• Animal Bioscience
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• v.36 no.6
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• pp.851-860
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• 2023

Objective: This study aims to evaluate the target genes of gga-miR-20a-5p and the regulated immune responses in the chicken macrophage cell line, HD11, by the exosome-mediated delivery of miR-20a-5p. Methods: Exosomes were purified from the chicken macrophage cell line HD11. Then, mimic gga-miR-20p or negative control miRNA were internalized into HD11 exosomes. HD11 cells were transfected with gga-miR-20a-5p or negative control miRNA containing exosomes. After 44 h of transfection, cells were incubated with or without 5 ㎍/mL poly(I:C) for 4 h. Then, expression of target genes and cytokines was evaluated by quantitative realtime polymerase chain reaction. Results: Using a luciferase reporter assay, we identified that gga-miR-20a-5p directly targeted interferon gamma receptor 2 (IFNGR2), mitogen-activated protein kinase 1 (MAPK1), mitogen-activated protein kinase kinase kinase 5 (MAP3K5), and mitogen-activated protein kinase kinase kinase 14 (MAP3K14). Moreover, the exosome-mediated delivery of gga-miR-20a-5p successfully repressed the expression of IFNGR2, MAPK1, MAP3K5, and MAP3K14 in HD11 cells. The expressions of interferon-stimulated genes (MX dynamin like GTPase 1 [MX1], eukaryotic translation initiation factor 2A [EIF2A], and oligoadenylate synthase-like [OASL]) and proinflammatory cytokines (interferon-gamma [IFNG], interleukin-1 beta [IL1B], and tumor necrosis factor-alpha [TNFA]) were also downregulated by exosomal miR-20a-5p. In addition, the proliferation of HD11 cells was increased by exosomal miR-20a-5p. Conclusion: The exosome-mediated delivery of gga-miR-20a-5p regulated immune responses by controlling the MAPK and apoptotic signaling pathways. Furthermore, we expected that exosomal miR-20a-5p could maintain immune homeostasis against highly pathogenic avian influenza virus H5N1 infection by regulating the expression of proinflammatory cytokines and cell death.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.102-116
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• 2021

For the safe operation of autonomous vehicles, the representative technology of the 4^th industrial revolution era, a combination of various technologies such as sensor technology, software technology and car technology is required. An autonomous vehicle is a vehicle that recognizes current location and situation by using the various sensors, and makes its own decisions without depending on the driver. Perfect recognition technology is required for fully autonomous driving. Since the precise road maps provide various road information including lanes, stop lines, traffic lights and crosswalks, it is possible to minimize the cognitive errors that occur in autonomous vehicles by using the precise road maps with location information of the road facilities. In this study, the definition, necessity and technical trends of the precise road map have been analyzed, and the HD(High Definition) map prototype based on the web GIS has been built in the autonomous driving-specialized areas of Daegu Metropolitan City(Suseong Medical District, about 24km), the Happy City of Sejong Special Self-Governing City(about 33km), and the FMTC(Future Mobility Technical Center) PG(Proving Ground) of Seoul National University Siheung Campus using the MMS(Mobile Mapping System) surveying results given by the National Geographic Information Institute. In future research, the built-in precise road map service will be installed in the autonomous vehicles and control systems to verify the real-time locations and its location correction algorithm.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.146-154
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• 2019

Recently, a system has been developed and applied to establish and utilize HD maps through R&D. The biggest problem, however, is the lack of a proper HD map update system, which requires the development and adoption of such a system as soon as possible. In addition, in the case of updating HD maps for automated driving, integrity and accuracy of maps are required for safe driving, so an test of these technologies and data quality is required. In April 2018, the Ministry of Land, Infrastructure and Transport implemented a project to 'Develop Technology to Demonstrate and Share the Instant Road Change Detection and Update Technology for automated driving. This paper analyzed the technology for updating map based on the investigation and analysis of relevant technology trends for the development of integrated demonstration and sharing technology of road change rapid detection and updating map technology, and put forward the criteria for road change rapid detection, integrated quality verification of update technology. It is expected that the results of this study will contribute to quality assurance of HD maps that support safety driving for automated vehicles.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.1373-1378
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• 2006

대용량의 네트워크 인프라가 확대되고, 네트워크를 통한 DV, HD 급의 고화질 비디오 전송이 보편화 되면서, 고화질의 비디오에 몰입감, 현실감을 증진시키기 위한 스테레오 HD 비디오 전송이 가능하게 되었다. 본 논문은 IP 네트워크를 통해서 스테레오 HD 비디오 전송을 가능하게 해주는 소프트웨어 기반의 HD 비디오 전송 시스템에서 효과적으로 몰입감과 입체감을 제공하기 위해 충족되어야 하는 좌우 영상의 동기화에 필요한 요소들을 다룬다. 제안된 동기화 기법은 수신 측에서 최종적으로 동기화된 좌우 영상을 통해 스테레오 HD 비디오를 얻기 위해서 좌우 카메라로부터 영상의 획득 시, 획득된 영상의 네트워크 전송 시, 또 수신된 영상의 재생 시 좌우 영상의 동기화한다. 결과적으로 동기화 된 좌우 영상을 통하여 몰입감과 현실감을 가지는 스트레오 HD 비디오를 실시간으로 감상할 수 있다.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.12
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• pp.70-80
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• 1997

This paper proposes a hierarchical Hausdorff distance (HD) matching algorithm baased on coarse-to-fine approach. It reduces the computational complexity greatly by using the pyramidal structures consisting of distance transform (DT) and edge pyramids. Also, inthe proposed hierarchical HD matching, a thresholding method is presented to find an optimal matching position with small error, in which the threshold values are determined by using the property between adjacent level of a DT map pyramid. By computer simulation, the performance of the conventional and proposed hierarchical HD matching algorithms is compared in therms of the matching position for binary images containing uniform noise.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.07a
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• pp.35-38
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• 2011

