• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.55 no.4
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• pp.378-391
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• 2019

The objective of this study is to establish a direction for smart aquaculture technology development in the Republic of Korea through patent analysis and a demand survey of experts and fishermen. The patent analysis was conducted using Wisdomain for patents in the Republic of Korea, the United States of America, Europe, Japan, and China from 2005 to 2016. This study conducted an analytic hierarchy process (AHP) survey of experts in the fields of fishery, marine, and ICT among others. Furthermore, it carried out a demand survey of 85 fishermen in Jeonnam and Jeju. The smart aquaculture technology market has moderately grown in the Republic of Korea until recently, and it is expected to expand further because of the expansion of national investment in the smart aquaculture field. The priority evaluation results for developing smart aquaculture technology show that land-based aquaculture has a higher priority than sea-based aquaculture. Of the fishermen that responded, 84% said that they need to introduce smart aquaculture technology to solve problems in the supply and demand of manpower, labor cost, and maintenance expenses. The direction of development should lie in developing biological and environment-based standard aquaculture models to spread high-tech systems and vitalize the aquaculture industry. This requires continual training of human resources in the smart aquaculture field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3083-3098
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• 2023

As demands for efficient and echo-friendly production of marine products increase, smart aquaculture based on information and communication technology (ICT) has become a promising trend. The smart aquaculture is expected to control fundamental farm environment variables including water temperature and dissolved oxygen (DO) levels with less human intervention. A recirculating aquaculture system (RAS) is required for the smart aquaculture which utilizes a purification tank to reuse water drained from the water tank while blocking the external environment. Elaborate water treatment should be considered to properly operate RAS. However, analyzing the water treatment performance is a challenging issue because fish farm circumstance continuously changes and recursively affects water fluidity. To handle this issue, we introduce computational fluid dynamics (CFD) aided water treatment analysis including water fluidity and the solid particles removal efficiency. We adopt RAS parameters widely used in the real aquaculture field to better reflect the real situation. The simulation results provide several indicators for users to check performance metrics when planning to select appropriate RAS without actually using it which costs a lot to operate.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.56 no.6
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• pp.759-765
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• 2023

Owing to the increase in the elderly population at aquaculture farm and decrease in the number of aquaculture farmers, the need to improve aquaculture production system is increasing. In addition, asvirtual interactions become new normal after COVID-19 pandemic, the speed at which science and technology such as the internet of things (IoT), information and communications technology (ICT), and artificial intelligence (AI) are applied to each field is accelerating. Efforts are being made to enhance the quality of life of aquaculture farmer and competitiveness of the aquaculture industry by incorporating digital technology. This study analyzed national and global aquaculture technology development and policy trends, smart aquaculture terminology application scenarios, and prior research cases to propose smart aquaculture industrialization models and technology development directions considering technology, economy, and environment. This study can also provide valuable reference for promoting smart and efficient development of aquaculture.

• Electronics and Telecommunications Trends
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• v.36 no.5
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• pp.62-73
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• 2021

A stable protein source is required to support the rapidly increasing global population, and fishery products are a particularly important part of the required protein supply. However, due to continued overfishing, fishery resources are depleted, and the number of fish caught by fishing boats has stagnated. Consequently, the aquaculture industry is becoming increasingly important. Internationally, smart aquaculture technology that minimizes labor and environmental pollution has been established through technological developments supported by large investments in automation and water treatment technology over the last several decades. In the case of Korea, the aquaculture industry has not yet emerged as a labor-intensive primary industry. However, in recent years various attempts have been made to apply ICT technology to aquaculture to overcome these problems. In this study, domestic and foreign technologies and patent trends for smart aquaculture are analyzed. In addition, the current status of the smart aquaculture cluster business that the Ministry of Oceans and Fisheries has been promoting since 2019 to utilize ICT technology in aquaculture is introduced.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.93-105
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• 2023

In order to transform the traditional aquaculture industry, which is dependent on experience, labor-intensive and natural environment, into future intelligent smart aquaculture, digital aquaculture improves aquaculture reproducibility and efficiency of production process through digitization of the aquaculture industry based on ICT equipments, Data analysis and utilization for promoted to increase the acceptability of aquaculturist. Europe's advanced fisheries countries have achieved rapid growth not only in aquaculture technology but also in the aquaculture equipment industry through digitization that combines information and communication technology with aquaculture farms. However, it is not possible to collect aquacultural data in Korea because it has not secured a Korean aquaculture industry for multi-variety, small-scale production and aquaculturists' refusal of reception for digital transformation. Therefore, this study intends to suggest the development direction of digital aquaculture to convert to intelligent smart aquaculture in the future by analyzing trends and critical technology.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.56 no.3
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• pp.265-276
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• 2020

