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양미연,한은규,김명숙 한국유전학회 2013 Genes & Genomics Vol.35 No.2
The marine red algae Grateloupia is the largest genus in the family Halymeniaceae and widely distributed from tropical to warm temperate regions of the world. In the genus Grateloupia, especially G. elliptica and G. lanceolata have common features of bladelike thalli with leather in texture and cruciately divided tetrasporangia. Due to this similar morphology, G. elliptica and G. lanceolata are frequently confused and resulted in considerable difficulty distinguishing these two taxa. We have reassessed the relationships between two species using molecular identification including plastid rbcL and mitochondrial COI genes to more accurately define their genetic diversity owing to the confusion of identification. As a result, the chloroplast-encoded rbcL sequence analyses support the distinction of two species, G. elliptica and G. lanceolata collected from Jeju Island, Korea and Japan at the species level, with interspecific divergence of 3.7-4.6 %. The genetic diversity of COI gene within species are estimated to be 0–0.3 % in G. elliptica and 0–1.0 % in G. lanceolata, respectively. The effectiveness of mtDNA COI barcoding in the identification for two species demonstrates in this study.
박래근(Raegeun Park),윤혁진(Hyeok-Jin Yun),한은규(Ean-Gyu Han),강성우(Seong-Woo Kang),전대현(Dae-Hyeon Jeon),박지훈(Ji-Hoon Park),정권영(Kown-Young Jung),강태구(Tae-Koo Kang) 제어로봇시스템학회 2020 제어·로봇·시스템학회 논문지 Vol.26 No.5
Research on autonomous vehicles, such as lane recognition, sign recognition, LiDAR data processing, and avoidance control, continues to expand, and recently, commercially available vehicles were partially supplied with level 3 autonomous driving technology for use in highway environments. However, in urban environments, the application of level 3 technology is limited due to variables such as traffic lights and pedestrians. For this reason, for autonomous driving in urban environments, an integrated system of technologies for responding to many variables, such as road component recognition and obstacle avoidance, is required. However, if each sensor in the integrated system malfunctions or arrives at different judgment based on the same situation, a problem in vehicle control may occur due to exclusive data collision. Therefore, this paper proposes an integrated control system that develops a technology for responding to situations that occur during driving in urban environments, such as lane recognition, traffic light recognition, and obstacle avoidance, and gives priority to each algorithm. Experiments were conducted to confirm the performance of each algorithm and to verify the performance of the integrated system. Experiments were conducted at various section of K-City(an autonomous driving test city in Korea). This integrated system showed how to appropriately respond to common situation in urban driving while considering many factors such as left turn, right turn, straight driving, and emergency braking. It is expected that the system proposed in this study and the experimental results obtained in K-City can be applied to actual urban environments in order to reflect various adverse conditions, such as road damage, encountered while driving on roads.