Dominant Partitioning of Discontinuities of Rock Masses Based on DBSCAN Algorithm

Ruan, Yunkai; Liu, Weicheng; Wang, T.; Chen, J.; Zhou, Xin; Sun, Yunqiang

doi:10.3390/app13158917

Cited by 2 publications

(1 citation statement)

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“…Clustering is the most well-known and frequently utilized unsupervised learning technique in data mining, pattern recognition, image segmentation, and so on [24][25][26][27]. The cluster algorithm is a set of methodologies for automatic classification of samples into a number of groups using a measure of similarity, so that the samples in one group are similar to each other and samples belonging to different groups are not similar to each other.…”

Section: Hierarchical Clustering Algorithmmentioning

confidence: 99%

Longwall Face Automation: Coal Seam Floor Cutting Path Planning Based on Multiple Hierarchical Clustering

Guan,

Wang,

Wang

et al. 2023

Applied Sciences

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Space adaptability between mining equipment and coal-rock mass, to ensure the machines cut in a coal seam, is an importance technique in longwall mining automation. In order to guide the mining equipment cutting in the coal seam, a cutting path planning method based on multiple hierarchical clustering was proposed. Morphology similarity and the coplanarity measurement method were defined to evaluate the similarity of clusters. The coal seam floor series in the face-advancing direction were clustered according to the morphology similarity and coplanarity, respectively. Taking the morphology-based and coplanarity-based cluster centers as generating lines and stretching angle, respectively, the coal seam floor was reconstructed. The reconstructed floor can be regarded as the cutting path. The coal seam geological model of the 18,201 longwall face was analyzed with the proposed cutting path planning method. Comparing the reconstructed floor and original floor, the amounts of coal left and cut gangue were 1999 m3 and 1856 m3, respectively, for the segmental floor. For the case of whole floor, the amounts of coal left and cut gangue were 5642 m3 and 5463 m3, respectively. The coal loss rates only were 0.57% and 0.87% for the segmental and whole coal seam, respectively.

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Section: Hierarchical Clustering Algorithmmentioning

confidence: 99%