Privacy protection is a hot research topic in information security field.An improved XGBoost algorithm is proposed to protect the privacy in classification tasks.By combining with differential privacy protection,the X...Privacy protection is a hot research topic in information security field.An improved XGBoost algorithm is proposed to protect the privacy in classification tasks.By combining with differential privacy protection,the XGBoost can improve the classification accuracy while protecting privacy information.When using CART regression tree to build a single decision tree,noise is added according to Laplace mechanism.Compared with random forest algorithm,this algorithm can reduce computation cost and prevent overfitting to a certain extent.The experimental results show that the proposed algorithm is more effective than other traditional algorithms while protecting the privacy information in training data.展开更多
Driving fatigue is one of the important causes of accidents in tunnel(group)sections.In this paper,in order to effectively identify the driving fatigue of tunnel(group)drivers,an eye tracker and other instruments were...Driving fatigue is one of the important causes of accidents in tunnel(group)sections.In this paper,in order to effectively identify the driving fatigue of tunnel(group)drivers,an eye tracker and other instruments were used to conduct real vehicle tests on long tunnel(group)expressways and thus obtain the eye movement,driving duration,and Karolinska sleepiness scale(KSS)data of 30 drivers.The impacts of the tunnel and non-tunnel sections on drivers were compared,and the relationship between blink indexes,such as the blink frequency,blink duration,mean value of blink duration,driving duration,and driving fatigue,was studied.A paired t-test and a Spearman correlation test were performed to select the indexes that can effectively characterize the tunnel driving fatigue.A driving fatigue detection model was then developed based on the XGBoost algorithm.The obtained results show that the blink frequency,total blink duration,and mean value of blink duration gradually increase with the deepening of driving fatigue,and the mean value of blink duration is the most sensitive in the tunnel environment.In addition,a significant correlation exists between the driving duration index and driving fatigue,which can provide a reference for improving the tunnel safety.Using the mean value of blink duration and driving duration as the characteristic indexes,the accuracy of the driving fatigue detection model based on the XGBoost algorithm reaches 98%.The cumulative and continuous tunnel proportion effectively estimates the driving fatigue state in a long tunnel(group)environment.展开更多
基金This work is supported by the NSFC[Grant Nos.61772281,61703212,61602254]Jiangsu Province Natural Science Foundation[Grant No.BK2160968]the Priority Academic Program Development of Jiangsu Higher Edu-cation Institutions(PAPD)and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology(CICAEET).
文摘Privacy protection is a hot research topic in information security field.An improved XGBoost algorithm is proposed to protect the privacy in classification tasks.By combining with differential privacy protection,the XGBoost can improve the classification accuracy while protecting privacy information.When using CART regression tree to build a single decision tree,noise is added according to Laplace mechanism.Compared with random forest algorithm,this algorithm can reduce computation cost and prevent overfitting to a certain extent.The experimental results show that the proposed algorithm is more effective than other traditional algorithms while protecting the privacy information in training data.
基金supported by the National Natural Science Foundation of China(52362050,52472347)Science and Technology Project of Shandong Transportation Department(2022KJ-044)+1 种基金“Hongliu Excellent Young”Talents Support Program of Lanzhou University of Technologythe Fundamental Research Funds for the Cornell University,CHD University(300102223505)。
文摘Driving fatigue is one of the important causes of accidents in tunnel(group)sections.In this paper,in order to effectively identify the driving fatigue of tunnel(group)drivers,an eye tracker and other instruments were used to conduct real vehicle tests on long tunnel(group)expressways and thus obtain the eye movement,driving duration,and Karolinska sleepiness scale(KSS)data of 30 drivers.The impacts of the tunnel and non-tunnel sections on drivers were compared,and the relationship between blink indexes,such as the blink frequency,blink duration,mean value of blink duration,driving duration,and driving fatigue,was studied.A paired t-test and a Spearman correlation test were performed to select the indexes that can effectively characterize the tunnel driving fatigue.A driving fatigue detection model was then developed based on the XGBoost algorithm.The obtained results show that the blink frequency,total blink duration,and mean value of blink duration gradually increase with the deepening of driving fatigue,and the mean value of blink duration is the most sensitive in the tunnel environment.In addition,a significant correlation exists between the driving duration index and driving fatigue,which can provide a reference for improving the tunnel safety.Using the mean value of blink duration and driving duration as the characteristic indexes,the accuracy of the driving fatigue detection model based on the XGBoost algorithm reaches 98%.The cumulative and continuous tunnel proportion effectively estimates the driving fatigue state in a long tunnel(group)environment.
