• Proceedings of the Korea Water Resources Association Conference
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• 2005.09b
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• pp.930-931
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• 2005

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.3
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• pp.125-151
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• 2018

We grouped the names attributed to the seas surrounding the Korean Peninsula in maps published in two major Korean ocean and fisheries science journals over the period from 1998 to 2017: the Journal of the Korean Society of Oceanography (The Sea) and the Korean Journal of Fisheries and Aquatic Science (KFAS). The names attributed to these seas in maps of journal paper broadly were classified into three groupings: (1) East Sea and Yellow Sea; (2) East Sea, Yellow Sea, and South Sea; or (3) East Sea, West Sea and South Sea. The name 'East Sea' was dominantly used for the waters between Korea and Japan. In contrast, the water between Korea and China has been mostly labelled as 'Yellow Sea' but sometimes labelled as 'West Sea'. The waters between the south coast of Korea and Kyushu, Japan were labelled as either 'Korea Strait' or 'South Sea'. This analysis on sea names in the maps of 'The Sea' and 'KFAS' reveals that domestic researchers frequently mix geographical and international names when referring to the waters surrounding the Korean Peninsula. These inconsistencies provide the motivation for the development of a basic unifying guideline for naming the seas surrounding the Korean Peninsula. With respect to this, we recommend the use of separate names for the marginal seas between continental landmasses and/or islands versus for the coastal waters surrounding Korea. For the marginal seas, the internationally recognized names are recommended to be used: East Sea; Yellow Sea; Korea Strait; and East China Sea. While for coastal seas, including Korea's territorial sea, the following geographical nomenclature is suggested to differentiate them from the marginal sea names: Coastal Sea off the East Coast of Korea (or the East Korea Coastal Zone), Coastal Sea off the South Coast of Korea (or the South Coastal Zone of Korea), and Coastal Sea off the West Coast of Korea (or the West Korea Coastal Zone). Further, for small or specific study areas, the local region names, district names, the sea names and the undersea feature names can be used on the maps.

• Ocean and Polar Research
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• v.25 no.1
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• pp.41-52
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• 2003

Sediment trap samples were collected to find out characteristic behaviors of metals in the settling particles by using time-series sediment traps at 678m and 1678m water depths in the Bransfield Strait from December 27th, 1999 to December 26th, 2000. Total mass fluxes at the intermediate water depth (678m water depth) were high in the austral summer and low in the austral winter, whereas at the deep water depth (1678m water depth) they showed high values in both the summer and winter. Total mass fluxes were generally higher in the deep water depth than in the intermediate water depth, which indicates that a substantial amount of sediments are laterally transported by strong currents into the deep basin from the shallow water depths. Aluminium contents also showed large seasonal variations with high values in the winter and low values in the summer. On the contrary, organic carbon contents were high in the summer and low in the winter. Al contents were negatively correlated with organic carbon contents, which may be ascribed that detrital particles are diluted by organic matter produced by phytoplankton in the surface waters. Metals measured in this study exhibited three characteristic behaviors; 1) a positive correlation with Al-Ti, Fe, Mn, V, Co, and Ba, 2) a negative correlation with Al-Cd and Zn, 3) no relationship with Al-Sr, Cu, Cr, Ni. Terrestrial materials may act as a major source fer metals that are positively correlated with Al, and organic matter may be a major source for metals that are negatively correlated with Al. Enrichment factor (EF) of Fe, Mn, Ba, Vi Co, Sr, Cr, and Ni ranged from 0.5 to 1.5, whereas EF of Zn, Cu, and Cd showed much higher values than 1.

• Ocean and Polar Research
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• v.31 no.2
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• pp.177-187
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• 2009

This study examined the multi-scale variabilities of sea surface temperature (SST) and salinity in the Japan/East Sea (JES) based on statistical analyses of observational data, with a focus on the northwestern part of the sea. The regionality of JES SST variability was estimated for different frequency ranges on semimonthly (11-17 days), monthly to seasonal (30-90 days), quasi-semiannual (157-220 days), and quasi-biennial (1.5-3 years) time scales using cluster analyses of daily gridded SST data for 1996 to 2007 from the Japan Meteorological Agency (JMA). Several significant peaks and regional cores were found in each frequency range of the SST anomaly (SSTA) oscillations. Quasi-semiannual SSTA oscillations with high amplitude were found in the south-southwestern part of the Japan Basin ($41-43^{\circ}N$) and were amplified in the area adjacent to Peter the Great Bay. Oscillations with periods of 79 and 55 days also prevailed over the southwest Japan Basin between the Yamato Rise and the continental slope. A similar method was applied to classify SST and the annual cycle of surface salinity using Generalized Digital Environmental Model (GDEM) gridded data. The Tatarskii Strait and adjacent area showed the most specific annual cycles and variability in salinity on interannual to interdecadal time scales. The most significant inverse relationship between surface salinity in the Tatarskii Strait and southern JES areas was found on the interdecadal time scale. Linkages of sea water salinity in the Tatarskii Strait with Amur River discharge and wind velocity over Amurskii Liman were also revealed.

