• Journal of Multimedia Information System
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• v.5 no.3
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• pp.215-220
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• 2018

This work presents a smart water quality sensor for hydroponic plant growing applications. The proposed sensor can effectively measure pH level and electrical conductivity of the water solution. The micro-controller incorporated in the sensor processes the raw sensor data, and converts it into a readable format. In addition, through the mobile interface realized using a WiFi module, the sensor can send data to the web server database that collects and stores the data. The data stored in the web server can be accessed by a personal computer or smart phone. The prototype sensor has been implemented, and the operations have been verified under an actual hydroponic plant growing application.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.5
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• pp.983-990
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• 2024

Water quality management is an important element of aquaculture, and digitization of water quality information can be said to be the starting point of smart aquaculture. The complex water quality sensor is an IoT device that automatically measures arrow items such as pH, DO, water temperature, and salinity. However, due to the high price of the water quality sensor, it is economically burdensome to apply it to all aquaculture tanks, and it requires specialized knowledge to maintain them, making it difficult for small-scale aquaculture providers to apply them. In this paper, we propose a multi-channel complex water quality sensor to solve this problem. The proposed system is a device that measures the water quality of multiple tanks with a single sensor through valve control with a pump, thereby reducing sensor purchase costs for fish farms. In addition, we aimed to extend the service life of the sensor and improve the convenience of management by providing an automatic cleaning function using fresh water after measurement.

• Smart Media Journal
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• v.11 no.9
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• pp.56-63
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• 2022

Recently, the frequency and range of algae occurrence in major rivers and lakes are increasing due to the increase in water temperature due to climate change, the inflow of excessive nutrients, and changes in the river environment. Abnormal algae include green algae and red algae. Green algae is a phenomenon in which blue-green algae such as chlorophyll (Chl-a) in the water grow excessively and the color of the water changes to dark green. In this study, a 3D virtual world of digital twin was built to monitor and control water quality information measured in ecological rivers and lakes in the living environment in real time from a remote location, and a sensor measuring device for water quality information based on the Internet of Things (IOT) sensor. We propose to build a smart water environment service platform that can provide algae warning and water quality forecasting by predicting the causes and spread patterns of water pollution such as algae based on AI machine learning-based collected data analysis.

• International journal of advanced smart convergence
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• v.5 no.2
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• pp.73-79
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• 2016

Remote sensing and measurement are of paramount importance of providing information on the state of water quality in water bodies. The formation and growth of cyanobacteria is of serious concern to in land aquatic life forms and human life. The main cause of water quality deterioration stems from anthropogenic induced eutrophication. The goal of this research to quantify and determine the spatial distribution of cyanobacteria concentration in the water using remote sensing technique. The standard approach to measure water quality based on the direct measurement of the fluorescence of the chlorophyll a in the living algal cells and the same approach used to detect the phycobilin pigments found in blue-green algae (a.k.a. cyanobacteria), phycocyanin and phycoerythrin. This paper propose the emerging sensor design to measure the water quality based on the optical analysis by fluorescence of the phycocyanin pigment. In this research, we developed an method to sense and quantify to derive phycocyanin intensity index for estimating cyanobacteria concentrations. The development of the index was based on the reflectance difference between visible light band 620nm and 665nm. As a result of research this paper presents, an optical biological sensor design information to measure the Phycocyanin parameters in water content.

• Journal of Korean Society of Water and Wastewater
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• v.38 no.1
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• pp.1-15
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• 2024

Occurrence of process environment changes, such as influent load variances and process condition changes, can reduce treatment efficiency, increasing effluent water quality. In order to prevent exceeding effluent standards, it is necessary to manage effluent water quality based on process operation data including influent and process condition before exceeding occur. Accordingly, the development of the effluent water quality prediction system and the application of technology to wastewater treatment processes are getting attention. Therefore, in this study, through the multi-channel measuring instruments in the bio-reactor and smart multi-item water quality sensors (location in bio-reactor influent/effluent) were installed in The Seonam water recycling center #2 treatment plant series 3, it was collected water quality data centering around COD, T-N. Using the collected data, the artificial intelligence-based effluent quality prediction model was developed, and relative errors were compared with effluent TMS measurement data. Through relative error comparison, the applicability of the artificial intelligence-based effluent water quality prediction model in wastewater treatment process was reviewed.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.723-728
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• 2019

