Wenbo Zhu¹, Neng Liu¹, Zhengjun Zhu^2,*, Haibing Li¹, Weijie Fu¹, Zhongbo Zhang¹, Xinghao Zhang¹

Intelligent Automation & Soft Computing, Vol.38, No.3, pp. 259-273, 2023, DOI:10.32604/iasc.2023.041860

Abstract The detection of ash content in coal slime flotation tailings using deep learning can be hindered by various factors such as foam, impurities, and changing lighting conditions that disrupt the collection of tailings images. To address this challenge, we present a method for ash content detection in coal slime flotation tailings. This method utilizes chromatographic filter paper sampling and a multi-scale residual network, which we refer to as MRCN. Initially, tailings are sampled using chromatographic filter paper to obtain static tailings images, effectively isolating interference factors at the flotation site. Subsequently, the MRCN, consisting of… More >

Yu Zhang^1,2,3, Mingkui Zhang^1,2,*, Jitao Li^1,2, Guangshu Chen^1,2

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1987-2006, 2023, DOI:10.32604/csse.2023.040381

Abstract Rockburst is a phenomenon in which free surfaces are formed during excavation, which subsequently causes the sudden release of energy in the construction of mines and tunnels. Light rockburst only peels off rock slices without ejection, while severe rockburst causes casualties and property loss. The frequency and degree of rockburst damage increases with the excavation depth. Moreover, rockburst is the leading engineering geological hazard in the excavation process, and thus the prediction of its intensity grade is of great significance to the development of geotechnical engineering. Therefore, the prediction of rockburst intensity grade is one… More >