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유독플랑크톤(와편모조류(渦鞭毛藻類)를 중심으로) 에 관한 고찰
한명수 한국수산학회 1990 한국수산과학회지 Vol.23 No.1
Some species of dinoflagellates were considered as one of the causative organisms of PSP(Paralytic Shellfish Poison) or DSP(Diarrhetic Shellfish Poison). Fish and shellfish are intoxicated by feeding of toxic plankton, sometimes human is intoxicated by feeding on these intoxicated fish and shellfish. In past ten years, the physiological and ecological studies of the toxic plankton has been investigated for development of monitoring system and preventation and control measures of PSP. However, in our country still little is known on a research for the toxic dinoflagellates. This paper reviews the general biology, taxonomic problem, physioecology and culture method of the toxic planktons such as Protogonyaulax and Dinophysis.
한명수,박성은,최영진,김영민,황재동 해양환경안전학회 2020 해양환경안전학회지 Vol.26 No.4
In this study, we used artificial intelligence algorithms for the prediction of dissolved oxygen in Jindong Bay. To determine missing values in the observational data, we used the Bidirectional Recurrent Imputation for Time Series (BRITS) deep learning algorithm, Auto-Regressive Integrated Moving Average (ARIMA), a widely used time series analysis method, and the Long Short-Term Memory (LSTM) deep learning method were used to predict the dissolved oxygen. We also compared accuracy of ARIMA and LSTM. The missing values were determined with high accuracy by BRITS in the surface layer; however, the accuracy was low in the lower layers. The accuracy of BRITS was unstable due to the experimental conditions in the middle layer. In the middle and bottom layers, the LSTM model showed higher accuracy than the ARIMA model, whereas the ARIMA model showed superior performance in the surface layer. 본 연구에서는 인공지능기법을 이용하여 진동만의 용존산소량 예측을 하였다. 관측자료에 존재하는 결측 구간을 보간하기 위해 양방향재귀신경망(BRITS, Bidirectional Recurrent Imputation for Time Series) 딥러닝 알고리즘을 이용하였고, 대표적 시계열 예측 선형모델인 ARIMA(Auto-Regressive Integrated Moving Average)과 비선형모델 중 가장 많이 이용되고 있는 LSTM(Long Short-Term Memory) 모델을 이용하여 진동만의 용존산소량을 예측하고 그 성능을 평가했다. 결측 구간 보정 실험은 표층에서 높은 정확도로 보정이 가능했으나, 저층에서는 그 정확도가 낮았으며, 중층에서는 실험조건에 따라 정확도가 불안정하게 나타났다. 실험조건에 따라 정확도가 불안정하게 나타났다. 결과로부터 LSTM 모델이 중층과 저층에서 ARIMA 모델보다 우세한 정확도를 보였으나, 표층에서는 ARIMA모델의 정확도가 약간 높은 것으로 나타났다.