• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.661-664
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• 2006

무선 이동 Ad hoc 네트워크는 고정된 인프라의 도움 없이 이동 노드들의 협력에 의해 자율적으로 구성되는 독립적이고 융통성 있는 네트워크이다. 최근 상업적인 분야에서도 Ad hoc 네트워크의 응용에 대한 관심이 급증하면서 Ad hoc 네트워크의 보안 문제도 해결되어야 할 기술적 요구사항으로 대두되고 있다. 특히, 이러한 요구사항을 만족시키기 위해 Ad hoc 네트워크상에서 공개키 기반구조(PKI)를 도입하는 것은 고정된 인프라를 사용하지 않는 애드혹 네트워크 특성상 매우 제한적인 기법이다. 또한 password를 기반으로 하는 인증된 키교환을 하기위해서는 모든 사용자가 사전에 password를 공유를 해야 하는 불편함이 있다. 본 논문에서는 인증을 위한 제 3의 신뢰기관이나, 사전 비밀 공유가 필요 없는 인증된 키교환 프로토콜을 제안한다.

• Journal of the military operations research society of Korea
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• v.25 no.1
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• pp.29-36
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• 1999

In this paper, we are concerned with the K-shortest loopless path problem. The MPS algorithm, recently proposed by Martins et al., finds paths efficiently because it solves the shortest path problem only one time unlike other algorithms. But its computational complexity has not been known yet. We propose a few techniques by which the MPS algorithm can be implemented efficiently. First, we use min-heap data structure for the storage of candidate paths in order to reduce searching time for finding minimum distance path. Second, we prevent the eliminated paths from reentering in the list of candidate paths by lower bounding technique. Finally, we choose the source mode as a deviation node, by which selection time for the deviation node is reduced and the performance is improved in spite of the increase of the total number of candidate paths.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.295-301
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• 2021

Digital media technology is gradually developing with the development of convergence quaternary industrial technology and mobile devices. The combination of deep learning and augmented reality can provide more convenient and lively services through the interaction of 3D virtual images with the real world. We combine deep learning-based pose prediction with augmented reality technology. We predict the eight vertices of the bounding box of the object in the image. Using the predicted eight vertices(x,y), eight vertices(x,y,z) of 3D mesh, and the intrinsic parameter of the smartphone camera, we compute the external parameters of the camera through the PnP algorithm. We calculate the distance to the object and the degree of rotation of the object using the external parameter and apply to AR content. Our method provides services in a web environment, making it highly accessible to users and easy to maintain the system. As we provide augmented reality services using consumers' smartphone cameras, we can apply them to various business fields.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.31-40
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• 2022

YOLOv4 can be used for detecting parking vehicles in order to check a vehicle in out-door parking space. YOLOv4 has 9 anchor boxes in each of 13x13 grid cells for detecting a bounding box of object. Because anchor boxes are allocated based on each cell, there can be existed small observational error for detecting real objects due to the distance between neighboring cells. In this paper, we proposed YOLOv4 grid cell shift algorithm for improving the out-door parking vehicle detection accuracy. In order to get more chance for trying to object detection by reducing the errors between anchor boxes and real objects, grid cells over image can be shifted to vertical, horizontal or diagonal directions after YOLOv4 basic detection process. The experimental results show that a combined algorithm of a custom trained YOLOv4 and a cell shift algorithm has 96.6% detection accuracy compare to 94.6% of a custom trained YOLOv4 only for out door parking vehicle images.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.28-43
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• 1995

This paper concerns with automatic generation of fuzzy rules which can be used for pattern classification. Feature space is recursively subdivided into hyperspheres, and each hypersphere is represented by its centroid and bounding distance. Fuzzy rules are then generated based on the constructed hyperspheres. The resulting fuzzy rules have very simple premise parts, and they can be organized into a hierarchical structure so that classification process can be implemented very rapidly. The experimented results show that the suggested method works very well compared to other methods.

