise Denoising Using Mini-Batch Multivariate Variational Mode Decomposition. [PDF]

Comput Intell Neurosci, 2022
Seismic noise attenuation plays an important role in seismic interpretation. The empirical mode decomposition, synchrosqueezing wavelet transform, variational mode decomposition, etc., are often applied trace by trace. Multivariate empirical mode decomposition, multivariate synchrosqueezing wavelet transform, and multivariate variational mode ...
Wu G, Liu G, Wang J, Fan P.
europepmc   +2 more sources

Early fault diagnosis of transformer windings based on the improved MVMD-ELM

Frontiers in Energy Research
Aiming at the problems of weak early fault characteristics of transformer windings, large noise interference and insufficient accuracy of traditional diagnostic methods, this paper proposes an early fault diagnosis method for transformer windings based ...
Qiuyang Lin, Congwei Wang, Luyi Zhang, Fengping Zhang, Zichi Zhao, Minyi Wei, Jiaqi Liu, Cheng Li   +7 more
doaj   +2 more sources

Continuous Small Leakage Identification Method of Urban Pipeline Based on Improved MVMD Fusion Machine Learning

Journal of Nondestructive Evaluation
Abstract To address the challenge that continuous small leakage signals are easily disrupted by noise, resulting in a low recognition rate for urban pipeline leakage, we propose an improved multivariate variational mode decomposition (IMVMD) fusion machine learning method specifically for the recognition of continuous small leakages in urban ...
Anning Wang, Yongmei Hao
exaly   +2 more sources

A Bi-Level Capacity Optimization Method for Hybrid Energy Storage Systems Combining the IBWO and MVMD Algorithms

Energies
With the swift evolution of renewable energy technologies, the design and optimization of microgrids have emerged as vital components for fostering energy transition and promoting sustainable development. This study presents a bi-level capacity optimization model for microgrids, integrating wind–solar generation with hybrid electric–hydrogen energy ...
Shidong Li, Yang Long, Yunxiang Li
exaly   +3 more sources

Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method

IEEE Access, 2022
In process control system, nonlinearity-induced unit-wide oscillations are a common fault, which degrades the control performance and threaten the stability.
Zhuliang Lin, Min Sun, Xialai Wu
doaj   +1 more source