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DriveMe:Towards Lightweight and Practical Driver Authentication System Using Single-Sensor Pressure Data
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作者 Mohsen Ali Alawami Dahyun Jung +3 位作者 Yewon Park Yoonseo Ku Gyeonghwan Choi Ki-Woong Park 《Computer Modeling in Engineering & Sciences》 2025年第5期2361-2389,共29页
To date,many previous studies have been proposed for driver authentication;however,these solutions have many shortcomings and are still far from practical for real-world applications.In this paper,we tackle the shortc... To date,many previous studies have been proposed for driver authentication;however,these solutions have many shortcomings and are still far from practical for real-world applications.In this paper,we tackle the shortcomings of the existing solutions and reach toward proposing a lightweight and practical authentication system,dubbed DriveMe,for identifying drivers on cars.Our novelty aspects are 1⃝Lightweight scheme that depends only on a single sensor data(i.e.,pressure readings)attached to the driver’s seat and belt.2⃝Practical evaluation in which one-class authentication models are trained from only the owner users and tested using data collected from both owners and attackers.3⃝Rapid Authentication to quickly identify drivers’identities using a few pressure samples collected within short durations(1,2,3,5,or 10 s).4⃝Realistic experiments where the sensory data is collected from real experiments rather than computer simulation tools.We conducted real experiments and collected about 13,200 samples and 22,800 samples of belt-only and seat-only datasets from all 12 users under different settings.To evaluate system effectiveness,we implemented extensive evaluation scenarios using four one-class detectors One-Class Support Vector Machine(OCSVM),Local Outlier Factor(LOF),Isolation Forest(IF),and Elliptic Envelope(EE),three dataset types(belt-only,seat-only,and fusion),and four different dataset sizes.Our average experimental results show that the system can authenticate the driver with an F1 score of 93.1%for seat-based data using OCSVM classifier,an F1 score of 98.53%for fusion-based data using LOF classifier,an F1 score of 91.65%for fusion-based data using IF classifier,and an F1 score of 95.79%for fusion-based data using EE classifier. 展开更多
关键词 Driver authentication pressure data SENSOR car machine learning
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The Design of Temperature and Pressure Data Collector Based on HART protocol
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作者 SONG Quan-you GUO Bin 《微计算机信息》 2011年第1期43-44,71,共3页
The Field bus device based on HART field communications protocol has been widely used in industrial control. In the pro-duction of petroleum, the method which using the HART bus to transfer the temperature and pressur... The Field bus device based on HART field communications protocol has been widely used in industrial control. In the pro-duction of petroleum, the method which using the HART bus to transfer the temperature and pressure data collected from the oilwell, can improve the traditional collecting method's shortage, prevent the failure of the data transfer caused of the rupture of the oiltransfer pole, moreover, can enhance the collection accuracy. 展开更多
关键词 HART:field bus:temperature and pressure data collection PETROLEUM
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TRANSONIC WALL IN TERFERENCE CORRECTIONS FOR CIVIL AIRCRAFT MODEL TESTS
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作者 Fan Zhaolin Cui Naiming +1 位作者 Yun Qilin Yin Lupin(China Aerodynamics Research & Development Center,Mianyang, Sichuan, China, 621000) 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 1994年第3期160-169,共10页
The correction method uses the static pressures measured near the tunnelwalls during model tests as boundary conditions. It is required that the flow near thewalls is subsonic and the freestream Mach number is less th... The correction method uses the static pressures measured near the tunnelwalls during model tests as boundary conditions. It is required that the flow near thewalls is subsonic and the freestream Mach number is less than 1. It is still valid whenthere are shock waves and supersonic pockets near the model as long as shock waves donot extend to walls, and the method is applicable to various ventilated wall or solid walltest sections. Corrections for three models tested abroad are in quite good agreementwith NASA's results, which are obtained by a nonhnear correction method. Thepresnet method has been apphed to B737 inodel tcsted in CARDC 1.2 m wind tunnel.The results show that this method is suitable for transonic wall interference correctionfor high aspect ratio airplane tests. 展开更多
关键词 aerodynamic interference transonic wind tunnels wall pressure. data reduction
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