• Journal of Korean Institute of Industrial Engineers
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• v.37 no.2
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• pp.144-152
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• 2011

Most of the models for the vehicle routing problems studied in the literature assumed constant travel times. However, those approaches may give infeasible solutions when traffic congestion causes delays in travel time. To overcome such difficulty, there have been some researches considering the change of the travel time which is called the time dependent vehicle routing problem (TDVRP). TDVRP assumes that the travel time between two locations is not only affected by the distance traveled, but by many other factors including the time of the day. In this paper, we propose a branch-and-price algorithm to solve the TDVRP. The time dependent property of the travel time is dealt with an enumeration scheme with bounding procedures in the column generation procedure identifying a profitable route. The proposed algorithm guarantees the "Non-passing" property to be held in the solutions. The algorithm was tested on problems composed of the Solomon's benchmark instances for 25 and 50 nodes. Computational results are reported.

• Journal of Auto-vehicle Safety Association
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• v.13 no.4
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• pp.7-13
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• 2021

This paper presents an vulnerable road user (VRU) classification and tracking algorithm using vision and LiDAR sensor fusion method for urban autonomous driving. The classification and tracking for vulnerable road users such as pedestrian, bicycle, and motorcycle are essential for autonomous driving in complex urban environments. In this paper, a real-time object image detection algorithm called Yolo and object tracking algorithm from LiDAR point cloud are fused in the high level. The proposed algorithm consists of four parts. First, the object bounding boxes on the pixel coordinate, which is obtained from YOLO, are transformed into the local coordinate of subject vehicle using the homography matrix. Second, a LiDAR point cloud is clustered based on Euclidean distance and the clusters are associated using GNN. In addition, the states of clusters including position, heading angle, velocity and acceleration information are estimated using geometric model free approach (GMFA) in real-time. Finally, the each LiDAR track is matched with a vision track using angle information of transformed vision track and assigned a classification id. The proposed fusion algorithm is evaluated via real vehicle test in the urban environment.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.1-8
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• 2024

Robotic arms have been widely utilized in various labor-intensive industries such as manufacturing, agriculture, and food services, contributing to increasing productivity. In the development of industrial robotic arms, camera sensors have many advantages due to their cost-effectiveness and small sizes. However, estimating object positions is a challenging problem, and it critically affects to the robustness of object manipulation functions. This paper proposes a method for estimating the 3D positions of objects, and it is applied to a pick-and-place task. A deep learning model is utilized to detect 2D bounding boxes in the image plane, and the pinhole camera model is employed to compute the object positions. To improve the robustness of measuring the 3D positions of objects, we analyze the effect of lens distortion and introduce a false positive filtering process. Experiments were conducted on a real-world scenario for moving medicine bottles by using a camera-based manipulator. Experimental results demonstrated that the distortion removal and false positive filtering are effective to improve the position estimation precision and the manipulation success rate.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1454-1464
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• 2013

In this paper, we propose a rapid rigid registration method for the fusion visualization of intra-operative 2D XA and pre-operative 3D CTA images. In this paper, we propose a global movement estimation based on a trilateration for the fast and robust initial registration. In addition, the principal axis of each image is generated and aligned, and the bounding box of the vascular shape is compared for more accurate initial registration. For the fine registration, two images are registered where the distance between two vascular structures is minimized by selective distance measure. In the experiment, we evaluate a speed, accuracy and robustness using five patients' data by comparing the previous registration method. Our proposed method shows that two volumes can be registered at optimal location rapidly, and robustly comparing with the previous method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10C
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• pp.942-948
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• 2003

In this paper, we analyze the performance of multiple trellis coded modulation applied to continuous phase frequency shift keying (MTCM/CPFSK) for the noncoherent receiver without channel state information (CSI) on the interleaved Rician fading channel. In this system, the squared cross-correlation between the received signal and a candidate signal is used as the branch metric of the Viterbi decoder. To obtain the bit error performance of this system, we analyze the approximated pairwise error probability (PEP) and the exact PEP. We also derive the equivalent normalized squared distance (ENSD) and compare it with the ENSD of the noncoherent receiver with perfect CSI. Simulation results are also provided to verify the theoretical performance analysis.