The objective of this study is to develop education programs for cultivating smart aquaculture experts through a education demand survey of industries, high school students, university (graduate) students and field workers. The industry demand analysis was conducted as an in-depth interview on representives from seven companies. Education demand surveys were conducted on 96 students and field workers in the Jeonnam region. Results on the demand survey were analyzed using frequency analysis and cross-analysis. The company representatives responded that they want to participated in internship and retraining programs to proactively secure manpowers with convergence capabilities about smart aquaculture. Seven companies preferred manpowers with basic competencies on ICT (Information and Communications Technologies) or aquaculture. The most respondents in the demand survey want to participate in the education program for experience on advanced technology, self-development and enhancement of work capability. On the other hand, some respondents said that the education is time-consuming and that the education program does not fit their level. Thus, the education program should be developed in a way to minimize the spatial and temporal limitations of education targets and to improve understanding of non-majors by reflecting the demands of human resources in the industrial field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2259-2277
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• 2023

We propose a probabilistic fish growth model for smart aquaculture systems equipped with IoT sensors that monitor the ecological environment. As IoT sensors permeate into smart aquaculture systems, environmental data such as oxygen level and temperature are collected frequently and automatically. However, there still exists data on fish weight, tank allocation, and other factors that are collected less frequently and manually by human workers due to technological limitations. Unlike sensor data, human-collected data are hard to obtain and are prone to poor quality due to missing data and reading errors. In a situation where different types of data are mixed, it becomes challenging to develop an effective fish growth model. This study explores the unique characteristics of such a combined environmental and weight dataset. To address these characteristics, we develop a preprocessing method and a probabilistic fish growth model using mixed data sampling (MIDAS) and overlapping mixtures of Gaussian processes (OMGP). We modify the OMGP to be applicable to prediction by setting a proper prior distribution that utilizes the characteristic that the ratio of fish groups does not significantly change as they grow. We conduct a numerical study using the eel dataset collected from a real smart aquaculture system, which reveals the promising performance of our model.

• Annual Conference of KIPS
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• 2018.10a
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• pp.350-352
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• 2018

In this paper, we propose a smart indoor garden aquaculture system using PIC. In the proposed smart indoor garden aquaculture system, the aquaphonics system circulates the nutrient solution of the aquarium, the filtrate tank, and the flowerpot. The automated control system that controls the state of the nutrient solution through the various sensors and controls the pump and valve is designed. As a result of the verification of the smart indoor garden aquaculture system proposed in this paper, it was confirmed that the environment suitable for the survival of fish and plants was maintained by monitoring water temperature, DO, pH measured in real time. The water level in aquarium and flower pot was controlled through automatic operation of the Electric vale and pump.

• Smart Media Journal
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• v.11 no.2
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• pp.39-52
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• 2022

In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.54 no.5
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• pp.751-760
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• 2021

This study investigated the changes in water quality parameters and microbial colonies when ozone was applied to a semi-recirculating aquaculture system (semi-RAS) for the olive flounder Paralichthys olivaceus (500 g in average weight). Concentrations of ozone-produced oxidants (OPO) in rearing tanks were maintained at 0, 0.014, 0.025 mg/L as Cl₂ for 26 days. Except total ammonia nitrogen, nitrite nitrogen, nitrate nitrogen, phosphate phosphorus, chemical oxygen demand, and total suspended solids decreased significantly with increasing OPO concentration in daily and weekly monitoring (P<0.05). Colony forming unit (CFU) counts of heterotrophic marine bacteria decreased in an OPO concentration-dependent manner. Overall reduction rates of microbial colonies in the treatments were 80% higher than those of the control (P<0.05). During the experiment, the OPO concentration-driven ozonation was reliably practiced without any adverse effects on the animals cultured in semi-RAS. Considering the biohazard, operating cost, and stability of ozonation, an OPO concentration of 0.014 mg/L would be sufficient to control water quality parameters and microbial colonies in a semi-RAS.