• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.5
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• pp.743-754
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• 2022

This paper contains the results of the development and performance evaluation of the environmental data monitoring system for the fish farm. For the hardware development, the analogue sensor is used to collect dissolved oxygen, pH, salinity, and temperature of the fish farm water, and the digital sensor is used for collecting ambient temperature, humidity, and location information via a GPS module to be sent to cloud-based Firebase DB. A set of LoRa transmitters and receivers is used as a communication module to upload the collected data. The data stored in Firebase is retrieved as a graph on a web and mobile application to monitor the environmental data changes in real-time. A notification will be delivered if the collected data is outside the determined optimal value. To evaluate the performance of the developed system, a response time from hardware modules to web and mobile applications is ranging from 6.2 to 6.85 seconds, which indicates satisfactory results.

• Journal of information and communication convergence engineering
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• v.9 no.4
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• pp.475-482
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• 2011

In this research, we have attempted to estimate the water quality of fish farms in terms of parameters such as water temperature, dissolved oxygen, pH, and salinity by employing observational data obtained from a coastal ocean observatory of a national institution located close to the fish farm. We requested and received marine data comprising nine factors including water temperature from Korea Hydrographic and Oceanographic Administration. For verifying our results, we also established an experimental fish farm in which we directly placed the sensor module of an optical mode, YSI-6920V2, used for self-cleaning inside fish tanks and used the data measured and recorded by a environment monitoring system that was communicating serially with the sensor module. We investigated the differences in water temperature and salinity among three areas - Goheung Balpo, Yeosu Odongdo, and the experimental fish farm, Keumho. Water temperature did not exhibit significant differences but there was a difference in salinity (significance <5%). Further, multiple regression analysis was performed to estimate the water quality of the fish farm at Keumho based on the data of Goheung Balpo. The water temperature and dissolved-oxygen estimations had multiple regression linear relationships with coefficients of determination of 98% and 89%, respectively. However, in the case of the pH and salinity estimated using the oceanic environment with nine factors, the adjusted coefficient of determination was very low at less than 10%, and it was therefore difficult to predict the values. We plotted the predicted and measured values by employing the estimated regression equation and found them to fit very well; the values were close to the regression line. We have demonstrated that if statistical model equations that fit well are used, the expense of fish-farm sensor and system installations, maintenances, and repairs, which is a major issue with existing environmental information monitoring systems of marine farming areas, can be reduced, thereby making it easier for fish farmers to monitor aquaculture and mariculture environments.

• Journal of Environmental Impact Assessment
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• v.14 no.3
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• pp.127-134
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• 2005

Recently, aquaculture farm sites have been increased with demand of the expensive fish species and sea food like as seaweed, laver and oyster. Therefore coastal water quality have been deteriorated by organic contamination from marine aquaculture farm sites. For protecting of coastal environment, we need to control the location of aquaculture sites. The purpose of this study is to detect the laver aquaculture sites using multispectral remotely sensed data with autodetection algorithm. In order to detect the aquaculture sites, density slice and contour and vegetation index methods were applied with SPOT and IKONOS data of Shinan area. The marine aquaculture farm sites were extracted by density slice and contour methods with one band digital number(DN) carrying 65% accuracy. However, vegetation index algorithm carried out 75% accuracy using near-infra red and red bands. Extraction of the laver aquaculture site using remotely sensed data will provide the efficient digital map for coastal water management strategies and red tide GIS management system.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.31 no.6
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• pp.908-913
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• 1998

The productivity of land based seawater fish farm has been decreased because of unexpected outbreaks of diseases caused by the contaminated inlet seawater. Sometimes unfiltered/untreated outlet seawater from the land based seawater fish farm has created serious environmental problem. In the needs of treatment systems for the inlet and outlet seawater, the researchers have developed two different systems, The purpose of this study is to design and test two treatment systems, the screen filter for inlet seawater and drum filter for outlet seawater, on the basis of concept of system design and automatization. After developing two systems, an experiment has been conducted with two systems and collected data to improve design and efficiency of the system. In this study, detailed design and efficiency of the system could be improved by the programmable logic controller (PLC).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.178-180
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• 2021

In this paper, we design a system to monitor environmental data in fish farms in real-time and provide machine learning-based prediction services to prevent damage on fish farms caused by changes in the sea environment. The proposed system will install an IoT device module consisting of sensors that can measure hydrogen concentration, salinity, dissolved oxygen, and water temperature, which can be transferred to Cloud DB using LTE or LoRa communication technology and then monitor the real-time condition through a web or mobile application. In addition, it has a function to prepare for changes within the environment of fish farms by applying machine learning-based prediction technology using collected data.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.957-968
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• 2021

To improve the data on reliability of the onshore fish farm water quality monitoring system and operate the system efficiently, the water quality data of the onshore seawater fish farms which are progressing test operation, and the marine environmental information network(Wando tidal station) were compared and analyzed. Furthermore, data validation, data range filters, and data displacement checks were applied to analyze the data in a way that eliminates the data errors in water quality monitoring systems and increases the reliability of measurement data.

