差別化されたプラットフォームが両面の顧客に提出する価格に関する研究

首都大学東

Experimental study on permeability evolution of slender coal pillar of entry driven along goaf

Under the condition of roadway driving along goaf, slender coal pillar is affected by multiple mining-induced disturbances, and the permeability of coal and rock mass affected by mining will change due to the development and compaction of mining fractures and primary fractures. Determining the evolution of slender coal pillar permeability at different mining stages is the theoretical basis for the prevention and control of gas water disasters in adjacent goaf at the same layer. Taking the mining with slender coal gate pillar of the Carboniferous extra thick coal seam in Datong Mining Area as the engineering background, the distribution characteristics of the stress field for the slender coal gate pillar of the coal seam in different mining stages are comprehensively determined by the methods of geostress testing and numerical simulation, which provides a basis for the determination of the stress path for experimental research. The DJG - Ⅱ triaxial loading coal rock seepage testing equipment was used to conduct experimental research on the evolution of coal pillar permeability in different mining stages. The research results are as follows: The quantitative influence relationship between permeability and stress of slender coal gate pillar in different mining stages is established. The overall performance is that the permeability decreases with the increase of axial stress, and the permeability increases with the decrease of axial pressure in unloading stage; It reveals the evolution of stress strain permeability of the coal pillar in different mining stages. When loading and unloading in the first and second stages, the deformation of coal sample is still in the elastic deformation stage, and the change amplitude and rate of permeability are relatively gentle. In the third mining-impacted stage, the irreversible plastic failure of the specimen made the permeability increase sharply, and the rate of increase was also significantly greater than the first two mining stages. The permeability of slender coal pillar increased by 324.389 times compared with the initial permeability. In this stage, the slender coal pillar was damaged and lost its gas water barrier performance. It was clear that the 6 m small coal pillar was not damaged in the first two mining stages of the super thick coal seam gob side entry project. The research results can provide reference or theoretical support for the study of permeability evolution characteristics of slender coal pillar in different mining stages, and the prevention and control of gas water disasters in adjacent goaf under the condition of gob side entry mining in hard roof extra thick coal seams

Quantitative Prediction Method for Distribution Power Grid Risk

The electric power distribution grid is directly oriented to the majority of the ordinary users. Traditional operation and maintenance are performed mainly based on experience, which disable to rationally evaluate the status of the line and predict faults. Based on big data, the risk of the line is evaluated through principal component analysis in this paper, so that a machine learning algorithm is carried out to calculate the risk value of the distribution grid line unit. Finally, GA-BP neural network is used to build a line risk value prediction model for improvement

Self-Supervised Clustering Models Based on BYOL Network Structure

Contrastive-based clustering models usually rely on a large number of negative pairs to capture uniform representations, which requires a large batch size and high computational complexity. In contrast, some self-supervised methods perform non-contrastive learning to capture discriminative representations only with positive pairs, but suffer from the collapse of clustering. To solve these issues, a novel end-to-end self-supervised clustering model is proposed in this paper. The basic self-supervised learning network is first modified, followed by the incorporation of a Softmax layer to obtain cluster assignments as data representation. Then, adversarial learning on the cluster assignments is integrated into the methods to further enhance discrimination across different clusters and mitigate the collapse between clusters. To further encourage clustering-oriented guidance, a new cluster-level discrimination is assembled to promote clustering performance by measuring the self-correlation between the learned cluster assignments. Experimental results on real-world datasets exhibit better performance of the proposed model compared with the existing deep clustering methods