In this paper,the reliability of sense-switch p-channel flash is evaluated extensively.The endurance result indicates that the p-channel flash could be programmed and erased for more than 10000 cycles;the room tempera...In this paper,the reliability of sense-switch p-channel flash is evaluated extensively.The endurance result indicates that the p-channel flash could be programmed and erased for more than 10000 cycles;the room temperature read stress shows negligible influence on the p-channel flash cell;high temperature data retention at 150℃ is extrapolated to be about 5 years and 53 years corresponding to 30% and 40% degradation in the drive current,respectively.Moreover,the electrical parameters of the p-channel flash at different operation temperature are found to be less affected.All the results above indicate that the sense-switch p-channel flash is suitable to be used as the configuration cell in flash-based FPGA.展开更多
In order to improve the accuracy of used car price prediction,a machine learning prediction model based on the retention rate is proposed in this paper.Firstly,a random forest algorithm is used to filter the variables...In order to improve the accuracy of used car price prediction,a machine learning prediction model based on the retention rate is proposed in this paper.Firstly,a random forest algorithm is used to filter the variables in the data.Seven main characteristic variables that affect used car prices,such as new car price,service time,mileage and so on,are filtered out.Then,the linear regression classification method is introduced to classify the test data into high and low retention rate data.After that,the extreme gradient boosting(XGBoost)regression model is built for the two datasets respectively.The prediction results show that the comprehensive evaluation index of the proposed model is 0.548,which is significantly improved compared to 0.488 of the original XGBoost model.Finally,compared with other representative machine learning algorithms,this model shows certain advantages in terms of mean absolute percentage error(MAPE),5%accuracy rate and comprehensive evaluation index.As a result,the retention rate-based machine learning model established in this paper has significant advantages in terms of the accuracy of used car price prediction.展开更多
文摘In this paper,the reliability of sense-switch p-channel flash is evaluated extensively.The endurance result indicates that the p-channel flash could be programmed and erased for more than 10000 cycles;the room temperature read stress shows negligible influence on the p-channel flash cell;high temperature data retention at 150℃ is extrapolated to be about 5 years and 53 years corresponding to 30% and 40% degradation in the drive current,respectively.Moreover,the electrical parameters of the p-channel flash at different operation temperature are found to be less affected.All the results above indicate that the sense-switch p-channel flash is suitable to be used as the configuration cell in flash-based FPGA.
基金Supported by the Postgraduate Education Reform Project of Yangzhou University (JGLX2021_002)。
文摘In order to improve the accuracy of used car price prediction,a machine learning prediction model based on the retention rate is proposed in this paper.Firstly,a random forest algorithm is used to filter the variables in the data.Seven main characteristic variables that affect used car prices,such as new car price,service time,mileage and so on,are filtered out.Then,the linear regression classification method is introduced to classify the test data into high and low retention rate data.After that,the extreme gradient boosting(XGBoost)regression model is built for the two datasets respectively.The prediction results show that the comprehensive evaluation index of the proposed model is 0.548,which is significantly improved compared to 0.488 of the original XGBoost model.Finally,compared with other representative machine learning algorithms,this model shows certain advantages in terms of mean absolute percentage error(MAPE),5%accuracy rate and comprehensive evaluation index.As a result,the retention rate-based machine learning model established in this paper has significant advantages in terms of the accuracy of used car price prediction.
