• Korean Journal of Fisheries and Aquatic Sciences
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• v.43 no.6
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• pp.693-704
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• 2010

Seasonal variation in species composition around Dokdo, East Sea of Korea, was investigated using trammel-net catches, from 2006 to 2009. A total of 53 fish species belonging to 23 families in 12 orders were captured; the orders Perciformes (12 families, 22 species) and Scorpaeniformes (four families, 22 species) were dominant. Between 2008 and 2009, 43 species were collected by trammel net. The number of species was highest in August 2009 (25 species) and lowest in February 2009 (11 species). The number of individuals and total biomass peaked in November 2009. Diversity indices for fish catches were highest in August 2008 (2.4368) and lowest in November 2009 (0.4253). The dominant species were Thamnaconus modestus and Sebastes schlegeli. Hierarchical clustering analysis showed five fish groups, with frequency and number of individuals similar to results of correspondence analysis (CA), which showed a closer relationship to the year term than the season term. CA showed that temperature was an important factor influencing fish species richness and abundance. Three main fish assemblage types coexisted around Dokdo: an East Sea coastal fish assemblage, a subtropical fish assemblage, and a cold water fish assemblage.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.17-23
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• 2024

Losses in domestic water supply due to leaks are very large, such as fractures and defects in pipelines. Therefore, preventive measures to prevent water leakage are necessary. We propose the development of a leakage detection sensor utilizing vibration sensors and present an optimal leakage detection algorithm leveraging artificial intelligence. Vibrational sound data acquired from water pipelines undergo a preprocessing stage using FFT (Fast Fourier Transform), followed by leakage classification using an optimized tree-based boosting algorithm. Applying this method to approximately 260,000 experimental data points from various real-world scenarios resulted in a 97% accuracy, a 4% improvement over existing SVM(Support Vector Machine) methods. The processing speed also increased approximately 80 times, confirming its suitability for edge device applications.