• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.485-492
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• 2021

Vision-based autonomous precision landing technology for UAVs requires precise position estimation and landing guidance technology. Also, for safe landing, it must be designed to determine the safety of the landing point against ground obstacles and to guide the landing only when the safety is ensured. In this paper, we proposes vision-based navigation, and algorithms for determining the safety of landing point to perform autonomous precision landings. To perform vision-based navigation, CNN technology is used to detect landing pad and the detection information is used to derive an integrated navigation solution. In addition, design and apply Kalman filters to improve position estimation performance. In order to determine the safety of the landing point, we perform the obstacle detection and position estimation in the same manner, and estimate the speed of the obstacle using LSM. The collision or not with the obstacle is determined based on the CPA calculated by using the estimated state of the obstacle. Finally, we perform flight test to verify the proposed algorithm.

• Design & Manufacturing
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• v.14 no.4
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• pp.40-45
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• 2020

In a vehicle-to-vehicle accident, the impact posture, braking status, final stopping position, collision point and collision speed are important factors for accident reconstruction. In particular, the speed of collision is the most important issue. In this study, the collision speed and the final stopping position in the tunnel were estimated using PC-CRASH, a vehicle crash analysis program used for traffic accident analysis, and the final stopping position of the simulation and the final stopping position of the traffic accident report were compared. When the Pride speed was 0km/h or 30km/h and the Sorento speed was 100m/h, the simulation results and reports matched the final stopping positions and posture of the two vehicles. As a result of the simulation, it can be estimated that Pride was collided in an almost stationary state.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.514-521
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• 2021

When calculating an intersection collision speed using a formula, it is very difficult to grasp the degree of deceleration of a vehicle after the collision unless there is road surface trace in the entire section where each vehicle moved from the point of collision to their final positions after the collision. A vehicle's motion trajectory shows an irregular curve after a collision due to the effects of inertia based on the driving characteristics of the vehicle, the eccentric force according to the collision site, and the collision speed. Therefore, it is very important to set the appropriate departure angle after a collision for accurate collision speed analysis. In this study, based on experimental collision data using a computer simulation (PC-Crash), the correlation between an appropriate vehicle departure angle and the post-collision speed was analyzed, and then, a regression analysis model was derived. Through this, we propose a method to calculate collision speed by applying only the vehicle departure angle in some types of collisions for traffic accidents at intersections.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.12
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• pp.561-568
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• 2021

In recent years, autonomous driving technologies have become a high-value-added technology that attracts attention in the fields of science and industry. For smooth Self-driving, it is necessary to accurately detect an object and estimate its movement speed in real time. CNN-based deep learning algorithms and conventional dense optical flows have a large consumption time, making it difficult to detect objects and estimate its movement speed in real time. In this paper, using a single camera image, fast object detection was performed using the YOLOv5 algorithm, a deep learning algorithm, and fast estimation of the speed of the object was performed by using a local dense optical flow modified from the existing dense optical flow based on the detected object. Based on this algorithm, we present a system that can predict the collision time and probability, and through this system, we intend to contribute to prevent tram accidents.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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• pp.818-826
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• 2015

This paper presents a novel methodology for estimating vehicle ego-motion, i.e. tri-axis linear velocities and angular velocities by using stereo vision sensor and 2G1Y sensor (longitudinal acceleration, lateral acceleration, and yaw rate). The estimated ego-motion information can be utilized to predict future ego-path and improve the accuracy of 3D coordinate of obstacle by compensating for disturbance from vehicle movement representatively for collision avoidance system. For the purpose of incorporating vehicle dynamic characteristics into ego-motion estimation, the state evolution model of Kalman filter has been augmented with lateral vehicle dynamics and the vanishing point estimation has been also taken into account because the optical flow radiates from a vanishing point which might be varied due to vehicle pitch motion. Experimental results based on real-world data have shown the effectiveness of the proposed methodology in view of accuracy.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.427-433
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• 2022

