Despite the extensive studies conducted on the effectiveness of microwave treatment as a novel rock preconditioning method,there is yet to find reliable data on the rock failure mechanisms due to microwave heating.In ...Despite the extensive studies conducted on the effectiveness of microwave treatment as a novel rock preconditioning method,there is yet to find reliable data on the rock failure mechanisms due to microwave heating.In addition,there is no significant discussion on the energy efficiency of the method as one of the important factors among the mining and geotechnical engineers in the industry.This study presents a novel experimental method to evaluate two main rock failure mechanisms due to microwave treatment without applying any mechanical forces,i.e.distributed and concentrated heating.The result shows that the existence of a small and concentrated fraction of a strong microwave absorbing mineral will change the failure mechanism from the distributed heating to the concentrated heating,which can increase the weakening over microwave efficiency(WOME)by more than 10 folds.This observation is further investigated using the developed coupled numerical model.It is shown that at the same input energy,the existence of microwave absorbing minerals can cause major heat concentration inside the rock and increase the maximum temperature by up to three times.展开更多
A clustering-based undersampling(CUS)and distance-based near-miss method are widely used in current imbalanced learning algorithms,but this method has certain drawbacks.In particular,the CUS does not consider the infl...A clustering-based undersampling(CUS)and distance-based near-miss method are widely used in current imbalanced learning algorithms,but this method has certain drawbacks.In particular,the CUS does not consider the influence of the distance factor on the majority of instances,and the near-miss method omits the inter-class(es)within the majority of samples.To overcome these drawbacks,this study proposes an undersampling method combining distance measurement and majority class clustering.Resampling methods are used to develop an ensemble-based imbalanced-learning algorithm called the clustering and distance-based imbalance learning model(CDEILM).This algorithm combines distance-based undersampling,feature selection,and ensemble learning.In addition,a cluster size-based resampling(CSBR)method is proposed for preserving the original distribution of the majority class,and a hybrid imbalanced learning framework is constructed by fusing various types of resampling methods.The combination of CDEILM and CSBR can be considered as a specific case of this hybrid framework.The experimental results show that the CDEILM and CSBR methods can achieve better performance than the benchmark methods,and that the hybrid model provides the best results under most circumstances.Therefore,the proposed model can be used as an alternative imbalanced learning method under specific circumstances,e.g.,for providing a solution to credit evaluation problems in financial applications.展开更多
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia,for funding this research work through the project number(IFPRC036-135-2020)and King Abdulaziz University,DSR,Jeddah,Saudi Arabia.
文摘Despite the extensive studies conducted on the effectiveness of microwave treatment as a novel rock preconditioning method,there is yet to find reliable data on the rock failure mechanisms due to microwave heating.In addition,there is no significant discussion on the energy efficiency of the method as one of the important factors among the mining and geotechnical engineers in the industry.This study presents a novel experimental method to evaluate two main rock failure mechanisms due to microwave treatment without applying any mechanical forces,i.e.distributed and concentrated heating.The result shows that the existence of a small and concentrated fraction of a strong microwave absorbing mineral will change the failure mechanism from the distributed heating to the concentrated heating,which can increase the weakening over microwave efficiency(WOME)by more than 10 folds.This observation is further investigated using the developed coupled numerical model.It is shown that at the same input energy,the existence of microwave absorbing minerals can cause major heat concentration inside the rock and increase the maximum temperature by up to three times.
文摘A clustering-based undersampling(CUS)and distance-based near-miss method are widely used in current imbalanced learning algorithms,but this method has certain drawbacks.In particular,the CUS does not consider the influence of the distance factor on the majority of instances,and the near-miss method omits the inter-class(es)within the majority of samples.To overcome these drawbacks,this study proposes an undersampling method combining distance measurement and majority class clustering.Resampling methods are used to develop an ensemble-based imbalanced-learning algorithm called the clustering and distance-based imbalance learning model(CDEILM).This algorithm combines distance-based undersampling,feature selection,and ensemble learning.In addition,a cluster size-based resampling(CSBR)method is proposed for preserving the original distribution of the majority class,and a hybrid imbalanced learning framework is constructed by fusing various types of resampling methods.The combination of CDEILM and CSBR can be considered as a specific case of this hybrid framework.The experimental results show that the CDEILM and CSBR methods can achieve better performance than the benchmark methods,and that the hybrid model provides the best results under most circumstances.Therefore,the proposed model can be used as an alternative imbalanced learning method under specific circumstances,e.g.,for providing a solution to credit evaluation problems in financial applications.
