• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.643-644
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• 2023

본 논문에서는 고속도로 전용차로에서의 운행기준을 위반한 차량을 검지하는 시스템을 제안한다. 다인승 탑승차를 별도의 차로로 통행하도록 하여 혼잡도를 해소하겠다는 정책을 시행하고 있으며, 9인승 이상 차량에 6인 이상 텁숭자를 다인승 통행차량으로 정의하며, 이러한 기준을 만족하지 않는 차량을 자동 검지하는 시스템이다. 트리거 신호 검지기와 4조의 적외선 카메라로 차량 내부 촬영하고 결과 이미지를 분석하여 자동으로 다인승 차량을 판별하여 운행 위반을 검지한다. 테스트 결과 주야간에 관계없이 80% 이상의 우수한 검지율을 나타내었다.

• International Journal of Aeronautical and Space Sciences
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• v.1 no.1
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• pp.13-20
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• 2000

The applications of CFD in the design process of a transonic civil transport at Korea Aerospace Research Institute (KARI) are outlined. Three Navier-Stokes solvers, developed at KARI with different grid approaches, are used to predict the aerodynamic coefficients and solve the flowfield of various configurations. Multi-block, Chimera, and unstructured grids are the approaches implemented. The accuracy of the codes is verified for the transonic flow about RAE wing/fuselage configuration. The multi-block code is used to provide the detailed data on the flowfield around a wall interference model with different test section sizes which will be used in establishing the wall interference correction method. The subsonic and transonic flowfields about K100-04A, one of the configurations of a 100-seater transport developed by KARI and Korea Commercial Aircraft Development Consortium (KCDC), are computed to predict the aerodynamic coefficients. The results for the subsonic flow are compared with those of wind tunnel test, and the agreement is found to be excellent. The interference effect of nacelle installation on the wing of K100-04A is also investigated using the unstructured grid method, and about 10% reduction in wing lift is observed. The accuracy of the three developed codes is verified, and they are used as an efficient tool in the design process of a transonic transport.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.239-241
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• 2022

Currently, in Korea, a multi-seater lane (HOV) and a designated lane system are being implemented to solve traffic congestion. However, in both systems, it is difficult to crack down on cases of violations without permission, so people are required to be assigned to areas that want to crack down. In this process, manpower and budget are inefficiently consumed. To compensate for these shortcomings, we propose the development of an integrated enforcement system through YOLO, a deep learning object recognition model. If the two systems are implemented and integrated using YOLO, they will have advantages in terms of manpower and budget over existing systems because only data learning and system maintenance are considered. In addition, in the case of violations in which it is difficult for the existing unmanned system to crack down, the effect of increasing the crackdown rate through continuous learning can be expected.