• Parasites, Hosts and Diseases
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• v.52 no.1
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• pp.9-19
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• 2014

Anisakis spp. (Nematoda: Anisakidae) parasitize a wide range of marine animals, mammals serving as the definitive host and different fish species as intermediate or paratenic hosts. In this study, 18 fish species were investigated for Anisakis infection. Katsuwonus pelamis, Euthynnus affinis, Caranx sp., and Auxis thazard were infected with high prevalence of Anisakis type I, while Cephalopholis cyanostigma and Rastrelliger kanagurta revealed low prevalence. The mean intensity of Anisakis larvae in K. pelamis and A. thazard was 49.7 and 5.6, respectively. A total of 73 Anisakis type I larvae collected from K. pelamis and A. thazard were all identified as Anisakis typica by PCR-RFLP analysis. Five specimens of Anisakis from K. pelamis and 15 specimens from A. thazard were sequenced using ITS1-5.8S-ITS2 region and 6 specimens from A. thazard and 4 specimens from K. pelamis were sequenced in mtDNA cox2 region. Alignments of the samples in the ITS region showed 2 patterns of nucleotides. The first pattern (genotype) of Anisakis from A. thazard had 100% similarity with adult A. typica from dolphins from USA, whereas the second genotype from A. thazard and K. pelamis had 4 base pairs different in ITS1 region with adult A. typica from USA. In the mtDNA cox2 regions, Anisakis type I specimens from A. thazard and K. pelamis showed similarity range from 94% to 99% with A. typica AB517571/DQ116427. The difference of 4 bp nucleotides in ITS1 regions and divergence into 2 subgroups in mtDNA cox2 indicating the existence of A. typica sibling species in the Makassar Strait.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.24 no.2
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• pp.159-165
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• 2006

This paper deals with the estimation of geostrophic current using the sea surface topography calculated from the geoidal height from EGM96 geopotential model and the mean sea surface height from CLS_SHOM mean sea surface model. The CLS_SHOM model was developed using the altimetry data set. The estimation of geostrophic current is available in the characteristic research of ocean in many country, while for East Sea a few studies were done. The goal of this study is basically to provide the characteristics of geostrophic current in East Sea. The results show that the mean sea surface topography (SST) in East Sea is about 0.37 m and the mean geostrophic velocity is -0.028 m/sec. The Pacific water enters into the East Sea through the Korea Strait and after passing the strait, this inflow splits into two branches: one flows northward along the Korean coast and another outflows into Pacific ocean through Tsugaru and Soya strait passing the east-northeastward along the Japanese outer shelf, and outflows into Okhotsk ocean.

• Ocean and Polar Research
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• v.30 no.2
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• pp.193-205
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• 2008

A model of the current and seasonal volume transport in the East China Sea was used to investigate the origin of the Tsushima Warm Current (TSWC). The modeled volume transport field suggested that the current field west of Kyushu ($30^{\circ}-32^{\circ}N$) was divided into two regions, R1 and R2, according to the bottom depth. R1 consisted of the Taiwan Warm Current (TWWC) region and the mixed Kuroshio-TWWC (MKT) water region, while R2 was the modified Kuroshio water (MKW) region west of Kyushu. The MKW branched from the Kuroshio and flowed into the Korea/Tsushima Straits through the Cheju-Kyushu Strait, contributing 41% of the annual mean volume transport of the TSWC. The TWWC and MKT water flowed into the Korea/Tsushima Straits through the Cheju-Kyushu and Cheju Straits, contributing 32% and 27% of the volume transport, respectively. The maximum volume transport of the MKW was 53% of the total volume transport of the TSWC in November, while the maximum volume transport of the water in the R1 region through the Cheju-Kyushu Strait was 41% in July. Hence, there were two peaks per year of volume transport in the TSWC.

• Journal of Ocean Engineering and Technology
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• v.35 no.5
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• pp.336-346
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• 2021

The prediction of wave conditions is crucial in the field of marine and ocean engineering. Hence, this study aims to predict the significant wave height through machine learning (ML), a soft computing method. The adopted metocean data, collected from 2012 to 2020, were obtained from the Korea Institute of Ocean Science and Technology. We adopted the feedforward neural network (FNN) and long-short term memory (LSTM) models to predict significant wave height. Input parameters for the input layer were selected by Pearson correlation coefficients. To obtain the optimized hyperparameter, we conducted a sensitivity study on the window size, node, layer, and activation function. Finally, the significant wave height was predicted using the FNN and LSTM models, by varying the three input parameters and three window sizes. Accordingly, FNN (W48) (i.e., FNN with window size 48) and LSTM (W48) (i.e., LSTM with window size 48) were superior outcomes. The most suitable model for predicting the significant wave height was FNN(W48) owing to its accuracy and calculation time. If the metocean data were further accumulated, the accuracy of the ML model would have improved, and it will be beneficial to predict added resistance by waves when conducting a sea trial test.

• Proceedings of the Korean Society of Coastal and Ocean Engineers Conference
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• 1994.07a
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• pp.111-115
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• 1994

• Korean Journal of Fisheries and Aquatic Sciences
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• v.51 no.2
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• pp.187-198
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• 2018

The monthly salinity maps from Aquarius satellite covering the entire East Sea were produced to analyze the low-salinity water appearing in fall every year. The low-salinity water in the northern East Sea began to appear in May-June, spreading southward along the coast and eastward north of the subpolar front. Low-salinity water from the East China Sea entered the East Sea through the Korea Strait from July to September and was mixed with low-salinity water from the northern East Sea in the Ulleung Basin. The strength of the low-salinity water from the East China Sea was dependent on the strength of the southerly wind of the East China Sea in July-August. The salinity reaches a minimum in September with a distribution parallel to the latitude of $37.5^{\circ}N$. In October, low salinity water is distributed along the mean current path and subpolar front and the entire East Sea is covered with the low salinity water in November. Water with salinity larger than 34 psu starts to flow into the East Sea through the Korea Strait in December and it expands gradually northward up to the subpolar front in January- February.