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명현호 ( Myeong¸ Hyeon-ho ),장면 ( Jang¸ Myeon ),이점숙 ( Lee¸ Jeom-sook ),이정윤 ( Lee¸ Jong-yun ),최대훈 ( Choi¸ Dae-hun ) 한국도서(섬)학회 2021 韓國島嶼硏究 Vol.33 No.2
본 연구는 통영시 138개 무인도서의 관속식물 분포특성과 식생 조사를 위해 2019년 5월부터 10월까지 현지조사를 실시하였다. 조사지역에 분포하는 관속식물은 85과 301종 3아종 24변종 2품종 등 총 330분류군으로 조사되었다. 출현종에 대한 빈도분석을 한 결과 돌가시나무가 120로 가장 높은 빈도로 분석되었으며 억새(112회), 명아주(107회), 돈나무(102회), 곰솔(100회) 순으로 확인되었다. 환경 평가, 식물의 특성 및 서식처 보전 우선순위를 정하는데 활용되는 식물구계학적 특정 식물은 총 70 분류군으로 전체 출현종 중 21.21%를 나타났다. 또한 자생식물의 서식지를 침입해 생태계 영향을 미치는 귀화식물은 21종이 확인되었으며, 그중 망초(87회), 미국자리공(54회)로 나타났다. 해안선을 중심으로 생육하며, 염분스트레스나 혹독한 환경에서 살아가기 위해 형태 및 생리적으로 잘 적응한 염생식물은 32종이 생육하는 것으로 확인되었다. 출현 빈도를 분석 한 결과 해국, 갯장구채, 갯까치수염, 갯강아지풀 순으로 나타났다. 연안에 생육하는 염생식물은 광합성을 통해 흡수 효과적이다. 식물 생활형의 분류 방식으로 활용되고 있는 라운키에르 생활형으로 분류 한 결과 반지지중식물(H)이 87분류군(26.36%)으로 가장 많았으며, 그 다음으로 대형 육상식물(M)이 78분류군(23.64%), 일년생식물(Th)이 53분류군(16.06%)으로 나타났다. 식생은 대부분이 산지침염수림인 곰솔군락이 우점 분포하고 있으며 그 외에 소나무군락, 후박나무군락, 동백나무군락, 소사나무군락, 굴피나무군락, 느릅나무군락, 구실잣밤나무군락, 섬향나무군락, 밀사초군락, 참억새군락, 칡군락, 이대군락, 왕대군락 등이 분포하고 있었다. 조사지역은 지리학상 남해안아구에 속하고, 식생은 난온대삼림대에 해당되어 후박나무, 동백나무, 구실잣밤나무군락과 같은 상록활엽수림 천이가 활발하게 진행할 것으로 예상된다. 도서생물지리학 이론 관점에서 식물종 분포의 특성을 분석한 결과 도서면적에 대한 환경변수에 대해 출현종수(r<sup>2</sup>=0.6544)와 귀화식물(r<sup>2</sup>=0.524)에 대해서는 유의한 상관성을 나타났으나 귀화율 및 염생식물 출현양상은 상관성이 나타나지 않았다, 또한 육지와의 거리에 대해 출현 종수, 귀화식물, 귀화율, 염생식물은 상관성이 나타나지 않았다. 통영시 무인도서는 남해안 도서 중에서도 비교적 생태계 보전과 자연경관이 뛰어난 지역으로 식물상 및 식생 분포 특성에 대한 평가를 통해 효율적 관리방안을 수립하여야 한다. This survey was carried out to investigate the flora and vegetation of Tongyeong uninhabited island from May 2019 to October 2019. Vascular plants were identified as 330 taxa in total, including 85 families, 301 species, 3 subspecies, and 24 varieties, with 2 forms of Rosa wichuraiana s. showing the highest frequency at 120, followed by Miscanthus sinensis var. purpurascens(112 times), Chenopodium album var. centrorubrum (107 times), Pittosporum tobira(102 times), and Pinus thunbergii(100 times). There are a total of 70 taxa(21.21%) of floristic regional indicator plants specially designated by the Ministry of the Environment. 21 taxa of naturalized plants were also found, among them, in the order of Erigeron canadensis(87 times), and Phytolacca americana(54 times). Halophytes were identified as 31 species and the most frequent species were Aster spathulifolius, Silene aprica var. oldhamiana, Lysimachia mauritiana and Setaria viridis var. pachystachys. Furthermore Hemicryptophytes(H) had 87 taxa(26.36%), Microphanerophyte(M) was at 78 taxa(23.64%), and Therophytes(Th) stood at 53 taxa (16.06%), which showed a high proportional ratio in life forms. According to the physio-gnomic classification, this area can be divided into the following 5 categories. The deciduous broad-leaved forest, evergreen coniferous forest, evergreen broad-leaved forest, herb vegetation and plantation forest. such as Pinus densiflora, Pinus thunbergii, Machilus thunbergii, Camellia japonica, Carpinus turczaninowii, Platycarya strobilacea, Ulmus davidiana var. japonica, Castanopsis sieboldii, Juniperus chinensis, Carex boottiana, Miscanthus sinensis, Pueraria lobata, Pseudosasa japonica, and the Phyllostachys bambusoides community. Number of species and naturalized plants at the islands shows positive correlation with the size of the island (r<sup>2</sup>=0.6544, r<sup>2</sup>=0.524), but the correlations with distance from the mainland is not significant. The survey area is one of the southern coastal islands with a relatively excellent ecosystem of conservation and natural landscape. Therefore, for each island, management plans should be established for the sustainable development of uninhabited islands.