최근 3DTV 의 급격한 활성화로 스테레오 영상 콘텐츠 제작이 크게 증가하고 있다. 스테레오 영상은 일반 2D 영상과 달리 깊이(depth)가 존재하므로 자막과 같은 그래픽의 삽입에서 그 깊이를 반드시 고려해야 한다. 또한 시각피로를 줄이기 위해 스테레오 촬영 시 영상의 변이맵(disparity map)을 실시간 관찰할 필요성도 요구되고 있다. 본 논문에서는 최신의 그래픽 하드웨어를 이용하여 듀얼스트림 HD 영상을 실시간으로 스테레오 정합하는 방법을 제안한다.

• Journal of Drive and Control
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• v.17 no.4
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• pp.80-86
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• 2020

Autonomous driving is one of the most important new technologies of our time; it has benefits in terms of safety, the environment, and economic issues. Path following algorithms, such as automated lane keeping systems (ALKSs), are key level 3 or higher functions of autonomous driving. Pure-Pursuit and Stanley controllers are widely used because of their good path tracking performance and simplicity. However, with the Pure-Pursuit controller, corner cutting behavior occurs on curved roads, and the Stanley controller has a risk of divergence depending on the response of the steering system. In this study, we use the advantages of each controller to propose a hybrid control strategy that can be stably applied to complex driving environments. The weight of each controller is determined from the global and local curvature indexes calculated from HD map information and the current driving speed. Our experimental results demonstrate the ability of the hybrid controller, which had a cross-track error of under 0.1 m in a virtual environment that simulates K-City, with complex driving environments such as urban areas, community roads, and high-speed driving roads.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.1996-2015
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• 2023

High-definition (HD) maps can provide precise road information that enables an autonomous driving system to effectively navigate a vehicle. Recent research has focused on leveraging semantic segmentation to achieve automatic annotation of HD maps. However, the existing methods suffer from low recognition accuracy in automatic driving scenarios, leading to inefficient annotation processes. In this paper, we propose a novel semantic segmentation method for automatic HD map annotation. Our approach introduces a new encoder, known as the convolutional transformer hybrid encoder, to enhance the model's feature extraction capabilities. Additionally, we propose a multi-level fusion module that enables the model to aggregate different levels of detail and semantic information. Furthermore, we present a novel decoupled boundary joint decoder to improve the model's ability to handle the boundary between categories. To evaluate our method, we conducted experiments using the Bird's Eye View point cloud images dataset and Cityscapes dataset. Comparative analysis against stateof-the-art methods demonstrates that our model achieves the highest performance. Specifically, our model achieves an mIoU of 56.26%, surpassing the results of SegFormer with an mIoU of 1.47%. This innovative promises to significantly enhance the efficiency of HD map automatic annotation.

• Journal of the Korean Association of Geographic Information Studies
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• v.27 no.1
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• pp.29-40
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• 2024

Recently, the technology of autonomous driving, one of the core of the fourth industrial revolution, is developing, but sensor-based autonomous driving is showing limitations, such as accidents in unexpected situations, To compensate for this, HD-map is being used as a core infrastructure for autonomous driving, and interest in the public and private sectors is increasing, and various studies and technology developments are being conducted to secure the latest and accuracy of HD-map. Currently, NGII will be newly built in urban areas and major roads across the country, including the metropolitan area, where self-driving cars are expected to run, and is working to minimize data error rates through quality verification. Therefore, this study analyzes the spatial relationship of reference objects in the attribute structuring process for rapid and accurate renewal and production of HD-map under construction by NGII, By applying the attribute input automation methodology of the reference object in which spatial relations are established using the library of open source-based PyQGIS, target sites were selected for each road type, such as high-speed national highways, general national highways, and C-ITS demonstration sections. Using the attribute automation tool developed in this study, it took about 2 to 5 minutes for each target location to automatically input the attributes of the spatial relationship reference object, As a result of automation of attribute input for reference objects, attribute input accuracy of 86.4% for high-speed national highways, 79.7% for general national highways, 82.4% for C-ITS, and 82.8% on average were secured.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.39-44
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• 2022

This paper presents LiDAR static obstacle map based vehicle position correction algorithm for urban autonomous driving. Real Time Kinematic (RTK) GPS is commonly used in highway automated vehicle systems. For urban automated vehicle systems, RTK GPS have some trouble in shaded area. Therefore, this paper represents a method to estimate the position of the host vehicle using AVM camera, front camera, LiDAR and low-cost GPS based on Extended Kalman Filter (EKF). Static obstacle map (STOM) is constructed only with static object based on Bayesian rule. To run the algorithm, HD map and Static obstacle reference map (STORM) must be prepared in advance. STORM is constructed by accumulating and voxelizing the static obstacle map (STOM). The algorithm consists of three main process. The first process is to acquire sensor data from low-cost GPS, AVM camera, front camera, and LiDAR. Second, low-cost GPS data is used to define initial point. Third, AVM camera, front camera, LiDAR point cloud matching to HD map and STORM is conducted using Normal Distribution Transformation (NDT) method. Third, position of the host vehicle position is corrected based on the Extended Kalman Filter (EKF).The proposed algorithm is implemented in the Linux Robot Operating System (ROS) environment and showed better performance than only lane-detection algorithm. It is expected to be more robust and accurate than raw lidar point cloud matching algorithm in autonomous driving.