Results 21 to 30 of about 226 (142)
EnergiesShort-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
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A hybrid framework: singular value decomposition and kernel ridge regression optimized using mathematical-based fine-tuning for enhancing river water level forecasting [PDF]
Scientific ReportsThe precise monitoring and timely alerting of river water levels represent critical measures aimed at safeguarding the well-being and assets of residents in river basins.
Iman Ahmadianfar +5 more
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Development of a novel modeling framework based on weighted kernel extreme learning machine and ridge regression for streamflow forecasting [PDF]
Scientific ReportsA precise streamflow forecast is crucial in hydrology for flood alerts, water quantity and quality management, and disaster preparedness. Machine learning (ML) techniques are commonly employed for hydrological prediction; however, they still face certain
Arvin Samadi-Koucheksaraee, Xuefeng Chu
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EnergiesThe Electric Vehicle (EV) industry is developing rapidly, and EVs are becoming an increasingly important choice for the future of transportation. Therefore, accurately forecasting the electricity demand for EVs is crucial.
Anjie Zhong +3 more
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A 4-DOF Exosuit Using a Hybrid EEG-Based Control Approach for Upper-Limb Rehabilitation [PDF]
IEEE Journal of Translational Engineering in Health and MedicineRehabilitation devices, such as traditional rigid exoskeletons or exosuits, have been widely used to rehabilitate upper limb function post-stroke. In this paper, we have developed an exosuit with four degrees of freedom to enable users to involve more ...
Zhichuan Tang +5 more
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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.
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Fault feature extraction method for rolling bearing based on MVMD and complex Fourier transform [PDF]
, 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,
Huang, Chuanjin, Song, Haijun
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Multivariate Nonlinear Sparse Mode Decomposition and Its Application in Gear Fault Diagnosis
IEEE Access, 2021 Multi-channel signal has more abundant and accurate state characteristic information than single channel signal. How to separate fault characteristic information from the multi-channel signal is the key of fault diagnosis.
Haiyang Pan +3 more
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Short-time variational mode decomposition
Signal ProcessingVariational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies using Fourier transformations.
Hao Jia +2 more
exaly +2 more sources
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
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