Abstract To address the problem that it is difficult to accurately predict health status of hydropower units using real-time monitoring data, this paper proposes a method for predicting trend based on variational modal decomposition (VMD) and time convolution network (TCN); considering nonlinear factors series unit vibration index, prediction model VMD-TCN established achieve accurate status. A...

Abstract Water is an indispensable prerequisite and essential factor for human life social development. Change in water quality closely related to pollution. prediction the basic work control environmental Therefore, establishment of effective models great value economy, protection health. This paper aims at problem that error relative maximum minimum values Long Short-Term Memory (LSTM) dealin...

Abstract Considering the nonlinear and non-stationary characteristics of transformer vibration acceleration signal obtained from surface tank, variational mode decomposition (VMD) theory is introduced. Simulation analysis shows that VMD has obvious advantages over EMD when needle frequency similar to signal. It effectively avoids two types modal aliasing over-decomposition, accurately reflects ...

Atmospheric lidar is susceptible to the influence of light attenuation, sky background light, and detector dark currents during detection process. This results in a large amount noise return signal. To reduce extract useful signal, novel denoising method combined with variational modal decomposition (VMD), sparrow search algorithm (SSA) singular value (SVD) proposed. The SSA used optimize numbe...

In order to solve the problem of low localization accuracy water supply pipeline leakage detection using acoustic emission signals, a noise reduction algorithm based on variational mode decomposition (VMD) and Hilbert transform is proposed in this paper. Firstly, signal decomposed into several intrinsic functions (IMF) VMD, number layers determined, IMF component transformed by get marginal spe...

Short-term power load forecasting is critical for ensuring system stability. A new algorithm that combines CNN, GRU, and an attention mechanism with the Sparrow to optimize variational mode decomposition (PSVMD–CGA) proposed address problem of effect random fluctuations on accuracy short-term forecasting. To avoid manual selection VMD parameters, adopted by decomposing data into multiple subseq...

An integrated membrane distillation (MD) flowsheet, consisting of direct contact (DCMD) and vacuum (VMD) units, was proposed analysed in terms thermal performance water recovery factor, for the first time. The same lab-scale module (40 cm2) used carrying out experiments DCMD VMD at fixed feed operating conditions (deionised 230 L/h ~40 °C) while working permeate side with deionised 18 °C a 20 m...

Physiological signal measurement and processing are increasingly becoming popular in the ambulatory setting as hospital-centric treatment is moving towards wearable ubiquitous monitoring. Most of physiological signals highly susceptible to various types noises, especially movement artifacts. The electroencephalogram (EEG) functional near-infrared spectroscopy (fNIRS) no exception motion artifac...

Electricity load forecasting is one of the hot concerns current electricity market, and many models are proposed to satisfy market participants’ needs. Most have shortcomings large computation or low precision. To address this problem, a novel deep learning data processing ensemble model called SELNet proposed. We performed an experiment with model; consisted two parts: forecasting. In part, au...

Using vector meson dominance (VMD), tensor glueball photoproduction cross sections, asymmetries and widths are calculated. The predicted hadronic V V ′ decays are comparable for different vector meson (V = ρ, ω and φ) channels with the ωφ width the largest but the radiative ωγ and φγ decays are suppressed relative to ργ by over a factor of 2. This decay profile is distinct from typical meson br...