scholarly journals Ichthyotoxic Cochlodinium polykrikoides red tides offshore in the South Sea, Korea in 2014: III. Metazooplankton and their grazing impacts on red-tide organisms and heterotrophic protists

ALGAE ◽  
2017 ◽  
Vol 32 (4) ◽  
pp. 285-308 ◽  
Author(s):  
Moo Joon Lee ◽  
Hae Jin Jeong ◽  
Jae Seong Kim ◽  
Keon Kang Jang ◽  
Nam Seon Kang ◽  
...  
ALGAE ◽  
2017 ◽  
Vol 32 (3) ◽  
pp. 199-222 ◽  
Author(s):  
An Suk Lim ◽  
Hae Jin Jeong ◽  
Kyeong Ah Seong ◽  
Moo Joon Lee ◽  
Nam Seon Kang ◽  
...  

Harmful Algae ◽  
2015 ◽  
Vol 45 ◽  
pp. 26-32 ◽  
Author(s):  
An Suk Lim ◽  
Hae Jin Jeong ◽  
Tae Young Jang ◽  
Nam Seon Kang ◽  
Se Hyeon Jang ◽  
...  

2011 ◽  
Vol 46 (4) ◽  
pp. 239-263 ◽  
Author(s):  
Young Baek Son ◽  
Joji Ishizaka ◽  
Jong-Chul Jeong ◽  
Hyun-Choel Kim ◽  
Taehee Lee

2021 ◽  
Vol 10 (1) ◽  
pp. 31
Author(s):  
Youngjin Choi ◽  
Youngmin Park ◽  
Weol-Ae Lim ◽  
Seung-Hwan Min ◽  
Joon-Soo Lee

In this study, the occurrence of Cochlodinium polykrikoides bloom was predicted based on spatial information. The South Sea of Korea (SSK), where C. polykrikoides bloom occurs every year, was divided into three concentrated areas. For each domain, the optimal model configuration was determined by designing a verification experiment with 1–3 convolutional neural network (CNN) layers and 50–300 training times. Finally, we predicted the occurrence of C. polykrikoides bloom based on 3 CNN layers and 300 training times that showed the best results. The experimental results for the three areas showed that the average pixel accuracy was 96.22%, mean accuracy was 91.55%, mean IU was 81.5%, and frequency weighted IU was 84.57%, all of which showed above 80% prediction accuracy, indicating the achievement of appropriate performance. Our results show that the occurrence of C. polykrikoides bloom can be derived from atmosphere and ocean forecast information.


ALGAE ◽  
2021 ◽  
Vol 36 (1) ◽  
pp. 25-36
Author(s):  
Se Hee Eom ◽  
Hae Jin Jeong ◽  
Jin Hee Ok ◽  
Sang Ah Park ◽  
Hee Chang Kang ◽  
...  
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