• Journal of Multimedia Information System
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• v.6 no.4
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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.

• Journal of Korea Multimedia Society
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• v.21 no.8
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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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• v.9 no.1
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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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.167-177
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• 2018

As the number of attacks on vehicles has increased, studies on CAN-based security technologies are actively being carried out. However, since the upper layer protocol of CAN differs for each vehicle manufacturer and model, there is a great difficulty in researches such as developing anomaly detection for CAN or finding vulnerabilities of ECUs. In this paper, we propose a method to infer the detailed structure of the data field of CAN frame by analyzing CAN trace to mitigate this problem. In the existing Internet environment, many researches for reverse engineering proprietary protocols have already been carried out. However, CAN bus has a structure difficult to apply the existing protocol reverse engineering technology as it is. In this paper, we propose new field classification methods with low computation-cost based on the characteristics of data in CAN frame and existing field classification method. The proposed methods are verified through implementation that analyze CAN traces generated by simulations of CAN communication and actual vehicles. They show higher accuracy of field classification with lower computational cost compared to the existing method.