• Journal of Multimedia Information System
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• 제6권4호
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• pp.235-238
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• 2019

In real world object detection is an active research topic for understanding different objects from images. There are different models presented in past and had significant results. In this paper we are presenting vehicle logo detection using previous object detection models such as You only look once (YOLO) and Faster Region-based CNN (F-RCNN). Both the front and rear view of the vehicles were used for training and testing the proposed method. Along with deep learning an image pre-processing algorithm called perspective transformation is proposed for all the test images. Using perspective transformation, the top view images were transformed into front view images. This algorithm has higher detection rate as compared to raw images. Furthermore, YOLO model has better result as compare to F-RCNN model.

• 한국멀티미디어학회논문지
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• 제21권8호
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• pp.888-896
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• 2018

In this paper, we report detection and recognition of vehicle logo from images captured from street CCTV. Image data includes both the front and rear view of the vehicles. The proposed method is a two-step process which combines image preprocessing and faster region-based convolutional neural network (R-CNN) for logo recognition. Without preprocessing, faster R-CNN accuracy is high only if the image quality is good. The proposed system is focusing on street CCTV camera where image quality is different from a front facing camera. Using perspective transformation the top view images are transformed into front view images. In this system, the detection and accuracy are much higher as compared to the existing algorithm. As a result of the experiment, on day data the detection and recognition rate is improved by 2% and night data, detection rate improved by 14%.

• International Journal of Aeronautical and Space Sciences
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• 제9권1호
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• pp.100-110
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• 2008

It is not easy to monitor and identify all engine faults and conditions using conventional fault detection approaches like the GPA (Gas Path Analysis) method due to the nature and complexity of the faults. This study therefore focuses on a model based diagnostic method using Neural Network algorithms proposed for fault detection on a turbo shaft engine (PW 206C) selected as the power plant for a tilt rotor type unmanned aerial vehicle (Smart UAV). The model based diagnosis should be performed by a precise performance model. However component maps for the performance model were not provided by the engine manufacturer. Therefore they were generated by a new component map generation method, namely hybrid method using system identification and genetic algorithms that identifies inversely component characteristics from limited performance deck data provided by the engine manufacturer. Performance simulations at different operating conditions were performed on the PW206C turbo shaft engine using SIMULINK. In order to train the proposed BPNN (Back Propagation Neural Network), performance data sets obtained from performance analysis results using various implanted component degradations were used. The trained NN system could reasonably detect the faulted components including the fault pattern and quantity of the study engine at various operating conditions.

• 정보보호학회논문지
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• 제28권1호
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• pp.167-177
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• 2018

최근 차량에 대한 공격 사례가 늘어남에 따라 CAN 기반의 보안 기술에 대한 연구가 활발히 진행되고 있다. 그러나 CAN의 상위 계층 프로토콜은 차량 제조사 및 모델 별로 상이하므로 이상 탐지 기술 또는 ECU 대상의 취약점 탐지를 위한 연구에는 큰 어려움이 따른다. 본 논문에서는 이러한 문제를 완화하기 위하여 CAN 트레이스의 분석을 통해 데이터 필드 영역의 세부 구조를 추론하는 방법을 제안한다. 기존 인터넷 환경에서는 이미 프로토콜 역공학을 위한 연구가 다수 진행되었으나, CAN 버스는 기존의 프로토콜 역공학 기술을 그대로 적용하기 어려운 구조를 지닌다. 본 논문에서는 CAN 프레임 내 데이터의 특성을 이용한 낮은 계산 비용의 필드 구분 방법 및 기존의 CAN 데이터필드 내 필드 분류 방법을 이용한 새로운 추론 방법을 제안한다. 본 논문에서 제안하는 방식은 실제 차량의 CAN 트레이스 및 시뮬레이션으로 생성된 CAN 트레이스를 대상으로 검증되며, 기존 방식 대비 더 낮은 계산 비용으로 더 높은 정확도의 필드 구조 추론 결과를 보인다.