With the increasing depth and intensity of coal mining operations,high-energy mine tremors have become a major trigger for rockburst disasters,posing severe threats to mine safety.Conventional rockburst risk assessmen...With the increasing depth and intensity of coal mining operations,high-energy mine tremors have become a major trigger for rockburst disasters,posing severe threats to mine safety.Conventional rockburst risk assessment methods either lack real-time adaptability or rely heavily on qualitative microseismic data analysis,limiting their effectiveness in dynamic early warning.To address these limitations,this study proposed a predictive framework for rockburst risk assessment by integrating ensemble learning algorithms with Bayesian optimization.A dataset was constructed using a sliding time window approach,linking the highest MS energy in the subsequent days with predefined risk levels.Both undersampling and oversampling strategies were employed to mitigate class imbalance,and their performance was evaluated.Three ensemble models,i.e.CatBoost,Random Forest,and LightGBM,were developed,and their hyperparameters were optimized using Bayesian techniques to enhance predictive performance.The models were validated using MS data from the 6303 and 6306 working faces at the Dongtan Coal Mine.All three ensemble models outperformed conventional classification methods,particularly in accurately predicting high-risk categories.Among them,the CatBoost model exhibited the best performance,with an accuracy of 89.47%and an F1¯-score of 90.62%.Furthermore,SHapley Additive exPlanations analysis was used to enhance model interpretability,identifying key MS indicators influencing rockburst risk predictions.This study provides a systematic approach for leveraging MS data and machine learning to improve an early warning system for rockburst hazards,offering valuable insights for underground mining safety management.展开更多
As the traditional character-oriented frame synchronization methods are no longer applicable to the byte-misaligned stream, and the efficiency of the bit-oriented method is hardly acceptable, a character-oriented bit-...As the traditional character-oriented frame synchronization methods are no longer applicable to the byte-misaligned stream, and the efficiency of the bit-oriented method is hardly acceptable, a character-oriented bit-shift stream frame synchronization （COBS-FS） method is presented. In order to measure the performance of the given method, a bit-oriented frame synchronization method, based on Knuth-Morris-Pratt （KMP-FS） algorithm, is used for comparison. It is proven in theory that the COBS-FS has a much lower cost in frame header searching. Experiment shows that the COBS-FS method is with better performance than the KMP-FS algorithm in both computational effort and execution time.展开更多
基金funded by the National Natural Science Foundation of China(Grant No.42477208)Natural Science Foundation of Hubei Province,China(Grant No.2024AFA072)Open Research Fund of State Key Laboratory of Geomechanics and Geotechnical Engineering Safety(Grant No.SKLGME-JBGS2402).
文摘With the increasing depth and intensity of coal mining operations,high-energy mine tremors have become a major trigger for rockburst disasters,posing severe threats to mine safety.Conventional rockburst risk assessment methods either lack real-time adaptability or rely heavily on qualitative microseismic data analysis,limiting their effectiveness in dynamic early warning.To address these limitations,this study proposed a predictive framework for rockburst risk assessment by integrating ensemble learning algorithms with Bayesian optimization.A dataset was constructed using a sliding time window approach,linking the highest MS energy in the subsequent days with predefined risk levels.Both undersampling and oversampling strategies were employed to mitigate class imbalance,and their performance was evaluated.Three ensemble models,i.e.CatBoost,Random Forest,and LightGBM,were developed,and their hyperparameters were optimized using Bayesian techniques to enhance predictive performance.The models were validated using MS data from the 6303 and 6306 working faces at the Dongtan Coal Mine.All three ensemble models outperformed conventional classification methods,particularly in accurately predicting high-risk categories.Among them,the CatBoost model exhibited the best performance,with an accuracy of 89.47%and an F1¯-score of 90.62%.Furthermore,SHapley Additive exPlanations analysis was used to enhance model interpretability,identifying key MS indicators influencing rockburst risk predictions.This study provides a systematic approach for leveraging MS data and machine learning to improve an early warning system for rockburst hazards,offering valuable insights for underground mining safety management.
文摘As the traditional character-oriented frame synchronization methods are no longer applicable to the byte-misaligned stream, and the efficiency of the bit-oriented method is hardly acceptable, a character-oriented bit-shift stream frame synchronization （COBS-FS） method is presented. In order to measure the performance of the given method, a bit-oriented frame synchronization method, based on Knuth-Morris-Pratt （KMP-FS） algorithm, is used for comparison. It is proven in theory that the COBS-FS has a much lower cost in frame header searching. Experiment shows that the COBS-FS method is with better performance than the KMP-FS algorithm in both computational effort and execution time.