Obstacle detection, collision recognition, and avoidance technologies are required the collision avoidance technology for UAVs. In this paper, considering collinear multiple static obstacle, we propose an obstacle detection algorithm using LiDAR and a collision recognition and avoidance algorithm based on CPA. Preprocessing is performed to remove the ground from the LiDAR measurement data before obstacle detection. And we detect and classify obstacles in the preprocessed data using the K-means clustering algorithm. Also, we estimate the absolute positions of detected obstacles using relative navigation and correct the estimated positions using a low-pass filter. For collision avoidance with the detected multiple static obstacle, we use a collision recognition and avoidance algorithm based on CPA. Information of obstacles to be avoided is updated using distance between each obstacle, and collision recognition and avoidance are performed through the updated obstacles information. Finally, through obstacle location estimation, collision recognition, and collision avoidance result analysis in the Gazebo simulation environment, we verified that collision avoidance is performed successfully.

• Journal of information and communication convergence engineering
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• v.8 no.5
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• pp.519-523
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• 2010

In Gen-2 RFID system, the initial value of $Q_{fp}$, which is the slot-count parameter of Q-algorithm, is not defined in the standard. In this case, if the number of tags within the reader's identification range is small and we let the initial $Q_{fp}$ be large, the number of empty slot will be large. On the other hand, if we let the initial $Q_{fp}$ be small in spite of many tags, almost all the slots will be collided. As a result, the performance will be declined because the frame size does not converge to the optimal point quickly during the query round. In this paper, we propose a scheme to allocate the optimal initial $Q_{fp}$ through the tag number estimation before the query round begins. Through computer simulations, it is demonstrated that the proposed scheme achieves more stable performance than Gen-2 Q-algorithm.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.11a
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• pp.213-216
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• 2008

CFT(Concrete Filled Steel Tubular) Structure filled steel tubes with a concrete improves the stiffness and strength of the structure by the confinement effect of fillers. CFTA(Concrete Filled and Tied Steel Tubular Arch) girder is a new type structure that applies an arch structure and a pre-stressed structure to CFT Structure to maximize the efficiency of structure and economic. One of conspicuous characteristics of CFTA girder is exposed tendon and that is pointed out as the weak point of this girder. Therefore in this study, safety estimation for the exposed tendon is performed and dynamic analysis is also performed by the collision numerical simulation. For analyzing this model, ABAQUS 6.5-1 was used.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.3
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• pp.470-475
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• 2012

In Q-algorithm of EPCglobal Class-1 Gen-2 RFID system, the initial value of $Q_{fp}$, which is the slot-count parameter, is not defined in the standard. And the values of weight C, which is the parameter for incrementing or decrementing the slot-count size, are not determined. Therefore, if the number of tags is small and we let the initial $Q_{fp}$ be large, the number of empty slot will be large. On the other hand, if we let the initial $Q_{fp}$ be small in spite of many tags, almost all the slots will be collided. Also, if the reader selects an inappropriate weight, there are a lot of empty or collided slots. As a result, the performance will be declined because the frame size does not converge to the optimal point quickly during the query round. In this paper, we propose a scheme to allocate the optimal initial $Q_{fp}$ through the tag number estimation and select the weight based on the slot-count size of current query round.

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.61-71
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• 2003

Existent DDHV draws and is calculating K coefficient. D coefficient from sum of traffic volume two-directions time. There is difference of design order and actuality order, error of DDHV estimation value, problem of irregular change etc. of DDHV thereby. In this study, among traffic volume of each other independent two direction(going up, going down), decide design target order in the directional traffic volume, presented way(way) applying without separating K coefficient and D coefficient at the same time. The result were analysis about national highway permanent count point 360 points 30 orders by existing DDHV estimation value method(separation plan) analysis wave and following variation appear. - design order and actuality order are collision at 357 agencies(99.2%) - actuality order special quality : Measuring efficiency of average 80 orders, maximum 1,027 order, minimum 2 orders - error distribution of design order and actuality order : inside 10 hours is(30$\pm$10hour) 106 points(29.4%), 254 points(70.6%) more than 30 orders and $\pm$10 orders error occurrence be - DDHV estimation value : Average 8.4%, maximum 46.7% The other side, average 50 orders. error improvement effect of DDHV 8.4% was analysed that is at design hourly volume computation by inseparability method in case of AADT premises correct thing because inseparability plan agrees actuality order at whole agency with design order and measuring efficiency of DDHV estimation value is "0".t;0".uot;.