Publications by authors named "Xinchao Cui"
In order to ensure the safety of coal mine production, a mine water source identification model is proposed to improve the accuracy of mine water inrush source identification and effectively prevent water inrush accidents based on kernel principal component analysis (KPCA) and improved sparrow search algorithm (ISSA) optimized kernel extreme learning machine (KELM). Taking Zhaogezhuang mine as the research object, firstly, Na+, Ca2+, Mg2+, Cl-, SO2- 4 and HCO- 3 were selected as evaluation indexes, and their correlation was analyzed by SPSS27 software, with reducing the dimension of the original data by KPCA. Secondly, the Sine Chaotic Mapping, dynamic adaptive weights, and Cauchy Variation and Reverse Learning were introduced to improve the Sparrow Search Algorithm (SSA) to strengthen global search ability and stability.
View Article and Find Full Text PDFAccurate measurement of coal gas permeability helps prevent coal gas safety accidents effectively. To predict permeability more accurately, we propose the IDBO-BPNN coal body gas permeability prediction model. This model combines the Improved Dung Beetle algorithm (IDBO) with the BP neural network (BPNN).
View Article and Find Full Text PDFArticle Synopsis
- The study introduces a new cloud model that improves the assessment of coal and gas outburst risks using a comprehensive evaluation index system with primary and secondary indicators.
- It utilizes an enhanced Analytic Hierarchy Process (IAHP) and an improved CRITIC method to calculate both subjective and objective weights for the evaluation indicators, and combines these weights for optimal results.
- The findings from applying this model in a Hebei Province mine show that it outperforms traditional methods, accurately classifying the mine's risk level and aligning well with real-world observations, thus enhancing the reliability of mine risk assessments.
Article Synopsis
- The study focuses on enhancing the risk prediction of spontaneous combustion in gas extraction boreholes using a newly developed PSO-BPNN model, which combines particle swarm optimization with a backpropagation neural network for improved accuracy.
- The results indicate that the PSO-BPNN model significantly outperforms other models (BPNN, GA-BPNN, SSA-BPNN, and MPA-BPNN) in terms of prediction reliability, showing lower average relative and absolute errors and higher determination coefficients.
- When applied to coal mine extraction boreholes in Shanxi, the PSO-BPNN model's capabilities were effectively demonstrated, suggesting its practical value in preventing spontaneous combustion incidents.