• Korean Journal of Ecology and Environment
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• v.48 no.1
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• pp.68-76
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• 2015

There is no willow riparian vegetation in Jeju Island, Korea. Instead, a genetically-isolated population of Adina rubella is found in some parts of the riparian system. We describe its syntaxonomy and synecology. A total of 27 phytosociological relev$\acute{e}$s were collected, 11 relev$\acute{e}$s from 91 sites and 16 relev$\acute{e}$s from the previously published relevant materials. Data were analyzed by traditional Braun-Blanquet method and multivariate PCoA (Principal coordinates analysis). New syntaxa are distinguished, Adinion rubellae all. nov. and its type association Tripogono-Adinetum rubellae ass. nov. with two subassociations, typicum and rhododendretosum poukhanensae. Adino-Rhododendretum poukhanensae Itow et al. 1993 was discarded owing to mismatch of syntaxonomy and syngeography of Adina and Rhododendron phytocoena. The alliance Adinion is Jeju's regional and partly ombrotrophic vegetation occurring in pothole and rock crevice where are independent on ground-water table. We also suggest a revised alliance, Rhododendrion poukhanensae Lee 2004 ex. hoc loco in Korean peninsula, as a corresponding syntaxon to Adinion, which completely differs from Phragmito-Salicion. Finally we pointed out that Adina phytocoena requiring an absolutely monitoring has been threatened by river maintenance project of local government.

• The Journal of Bigdata
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• v.8 no.2
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• pp.73-82
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• 2023

With 78% of current fisheries workers being elderly, there's a pressing need to address labor shortages. Consequently, active research on smart aquaculture technologies, centered on object detection and tracking algorithms, is underway. These technologies allow for fish size analysis and behavior pattern forecasting, facilitating the development of real-time monitoring and automated systems. Our study utilized video data from cameras outside aquaculture facilities and implemented fish detection and tracking algorithms. We aimed to tackle high maintenance costs due to underwater conditions and camera corrosion from ammonia and pH levels. We evaluated the performance of a real-time system using YOLOv7 for fish detection and the SORT algorithm for movement tracking. YOLOv7 results demonstrated a trade-off between Recall and Precision, minimizing false detections from lighting, water currents, and shadows. Effective tracking was ascertained through re-identification. This research holds promise for enhancing smart aquaculture's operational efficiency and improving fishery facility management.