• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.6
• /
• pp.733-739
• /
• 2019

Water level measurement is highly demanding in IoT monitoring areas such as smart factory, smart farm, and smart fish farm. However, existing water level indicators are limited to be used in industrial fields as commercial products due to the high cost of sensors and the complexity of algorithms used. In order to solve these problems, our paper proposed methods using an infrared distance sensor as well as a hall sensor for the water level measurement, both of which are contactless smart sensors. Data errors caused by the inaccuracy of existing sensors were decreased by applying new simple structures so that versatility is enhanced. The performance of our method was validated using experiments based on simulations. We expect that our new water depth indicator can be extended to a general-purpose water level monitoring system based on IoT technology.

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

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.3
• /
• pp.177-183
• /
• 2022

To measure the size and weight of the fish, we developed an automatic fish size measurement system using a deep neural network, where the YOLO (You Only Look Once)v3 model was used. To detect fish, an IP camera with infrared function was installed over the fish pool to acquire image data and used as input data for the deep neural network. Using the bounding box information generated as a result of detecting the fish and the structure for which the actual length is known, the size of the fish can be obtained. A GUI (Graphical User Interface) program was implemented using LabVIEW and RTSP (Real-Time Streaming protocol). The automatic fish size measurement system shows the results and stores them in a database for future work.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.11a
• /
• pp.266-268
• /
• 2020

본 시스템은 사용자에게 수질, 수온, 산소량 측정 및 AI 자동 먹이 공급 그리고 불법 어선감지 시스템을 제공한다. 이는 사용자의 사유재산 보호, 효율적인 운영을 통한 경제성 향상 등 양식장 환경 개선을 목적으로 한다.

• Journal of Internet of Things and Convergence
• /
• v.9 no.1
• /
• pp.41-47
• /
• 2023

Today, due to the increase in global population growth, the international community is discussing solving the food problem. The aquaculture industry is emerging as an alternative to solving the food problem. For the innovative growth of the aquaculture industry, smart fish farms that combine the fourth industrial technology are recently being distributed, and full-cycle digitalization is being promoted. Water quality sensors, which are important in the aquaculture industry, are electrochemical portable sensors that check water quality individually and intermittently, making it impossible to analyze and manage water quality in real time. Recently, optically-based monitoring sensors have been developed and applied, but the reliability of monitoring data cannot be guaranteed because the state information of the water quality sensor is unknown. Therefore, this paper proposes an algorithm representing self-diagnosis status such as Failure, Out of Specification, Maintenance Required, and Check Function based on monitoring data collected by water quality sensors to ensure data reliability.

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

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.3
• /
• pp.371-378
• /
• 2016

We have studied on the maritime navigation with functions of navigation route search and maritime safety information for small-size fishery ships, fishing boats, yachts, and boats in coastal seas. We have designed a smart phone application which is more exact information on marine obstacles of islands, aquafarms, and rocks by using land navigation techniques. Our navigation system is relatively easy to be used for costal navigation and may be a substitute for expensive ECDIS(Electronic Chart Display and Information System). Our system consists of maritime navigation databases to manage necessary maritime data and a mobile application to support maritime navigation for operators. We believe that our research findings can improve safety navigation in coastal seas by supporting lower cost, no time-consuming installation and maintenance, and easy-to-use operations.

• Smart Media Journal
• /
• v.5 no.1
• /
• pp.130-136
• /
• 2016

In this paper, the real time monitoring system for the abnormal state of marine algae does not detect the plankton which may directly cause the red tide or the water bloom. But checks both oxygen reduction and nitrogen reduction in water, which indicates the characteristics of zooplankton and phytoplankton respectively, and this system makes a module that monitors in real time the temperature and the illumination of the water, which are indirect factors, with sensors placed in and outside the water, and this module transmits signals periodically at specific intervals to a sever that builds up data base, and the data collected in these ways will be analyzed and compared with the standard data from Ministry of Oceans and Fisheries, and then these data will be made the adequate form of information to be provided to the users as visual information, thus, this system intends to make a red tide and water bloom monitoring system tailored for individual fish farm businesses that has local characteristics and can quickly operate outside working hours, which differs from the existing wide area detecting and monitoring systems.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.16 no.6
• /
• pp.299-305
• /
• 2021

Dissolved oxygen, pH, and temperature are the most important factors for fish farming because they affect fish growth and mass mortality of the fish. Therefore, fish farm workers must always check all pools on the farm, but this is very difficult in reality. That's why we developed a control system for smart fish farms. This system includes a gateway, sensor gatherers, and a PC program using LabVIEW. One sensor gatherer can cover up to four pools. The sensor gatherers are connected to the gateway in the form of a bus. For the gateway, the ATmega2560 is used as the main processor for communication and the STM32F429 is used as a sub-processor for displaying LCD. For the sensor gatherer, ATmega2560 is used as the main processor for communication. MQTT (Message Queuing Telemetry Transport), RS-485, and Zigbee are used as the communication protocols in the control system. The users can control the temperature and the dissolved oxygen using the PC program. The commands are transferred from the PC program to the gateway through the MQTT protocol. When the gateway gets the commands, it transfers the commands to the appropriate sensor gatherer through RS-485 and Zigbee.

• Journal of the Korean Society of Fisheries and Ocean Technology
• /
• v.58 no.1
• /
• pp.59-67
• /
• 2022

This study assessed the levels of water qualities and microbials contamination of inland olive flounder farms in Jeju in the summers from 2015 to 2017. Three farms (A-C) located in a concentrated area using mixing coastal seawater and underground seawater and one farm (D) located in an independent area using only coastal seawater were selected. Total ammonia nitrogen (TAN) reached a maximum of 0.898 ± 1.024 mg/L as N in the coastal seawater of A-C, which was close to the limit of the water quality management goal of the fish farm. TAN in the influent from A-C was up to three times higher than that of D, so that the discharged water did not spread to a wide range area along the coast and continued to affect the influent. TAN of the effluent in A-C increased by 2.7-4.6 times compared to the influent, resulting in serious self-pollution in the flounder farm. Heterotrophic marine bacteria in the influent of A-C was about 600 times higher than D, and the discharge of A-C was increased by about 30 times compared to the influent.