• 천문학회보
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• 제41권1호
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• pp.49.2-49.2
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• 2016

MIRIS Paschen-${\alpha}$ ($Pa{\alpha}$) Galactic Plane Survey (MIPAPS) presents the first whole Galactic plane (with the width of $-3^{\circ}$ < b < $+3^{\circ}$) map for the $Pa{\alpha}$ emission line. Many of $Pa{\alpha}$ features were detected more brightly than the previous observed $H{\alpha}$ features, and they coincide well with dense cloud regions. This means that newly detected $Pa{\alpha}$ blobs can indicate massive star forming regions (H II regions) screened by foreground clouds around Galactic plane. Anderson et al. (2014) presented the most complete Galactic H II region catalog based on WISE 12 and 22 um data. Of the cataloged sources, only ~20% have measured radio recombination line (RRL) or $H{\alpha}$ emission, and the rest are still candidate H II regions. At first, we compare the MIPAPS results with Anderson's H II region catalog for the Cepheus region (Galactic longitude from $+96^{\circ}$ to $116^{\circ}$). From this, we will investigate how much MIPAPS can supplement the catalog, and show MIPAPS scientific potential. After that, we plan to extend this work to the whole plane, and finally catalog MIRIS $Pa{\alpha}$ blob sources for the whole Galactic plane.

• 대한조선학회논문집
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• 제61권5호
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• pp.324-333
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• 2024

This paper focuses on the cooperation between Unmanned Aerial Vehicle (UAV) and Unmanned Surface Vessel (USV). It aims to develop efficient guidance and control algorithms for USV based on obstacle identification and path planning from aerial images captured by UAV. Various obstacle scenarios were implemented using the Robot Operating System (ROS) and the Gazebo simulation environment. The aerial images transmitted in real-time from UAV to USV are processed using the computer vision-based deep learning model, You Only Look Once (YOLO), to classify and recognize elements such as the water surface, obstacles, and ships. The recognized data is used to create a two-dimensional grid map. Algorithms such as A^* and Rapidly-exploring Random Tree star (RRT^*) were used for path planning. This process enhances the guidance and control strategies within the UAV-USV collaborative system, especially improving the navigational capabilities of the USV in complex and dynamic environments. This research offers significant insights into obstacle avoidance and path planning in maritime environments and proposes new directions for the integrated operation of UAV and USV.