• Proceedings of the KIEE Conference
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• 2005.07c
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• pp.1853-1855
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• 2005

본 논문에서는 카메라폰용 광학줌(Optical zooming)과 자동초점조절장치(Auto Focusing, AF)에 쓰일 초음파모터를 제작하였다. 초음파모터의 제작 및 시뮬레이션은 유한요소해석 프로그램인 ATILA 5.2.1(Magsoft)를 사용하여 디자인설계에 따른 구동특성을 고찰하였고 세자된 초음파모터는 한쪽 면이 없는 사자형의 탄성체를 제작하였으며 탄성체의 양쪽 다리에 각각 압전체를 부착하였다. 또한 압전세라믹의 조성은 $0.9Pb(Zr_{0.51}Ti_{0.49})O_3$-$0.1Pb(Mn_{1/3}Nb_{1/3}Sb_{1/3})O_3$의 조성으로 설계하였고 시편의 제조는 7-layer로 적층하였다. 제작된 압전세라믹의 치수는 $6*2*0.35mm^3$ (길이*폭*두께)로 제작하였다. 또한 탄성체의 외형치수는 $10*10*2mm^3$ 제작하였으며 두께를 각각 0.3[mm], 0.5[mm], 0.8[mm]으로 변화시키며 제작하였다. 두께가 0.8[mm]인 경우 공진주파수 60.5[kHz]를 나타내었으며 초음파모터의 압전세라믹에 인가전압이 증가함에 따라 회전속도와 모터에 흐르는 전류는 증가하였다. 인가전압이 40[Vpp], 공진주파수는 60.5[kHz]일때 무게추를 달고 그때의 토크 및 효율을 측정하였다. 회전 속도는 토크가 증가함에 따라 거의 선형적으로 감소하였으며 효율은 토크에 따라 증가하다가 토크가 약 20(mNmm)일 때 약 28[%]로 최대치를 나타내었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.141-143
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• 2022

This paper presented a new technology to identify traffic violations of motorbike riders by detecting the traffic signal using You Only Look Once (YOLO) object detection. The hardware module that is mounted on the front of the motorbike consists of Raspberry Pi with a camera to run the YOLO object detection, a GPS module to acquire the motorcycle's coordinate, and a LoRa communication module to send the data to a cloud DB. The main goal of the software is to determine whether a motorbike has violated a traffic signal. This paper proposes a function to recognize the red traffic signal colour with its movement inside the camera angle and determine that the traffic signal violation happens if the traffic signal is moving to the right direction (the rider turns left) or moving to the top direction (the riders goes straight). Furthermore, if a motorbike rider is violated the signal, the rider's personal information (name, mobile phone number, etc), the snapshot of the violation situation, rider's location, and date/time will be sent to a cloud DB. The violation information will be delivered to the driver's smartphone as a push notification and the local police station to be used for issuing violation tickets, which is expected to prevent motorbike riders from violating traffic signals.

• Journal of IKEEE
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• v.18 no.4
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• pp.471-477
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• 2014

In this paper, we propose a low-cost license plate recognition system based on smart cam system using Android. The proposed system consists of a portable device and server. Potable device Hardware consists of ARM Cortex-A9 (S5PV210) processor control unit, a power supply device, wired and wireless communication, input/output unit. We develope Linux kernel and dedicated device driver for WiFi module and camera. The license plate recognition algorithm is consisted of setting candidate plates areas with canny edge detector, extracting license plate number with Labeling, recognizing with template matching, etc. The number that is recognized by the device is transmitted to the remote server via the user mobile phone, and the server re-transfer the vehicle information in the database to the portable device. To verify the utility of the proposed system, user photographs the license plate of any vehicle in the natural environment. Confirming the recognition result, the recognition rate was 95%. The proposed system was suitable for low cost portable license plate recognition device, it enabled the stability of the system when used long time by using the Android operating system.

• Journal of Broadcast Engineering
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• v.27 no.2
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• pp.228-239
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• 2022

In this paper, we propose an unmanned vehicle scratch detection deep learning model for car sharing services. Conventional scratch detection models consist of two steps: 1) a deep learning module for scratch detection of images before and after rental, 2) a manual matching process for finding newly generated scratches. In order to build a fully automatic scratch detection model, we propose a one-step unmanned scratch detection deep learning model. The proposed model is implemented by applying transfer learning and fine-tuning to the deep learning model that detects changes in satellite images. In the proposed car sharing service, specular reflection greatly affects the scratch detection performance since the brightness of the gloss-treated automobile surface is anisotropic and a non-expert user takes a picture with a general camera. In order to reduce detection errors caused by specular reflected light, we propose a preprocessing process for removing specular reflection components. For data taken by mobile phone cameras, the proposed system can provide high matching performance subjectively and objectively. The scores for change detection metrics such as precision, recall, F1, and kappa are 67.90%, 74.56%, 71.08%, and 70.18%, respectively.