• Journal of Advanced Marine Engineering and Technology
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• v.40 no.7
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• pp.622-628
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• 2016

The prosperity of IT technologies and the removal of restrictions regarding Unmanned Aerial Vehicles (UAVs), also known as drones, have driven growth in their popularity. However, without a proper solution to the problem of accident avoidance for UAVs, this popularity increases the potential for collisions between UAVs and between UAV and terrain features. These collisions can occur because UAVs to date have flown using radio control or image recognition based autonomous navigation. Therefore, we propose efficient UAV routing schemes to tackle the collision problem using vehicular communication systems. Performance evaluation by computer simulation shows that the proposed methods effectively reduce the collision probability and improve the routing efficiency of the UAV. Furthermore, the proposed algorithms are compatible and can be directly applied with small overhead to the commercial vehicular communication system implementation.

• Journal of the Korean Society for Precision Engineering
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• v.27 no.10
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• pp.52-60
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• 2010

It is generally important to get a precise position information for autonomous unmanned vehicle(AUV) to run safely. For getting the position of AUV, the GPS has been using to navigation in a vehicle. Though it is useful to finding a position, it is difficult to precisely control a trajectory of the AUV due to large measuring error which may reach over 10 meters. Therefore to apply AUV it needs to compensate for the error. This paper proposes a method to more precisely localize AUV using three low-cost differential global positioning systems (DGPS). The distance errors between each DGPS are minimized as using the least square method (LSM) and the Kalman filter to eliminate a Gaussian white noise. The selected DGPS is cheaper and easier to set up than the RTK-GPS. It is also more precise than the general GPS. The proposed method can compensate the relatively position error according to stationary and moving distance of the AUV. For evaluating the algorithm by simulation, the DGPS signal with the Gaussian white noise to any points is generated by the AR model and compared with the measurement signal. It is confirmed that the proposed method can effectively compensate the position error as comparing with the measurement signal. The compensated position signal can be used to localize and control the AUV in the road.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.717-719
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• 2015

The research and development of the unmanned vehicle is being carried out actively in domestic and foreign countries. The research is being carried out to provide various services so that the weakness of system such as conventional 2D-based navigation systems can be supplemented and the driving can be safer. This paper suggests the method that enables real-time video processing in more efficient way by realizing the location detection and signal recognition technique of traffic signals in video. In order to overcome the limit of conventional methods that have a difficulty in analyzing the signal as it is sensitive to brightness change, the proposed method realizes the program that grasps the depth data in front of the vehicle using video processing, analyzes the signal by detecting traffic signal and estimates color components of traffic signal in front and the distance between traffic signal and the vehicle.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.115-123
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• 2021

Pine nuts are Korea's representative nut forest products and profitable crops. However, pine nuts are harvested by climbing the trees themselves, thus the risk is high. In order to solve this problem, it is necessary to harvest pine nuts using a robot or an unmanned aerial vehicle(UAV). In this paper, we propose a deep learning based detection method for harvesting pine nut in UAV aerial images. For this, a video was recorded in a real pine forest using UAV, and a data augmentation technique was used to supplement a small number of data. As the data for 3D detection, Unity3D was used to model the virtual pine nut and the virtual environment, and the labeling was acquired using the 3D transformation method of the coordinate system. Deep learning algorithms for detection of pine nuts distribution area and 2D and 3D detection of pine nuts objects were used DeepLabV3+, YOLOv4, and CenterNet, respectively. As a result of the experiment, the detection rate of pine nuts distribution area was 82.15%, the 2D detection rate was 86.93%, and the 3D detection rate was 59.45%.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.140-145
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• 2013

This paper proposes an efficient integration method for GPS (Global Positioning System) and INS (Inertial Navigation System). To obtain accuracy and computational conveniency at the same time with low cost global positioning system receivers and micro mechanical inertial sensors, a new mechanization method and a new filter architecture are proposed. The proposed mechanization method simplifies velocity and attitude computation by eliminating the need to compute complex transport rate related to the locally-level frame which continuously changes due to unpredictable vehicle motions. The proposed filter architecture adopts two heterogeneous filters, i.e. position-domain Hatch filter and velocity-aided Kalman filter. Due to distict characteristics of the two filters and the distribution of computation into the two hetegrogeneous filters, it eliminates the cascaded filter problem of the conventional loosly-coupled integration method and mitigates the computational burden of the conventional tightly-coupled integration method. An experiment result with field-collected measurements verifies the feasibility of the proposed method.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.6
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• pp.267-275
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• 2013

EKF has been widely used for GPS/INS integration as standard method but EKF has one well-known drawback. if the errors are not within the bounded region, the filter may be divergent. The particle filter has the advantage of the nonlinear and non-gaussian system. This paper proposes a method for compensating the GPS position errors based on the particle filter and presents loosely-coupled GPS/INS integration using proposed algorithm. We used GPS position pattern with particle filter and added attitude kalman filter for improving attitude accuracy. To verify the performance, the proposed method is compared with high cost GPS as reference. In the experimental result, we verified that the accuracy and robust were well improved by the proposed method filter effectively and robustness than by original loosely-coupled integration when vehicle turns at corner.

• Journal of IKEEE
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• v.2 no.1 s.2
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• pp.139-149
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• 1998

There are some problems in robot navigations with a single kind of sensor. We propose a system that takes advantages of both CCD camera and ultrasonic sensors for the concerning matter. A coordinate extraction algorithm to avoid obstacles during the navigation is also proposed. We implemented a CCD based vision system at the front part of the vehicle and did experiments to verify the suggested algorithm's availability. From experimental results, the error rate was reduced when a CCD camera was used rather than when only ultrasonic sensors were used. Also we can generate path to avoid those obstacles using the measured values.

• Journal of Digital Contents Society
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• v.18 no.5
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• pp.889-894
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• 2017

The purpose of this paper is to implement smart farm using automatic navigation, short - range wireless communication network technology, and automatic take - off and landing system using unmanned aerial vehicle to maximize the efficiency of grazing farm management. The grazing pasture management system that integrates ICT fusion technology for the activation of the mountain ecological livestock production is expected to contribute to the improvement of the productivity of the grazing livestock, the infrastructure to produce the excellent quality, and the competitiveness of the livestock industry in response to the FTA. And it will contribute to the improvement of career force through the supply to the farmhouse.

• 전자공학회논문지 IE
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• v.47 no.1
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• pp.48-55
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• 2010

The goal of this paper is to determine optimal hardware platform for specific applications. In order to develop an understanding of how select the optimal platform, we focus upon the real-time embedded vehicle system for processing forward image and sound. In this paper we propose to measure parameters such as instructions, execution cycle, required memory size for program and data by using ARMulator. We have measured three types of processor cores: ARM7, ARM9 and ARM10. The results of the study indicated that the proposed methods could measure the minimal requirements of hardware platform for specific applications. By defining lower limit of hardware specifications in embedded systems, we can minimize expenses with suitable system performance without implementing the system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1196-1206
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• 2013

In this paper, based on the information about navigation system of UAV with PTZ camera and 3D topography, algorithm able to show us in real-time UAV's geographical shooting location and automatically calculate superficial measure of the shooting area is proposed. And the method that can automatically estimate whether UAV is allowed to shoot a specific area is shown. In case of an UAV's shooting attempt at the specific area, obtainability of valid image depends on not only UAV's location but also information of 3D topography. As a result of the study, Ground Control Center will have real-time information about whether UAV can shoot the needed topography. Therefore, accurate remote flight control will be possible in real-time. Furthermore, the algorithm and the method of estimating shooting probability can be applied to pre-flight simulation and set of flight route.