Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that empl...Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.展开更多
Purpose： This study aimed to investigate whether workload intensity modulates exercise-induced effect on reaction time （RT） performances, and more specifically to clarify whether cognitive control that plays a cruc...Purpose： This study aimed to investigate whether workload intensity modulates exercise-induced effect on reaction time （RT） performances, and more specifically to clarify whether cognitive control that plays a crucial role in rapid decision making is altered. Methods： Fourteen participants performed a Simon Task while cycling 20 min at a light （first ventilatory threshold, VT~ - 20%）, moderate （VTI）, or very hard （VTj ＋ 20%） level of exercise. Results： After 15 min of cycling, RTs are faster than during the first 5 min of exercise. This benefit does not fluctuate with the intensity of exercise and enlarges as RT lengthens. Despite a numerical difference suggesting a greater facilitation during moderate exercise （-16 ms） than during a light exercise （-10 ms）, the benefit is not statistically different. Interestingly, we did not observe any signs of worsening on RT or on accuracy during very hard exercise. Conclusion： Cognitive control is extremely robust and appears not to be affected by the intensity of exercise. The selective inhibition and the between-trials adjustments are effective from the beginning to the end of exercise, regardless of the workload output.展开更多
基金Project(42077244)supported by the National Natural Science Foundation of ChinaProject(2020-05)supported by the Open Research Fund of Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization,China。
文摘Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.
基金supported by a grant from the French Research Agency (ANR 2013-069)
文摘Purpose： This study aimed to investigate whether workload intensity modulates exercise-induced effect on reaction time （RT） performances, and more specifically to clarify whether cognitive control that plays a crucial role in rapid decision making is altered. Methods： Fourteen participants performed a Simon Task while cycling 20 min at a light （first ventilatory threshold, VT~ - 20%）, moderate （VTI）, or very hard （VTj ＋ 20%） level of exercise. Results： After 15 min of cycling, RTs are faster than during the first 5 min of exercise. This benefit does not fluctuate with the intensity of exercise and enlarges as RT lengthens. Despite a numerical difference suggesting a greater facilitation during moderate exercise （-16 ms） than during a light exercise （-10 ms）, the benefit is not statistically different. Interestingly, we did not observe any signs of worsening on RT or on accuracy during very hard exercise. Conclusion： Cognitive control is extremely robust and appears not to be affected by the intensity of exercise. The selective inhibition and the between-trials adjustments are effective from the beginning to the end of exercise, regardless of the workload output.