• Journal of Internet Computing and Services
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• v.24 no.1
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• pp.61-69
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• 2023

Recently, as the fishery resources are depleted, expectations for productivity improvement by 'rearing fishery' in land farms are greatly rising. In the case of land farms, unlike ocean environments, it is easy to control and manage environmental and breeding factors, and has the advantage of being able to adjust production according to the production plan. On the other hand, unlike in the natural environment, there is a disadvantage in that operation costs may significantly increase due to the artificial management for fish growth. Therefore, profit maximization can be pursued by efficiently operating the farm in accordance with the planned target shipment. In order to operate such an efficient farm and nurture fish, an accurate growth prediction model according to the target fish species is absolutely required. Most of the growth prediction models are mainly numerical results based on statistical analysis using farm data. In this paper, we present a growth prediction model from a stochastic point of view to overcome the difficulties in securing data and the difficulty in providing quantitative expected values for inaccuracies that existing growth prediction models from a statistical point of view may have. For a stochastic approach, modeling is performed by introducing a Gaussian process regression method based on water temperature, which is the most important factor in positive growth. From the corresponding results, it is expected that it will be able to provide reference values for more efficient farm operation by simultaneously providing the average value of the predicted growth value at a specific point in time and the confidence interval for that value.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.6
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• pp.637-644
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• 2010

The purpose of this study is to analyze characteristics of water quality factor scientifically and develop the multiple regression model predicting dissolved oxygen to save periodic replacement costs for dissolved oxygen sensor. Correlation analysis using the environmental data obtained from 2 different land-based fish farms of the Geogeum-do, Geheung-gun coastal area during the periods from November 2008 to January 2009 shows that water temperature was negatively correlated with dissolved oxygen and pH butpH was positively correlated with salinity and dissolved oxygen. The information of Keumho fish farm in 2009 is presented by the tables which are monthly statistics of water quality factors and seasonable difference by the Duncan's post-test. Also we developed multiple regression model predicting dissolved oxygen, the usefulness of which was verified by the comparison graph between estimates and actual observations. The developed regression model shows that seawater temperature and salinity give negative affect to dissolved oxygen while pH gives positive affect to it. Lastly the seawater temperature has much higher explanatory power than pH factor.

• Journal of Fisheries and Marine Sciences Education
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• v.29 no.3
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• pp.785-798
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• 2017

This study was conducted to evaluated the surface(10 variables) and bottom(10 variables) water quality, and sediment(3 variables) in the cage fish farm off Baegyado in Gamak Bay using a multivariate analysis from January 2013 to November 2014. Generally, the environmental data did not show a certain tendency by months during two years investigated. The pairwise simple correlation matrices among variables were also shown. The first four principal components of the surface water in 2013 explain 93% of the total sample variance; the first principal component($z_1$) showed the freshwater inflow and/or precipitation, $z_2$, $z_3$ and $z_4$ related to freshwater inflow and/or precipitation, organic matters and eutrophy, respectively; the first four principal components of the bottom water in 2013 explain 93% of the total sample variance; the $z_1$, $z_2$ and $z_4$ related to freshwater inflow and/or precipitation, and $z_3$ water temperature. In 2014, at the surface water the first three principal components explain 87%; the $z_1$, $z_2$ and $z_3$ related to water temperature, eutrophy and freshwater inflow and/or precipitation, respectively; at the bottom water the first three principal components explain 93%; $z_1$, $z_2$ and $z_3$ related to water temperature, freshwater inflow and/or precipitation and eutrophy. Half of the principal components related to freshwater inflow and/or precipitation.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.25 no.3
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• pp.176-180
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• 1992

Nitrification Is an important facet of water recycling fish culture system, because the toxic cation ammonia is converted to the innocuous anion nitrate. This study was attempted to find the optimal design factor of submerged filter for ammonia removal in water recycling fish culture system. The experimental system was designed submerged filter with corrugated skylight plate, and operated in the fish farm, National Fisheries University of Pusan. When the influent ammonia concentration was about 10mg/l (9.43-13.66mg/l) nitrification rates were tested for the removal of ammonia over a four stage of the hydraulic loadings. The submerged filter removed 76.24, 62.88, 39.09 and $9.20\%$ of the ammonia to hydraulic load of 0.028, 0.037, 0.056 and $0.111m^3/m^2$. day, respectively. We can apply the above data to the material balance on the ammonia concentration in a fish reservoir, and conclude that the maximum allowable ammonia production was 1.52mg/min, and the optimal hydraulic loading was $0.047m^3/m^2$\;\cdot day$, in order to maintain the ammonia concentration below 10mg/l in the fish reservoir.