• The Journal of the Korea Contents Association
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• v.20 no.10
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• pp.167-176
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• 2020

The automated fish feed system currently used in Korea supplies a certain amounts of feed to water tanks at a certain time. This automated system can reduce the labor cost of managing aqua farms, but it is very difficult to control intelligently and appropriately the amount of expensive feed that is critical to aqua farm productivity. In this paper, we propose the FIIFF Inference Model( Fuzzy Inference-based Intelligent Fish Feeding Model) that can solves the problems of these existing automatic fish feeding devices and maximizes the efficiency of feed supply while properly maintaining the growth rate of fish in aqua farms. The proposed FIIFF inference model has the advantage of being able to control feed amounts appropriately since it computes the amount of feed using the current water environments and fish activity state of the aqua farms. The result of the feed amount yield experiment with the proposed FIIFF Inference Model represents the effect of saving 14.8% over the eight months of actual feed amount in the aqua farm.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.552-560
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• 2021

Currently, the domestic aquaculture industry is pursuing smartization, but it is still proceeding with human subjective judgment in many processes in the aquaculture stage. The prerequisite for the smart aquaculture industry is to effectively grasp the condition of fish in the farm. If real-time monitoring is possible by identifying the number of fish populations, size, pathways, and speed of movement, various forms of automation such as automatic feed supply and disease determination can be carried out. In this study, we proposed an algorithm to identify the state of fish in real time using underwater video data. The fish detection performance was compared and evaluated by applying the latest deep learning-based object detection models, and an algorithm was proposed to measure fish object identification, path tracking, and moving speed in continuous image frames in the video using the fish detection results. The proposed algorithm showed 92% object detection performance (based on F1-score), and it was confirmed that it effectively tracks a large number of fish objects in real time on the actual test video. It is expected that the algorithm proposed in this paper can be effectively used in various smart farming technologies such as automatic feed feeding and fish disease prediction in the future.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.36 no.2
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• pp.143-150
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• 2023

Underwater wireless communication is a challenging issue for realizing the smart aqua-farm and various marine activities for exploring the ocean and environmental monitoring. In comparison to acoustic and radio frequency technologies, the visible light communication is the most promising method to transmit data with a higher speed in complex underwater environments. To send data at a speedier rate, high-performance photodetectors are essentially required to receive blue and/or cyan-blue light that are transmitted from the light sources in a light-fidelity (Li-Fi) system. Here, we fabricated high-performance organic phototransistors (OPTs) based on P-type donor polymer (PTO2) and N-type acceptor small molecule (IT-4F) blend semiconductors. Bulk-heterojunction (BHJ) PTO2:IT-4F photo-active layer has a broad absorption spectrum in the range of 450~550 nm wavelength. Solution-processed OPTs showed a high photo-responsivity >1,000 mA/W, a large photo-sensitivity >10³, a fast response time, and reproducible light-On/Off switching characteristics even under a weak incident light. BHJ organic semiconductors absorbed photons and generated excitons, and efficiently dissociated to electron and hole carriers at the donor-acceptor interface. Printed and flexible OPTs can be widely used as Li-Fi receivers and image sensors for underwater communication and underwater internet of things (UIoTs).