At present,Chinese financial supervision departments are constrained by information asymmetry and higher supervision costs,so their effectiveness in the ever-changing financial supervision needs to be improved urgentl...At present,Chinese financial supervision departments are constrained by information asymmetry and higher supervision costs,so their effectiveness in the ever-changing financial supervision needs to be improved urgently.Based on the G-SIBs fintech index,this paper analyzes the scores of fintech r&d,promotion,application and other indicators,aiming to explain the necessity of fintech regulation,and puts forward measures to strengthen fintech regulation.展开更多
In order to acquire the degradation state of rolling bearings and achieve predictive maintenance,this paper proposed a novel Remaining Useful Life(RUL)prediction of rolling bearings based on Long Short Term Memory(LST...In order to acquire the degradation state of rolling bearings and achieve predictive maintenance,this paper proposed a novel Remaining Useful Life(RUL)prediction of rolling bearings based on Long Short Term Memory(LSTM)neural network.The method is divided into two parts:feature extraction and RUL prediction.Firstly,a large number of features are extracted from the original vibration signal.After correlation analysis,the features that can better reflect the degradation trend of rolling bearings are selected as input of prediction model.In the part of RUL prediction,LSTM that making full use of the network’s memory in time is used to improve the accuracy of RUL prediction.The proposed method is validated by life cycle experimental data of bearings,and the RUL prediction results of LSTM model are compared with Support Vector Regression(SVR)and Light Gradient Boosting Machine(LightGBM)models respectively.The results show that the proposed method is more suitable for RUL prediction of rolling bearings.展开更多
文摘At present,Chinese financial supervision departments are constrained by information asymmetry and higher supervision costs,so their effectiveness in the ever-changing financial supervision needs to be improved urgently.Based on the G-SIBs fintech index,this paper analyzes the scores of fintech r&d,promotion,application and other indicators,aiming to explain the necessity of fintech regulation,and puts forward measures to strengthen fintech regulation.
基金This work is supported by the National Nature Science Foundation of China(No.51875100).The authors would like to thank anonymous reviewers and the associate editor,whose constructive comments help improve the presentation of this work.
文摘In order to acquire the degradation state of rolling bearings and achieve predictive maintenance,this paper proposed a novel Remaining Useful Life(RUL)prediction of rolling bearings based on Long Short Term Memory(LSTM)neural network.The method is divided into two parts:feature extraction and RUL prediction.Firstly,a large number of features are extracted from the original vibration signal.After correlation analysis,the features that can better reflect the degradation trend of rolling bearings are selected as input of prediction model.In the part of RUL prediction,LSTM that making full use of the network’s memory in time is used to improve the accuracy of RUL prediction.The proposed method is validated by life cycle experimental data of bearings,and the RUL prediction results of LSTM model are compared with Support Vector Regression(SVR)and Light Gradient Boosting Machine(LightGBM)models respectively.The results show that the proposed method is more suitable for RUL prediction of rolling bearings.
