Predicting TFe content and sorting iron ores from hyperspectral image by variational mode decomposition-based spectral feature

Nie, Cheng; Jiang, Jinbao; Deng, Jiushuai; Li, Kangning; Jia, Lixin; Sun, Tiancheng

doi:10.1016/j.jclepro.2023.139629

Cited by 6 publications

(1 citation statement)

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“…A new wind power forecasting method called IVMD combining VMD and HFCM was developed to achieve more accurate wind power generation prediction and reduce the prediction error by extracting time series features and learning the weights using Bayesian ridge regression method(Qiao et al 2022). The spectral characteristics of VMD can be used to sort iron ore in hyperspectral images(Nie et al 2023).…”

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confidence: 99%

Driving analysis and prediction of COD based on frequency division

Li,

Chen,

Wang

et al. 2024

Preprint

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COD (Chemical Oxygen Demand) is an important indicator to measure organic pollution of water body. To strengthen in-depth analysis and prediction of COD, a new method was proposed in this paper. A frequency division method, Variational Mode Decomposition (VMD) was used to complete time domain decomposition of COD data before model simulation. The original data was separated into five signals with different frequency bands, IMF1, IMF2, IMF3, IMF4 and IMF5, with which the influence of meteorological factors and water quality factors on COD were explored. The long-term COD content is mainly driven by nutrient factors phosphorus and nitrogen, while the immediate fluctuation characteristics exhibit relatively stability. Random Forest, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were used to predict COD with the original data and the signal data processed by VMD. It is found that frequency division can improve simulation stability and accuracy of GRU and LSTM more significantly than Random Forest. VMD-GRU and VMD-LSTM models can be used reliably for COD analyzation and prediction in Chengdu area.

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confidence: 99%