Three-dimensional quantitative mineral prediction from convolutional neural network model in developing intelligent cleaning technology

Lin, Weiwen; Qin, Shan; Zhou, Xinzhu; Guan, Xin; Zeng, Yanzhao; Wang, Zeyu; Shen, Yaohan

doi:10.1016/j.resourpol.2023.104418

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2024

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Cited by 4 publications

(1 citation statement)

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“…Aer extracting the fused features of the plasma images and spectral images in the attention, an efficient lightweight model, ShuffleNet_V2, was introduced for the nal fused feature extraction. The ShuffleNet_V2 model was designed for image classication with higher performance and lower computational complexity, [38][39][40] and it is widely used in resource-constrained scenarios, such as mobile devices, and edge computing. This choice is justied by the efficiency of ShuffleNet_V2 in the nal feature extraction for this model.…”

Section: Image Modulementioning

confidence: 99%

Air pressure prediction model based on the fusion of laser-induced plasma images and spectra

Ke,

Luo,

et al. 2024

J. Anal. At. Spectrom.

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Section: Image Modulementioning

confidence: 99%

Air pressure prediction model based on the fusion of laser-induced plasma images and spectra

Ke,

Luo,

et al. 2024

J. Anal. At. Spectrom.

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Quantitative prediction methods and applications of digital ore deposit models

Xiao,

Li,

Fan

et al. 2024

Ore Geology Reviews

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Study on the Influencing Factors of the Competitive Network Pattern of Cobalt Industry Chain Trade in the Context of Big Data Analysis

Yang,

Wang

2024

Applied Mathematics and Nonlinear Sciences

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The article proposes a time series model to explore the influencing factors of the cobalt industry chain trade competition network pattern. By analyzing the current situation of the evolution of the cobalt industry chain trade network, the relevant variables are selected. The data are described based on the overview of the relevant algorithmic research of the time series model. Finally, the model based on the time series data is empirically tested. The unit root is verified to be in an unsteady state by first-order differencing, and the p-values all have a probability of accepting the original hypothesis greater than 0. After the second-order differencing ADF test, all the series are smooth and all of them are second-order monotonic. After the cointegration test, it was found that the residual series is smooth at the 5% critical level, and there is a cointegration relationship.

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Three-dimensional quantitative mineral prediction from convolutional neural network model in developing intelligent cleaning technology

Cited by 4 publications

References 37 publications

Air pressure prediction model based on the fusion of laser-induced plasma images and spectra

Air pressure prediction model based on the fusion of laser-induced plasma images and spectra

Quantitative prediction methods and applications of digital ore deposit models

Study on the Influencing Factors of the Competitive Network Pattern of Cobalt Industry Chain Trade in the Context of Big Data Analysis

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