Jae-Ryon Kwag
Jae-Ryon Kwag has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Hyeong-Jun Kim
Department of Future Automotive Engineering, Gyeongsang National University, Jinju, South Korea
1 shared publication - In-Hyuk Han
Performance Research Team, NEXEN Tire, Yangsan-si 50592, Korea
1 shared publication - Jun-Young Han
School of Mechanical Engineering, Pusan National University, Busan 46241, Republic of Korea
1 shared publication - Man-Ho Kim
Division of Automotive Engineering, Dong-eui Institute of Technology, Busan 47230, Korea
1 shared publication - Min-Gu Kuk
Performance Research Team, NEXEN Tire, Yangsan-si 50592, Korea
1 shared publication - Suk Lee
School of Mechanical Engineering, Pusan National University, Busan 46241, Korea
1 shared publication
Latest Publications
- Open Access
- 28 February 2020
- Published by MDPI AG in Electronics
- Vol. 9 (3) , 404
A Road Condition Classification Algorithm for a Tire Acceleration Sensor using an Artificial Neural Network
The automotive industry is experiencing a period of innovation, represented by the term CASE (connected, autonomous, shared, and electric). Among the innovative new technologies for automobiles, intelligent tire (iTire) collects road surface information through sensors installed inside a tire and informs the driver of the road conditions. iTire can promote safe driving. Various kinds of research on iTire is ongoing, and this paper proposes an algorithm to determine the road surface conditions while driving. Specifically, we have proposed a method for extracting the feature points of a frequency band, by converting acceleration data collected by sensors through fast Fourier transform (FFT) and determining road surface conditions via an artificial neural network. Lastly, the applicability of the algorithm was verified.
- 28 February 2020
- Published by MDPI AG in Electronics
- Vol. 9 (3) , 404
Open AccessA Road Condition Classification Algorithm for a Tire Acceleration Sensor using an Artificial Neural Network
The automotive industry is experiencing a period of innovation, represented by the term CASE (connected, autonomous, shared, and electric). Among the innovative new technologies for automobiles, intelligent tire (iTire) collects road surface information through sensors installed inside a tire and informs the driver of the road conditions. iTire can promote safe driving. Various kinds of research on iTire is ongoing, and this paper proposes an algorithm to determine the road surface conditions while driving. Specifically, we have proposed a method for extracting the feature points of a frequency band, by converting acceleration data collected by sensors through fast Fourier transform (FFT) and determining road surface conditions via an artificial neural network. Lastly, the applicability of the algorithm was verified.