Hyeonho CHO(조현호),Joonho LEE(이준호),Hyundo HWANG(황현도),Woonbong HWANG(황운봉),Jin-Gyun KIM(김진균),Sunghan KIM(김승한) 대한기계학회 2021 대한기계학회 춘추학술대회 Vol.2021 No.11
By using the spin assisted layer-by-layer technique, the graphene oxide/silk fibroin based bionanocomposites were manufactured. The water vapor annealing process was employed in order to enhance the interfacial properties between the graphene oxide and silk fibroin. Moreover, the mechanical properties of the graphene oxide-based nanocomposites were found to be influenced by the water vapor annealing process. The mechanical properties of the graphene oxide-based nanocomposites can be improved by water vapor annealing process. In order to understand the details of the mechanical behaviors of the graphene oxide-based nanocomposites, the finite element analysis models of the graphene oxide-based nanocomposites were established.
Point Cloud Segmentation of Crane Parts Using Dynamic Graph CNN for Crane Collision Avoidance
Hyeonho Jeong,Hyosung Hong,Gyuha Park,Mooncheol Won,Mingyu Kim,Hoyeong Yu 한국정보과학회 2019 Journal of Computing Science and Engineering Vol.13 No.3
In this study, we have developed a point cloud segmentation algorithm for a collision avoidance system between cranes and other objects in construction yards. We used the Dynamic Graph CNN (DGCNN) algorithm to segment the point cloud of the entire yard into crane parts and backgrounds. The point cloud data were obtained from several LIDAR sensors attached to the crane. All points were grouped into specific core clusters using the DBSCAN algorithm. The core clusters were used to train the DGCNN after labeling with corresponding part names. This network classified the point cloud into crane types and their part names. Experimental results show that the crane part segmentation performance of the suggested algorithm is accurate enough to be used for collision avoidance system. It is possible to estimate the pose of a crane by comparing the segmented point clouds with those of the CAD model.
Railroad Surface Defect Segmentation Using a Modified Fully Convolutional Network
( Hyeonho Kim ),( Suchul Lee ),( Seokmin Han ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.12
This research aims to develop a deep learning-based method that automatically detects and segments the defects on railroad surfaces to reduce the cost of visual inspection of the railroad. We developed our segmentation model by modifying a fully convolutional network model [1], a well-known segmentation model used for machine learning, to detect and segment railroad surface defects. The data used in this research are images of the railroad surface with one or more defect regions. Railroad images were cropped to a suitable size, considering the long height and relatively narrow width of the images. They were also normalized based on the variance and mean of the data images. Using these images, the suggested model was trained to segment the defect regions. The proposed method showed promising results in the segmentation of defects. We consider that the proposed method can facilitate decision-making about railroad maintenance, and potentially be applied for other analyses.