Citizens' distrust of water pollution is very high in tap water that we routinely drink. In addition, water pollution accidents of tap water are difficult to predict and the risk is high, so real-time monitoring and management are needed. Therefore, it is necessary to introduce real-time water quality monitoring using the Internet of things(IoT). Residual chlorine is more persistent and economical than other disinfectants and it is easy to check residual effect, so it is mainly used as a disinfection index in waterworks. It can be monitored in real time by using IoT technology in order to secure the safety of tap water. In this study, we developed smart device for real-time water quality monitoring using amperometry sensor and analyzed its performance.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.2
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• pp.27-35
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• 2022

Based on the current status of groundwater usage in the dry season through field surveys, this study tried to suggest countermeasures to reduce groundwater usage and to improve the water quality of baseflow from agricultural fields. For this purposes, basins with water curtain cultivation preceded were targeted where decreases of groundwater due to continuous use of groundwater in spring and winter annually observed. From monitoring groudwater usage of the study watershed, 130,058, 130,105 m³/day of water was pumped in during the water curtain cultivation period (October-February) in the Shindun, Seokwon watershed respectively. And the pilot application of the smart automated sensor-based water curtain cultivation system (smart WC system) developed in this study to reduce groundwater consumption has been conducted. As a result, the efficiency of the smart WC system when threshold temperature is set as 6.3 ℃ was 21.1% compared to conventional cultivation and efficiency increased as threshold temperature gets lower. Lastly, in this study, culvert drainage and Bio-filters were installed and rainfall monitoring was performed 15 times in order to analyze the baseflow securement and pollutant loads behavior. As a result, the test-bed with culvert drainage and Bio-filter installed together generated 61.4% more baseflow (4.974 m³) than the test-bed with only culvert drainage was installed (3.056 m³). However, the total pollutant load of all water quality contents (BOD, COD, T-N, TOC) except for the SS and T-P was found to be greater in the culvert drain and Bio-filter installed than in the culvert drain test-bed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.717-722
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• 2013

The wastewater reclamation and reusing system has been rising as an alternative of water resource exhaustion that the whole world is experiencing. In order to be able to bring about improvement of the existing wastewater reclamation and reusing system, this research has developed of Conversion-Human Machine Interaction (C-HMI) based real-time control and monitoring system such as a sensor module and gate module, web monitoring system. This system was communication almost-error-free in various environment and situation. As a result, we have achieved our goal that has to doing work correctly as a sensor and gateway module that communication error is less than 0.2% throughout the embodied system and add that it can be easily controled and configured as an interface equipment to a complex sensor of water quality. According to this, the construction of a database capable of analyzing and assessing collection, storage and various elements of reliable water quality and flow rate data can be possible.

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

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.63-69
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• 2020

The rapid development in modern industrialization pollutant the water and atmospheric air across the globe that have a major impact on the human and livings health. In worldwide, every country government increasing the importance to improve the outdoor air pollution monitoring and control to provide quality of life and prevent the citizens and livings life from hazard disease. We proposed the environmental dust level detection method for outdoor facilities using sensor fusion technology to measure precise micro-dust level and monitor in realtime. In this proposed approach use the camera sensor and commercial dust level sensor data to predict the micro-dust level with data fusion method. The camera sensor based dust level detection uses the optical flow based machine learning method to detect the dust level and then fused with commercial dust level sensor data to predict the precise micro-dust level of the outdoor facilities and send the dust level informations to the outdoor air pollution monitoring system. The proposed method implemented on raspberry pi based open-source hardware with Internet-of-Things (IoT) framework and evaluated the performance of the system in realtime. The experimental results confirm that the proposed micro-dust level detection is precise and reliable in sensing the air dust and pollution, which helps to indicate the change in the air pollution more precisely than the commercial sensor based method in some extent.