• Journal of Aquaculture
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• v.9 no.3
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• pp.239-245
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• 1996

As one of the red tide control method, montmorillonite was used to eliminate the causative organisms in Korea and Japan. We assayed the loess to replace the montmorillonite because it distribute in large quantity and nearby the red tide occurrence in South Coast of Korea. By using the mixture of loess and coal ashes, we examined the decreasing level of nutrients such as ammonia and phosphate, the elimination of causative organisms as a chlorophyll a content, and the harmful effect on aquaculture orgarnisms in cage culture farms. Half of the ammonium and phosphate was adsorbed by the loess particles, but only $25\%$ of ammonium was adsorbed by the coal ashes particles. In water column test, the particles of loess and coal ashes were settled down by $80\%$ in 20 minutes, the red tide organisms was eliminated by $80\%$ after 2 hours in 1,000 ppm of loess suspension, but the organisms were eliminated only $30\%$ by the same concentration of coal ashes. The harmful test of fishes and invertbrates, we observed any other negative effects of test animals than a tint deceleration in yellowtail.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.128-130
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• 2021

This study aims to implement a Regression Analysis system for predicting the appropriate production of flatfish. Due to Korea's signing of FTAs with countries around the world and accelerating market opening, Korean flatfish farming businesses are experiencing many difficulties due to the specificity and uncertainty of the environment. In addition, there is a need for a solution to problems such as sluggish consumption and price drop due to the recent surge in imported seafood such as salmon and yellowtail and changes in people's dietary habits. in this study, Using the python module, xlwings, it was used to obtain for the production amount of flatfish and to predict the amount of flatfish to be produced later. was used to predict the amount of flatfish to be produced in the future. Therefore, based on the analysis results of this prediction of flatfish production, the flatfish aquaculture industry will be able to come up with a plan to achieve an appropriate production volume and control supply and demand, which will reduce unnecessary economic loss and promote new value creation based on data. In addition, through the data approach attempted in this study, various analysis techniques such as artificial neural networks and multiple regression analysis can be used in future research in various fields, which will become the foundation of basic data that can effectively analyze and utilize big data in various industries.