• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.46-63
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• 2024

One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring real-time water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA- PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.477-484
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• 2017

In the conventional recirculation fish farms, there is a lot of difficulties due to lack of professional manpower and high reliance on imported measurement equipment. In this paper, we implement an automatic water quality measurement system which can measure the pollution degree in a water tank of fish farms using an optical sensor(pH, DO) with the IoT technology. The problem with existing systems is that the fish tank should be checked by means of human, or put the measuring equipment into the water tank of fish farms and measurement directly. But, it has a bad influence on the growth of fish. In this paper, we propose a method of indirect measurement without immersing the measurement equipment in a water tank of fish farm and develop a sustainable measurement system in an environment containing salt and lots of pollutants without affecting the growth of fish within the water tank of fish farms.

• 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.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• v.10 no.1
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• pp.123-126
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• 2005

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.8-9
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• 2021

The unit-water content of concrete is one of the important factors in determining the quality of concrete and is directly related to the durability of the construction structure, and the current method of measuring the unit-water content of concrete is applied by the Air Meta Act and the Electrostatic Capacity Act. However, there are complex and time-consuming problems with measurement methods. Therefore, high frequency moisture sensor was used for quick and high measurement, and unit-water content of mortar was evaluated through machine running and deep running based on measurement big data. The multi-input deep learning model is as accurate as 24.25% higher than the OLS linear regression model, which shows that deep learning can more effectively identify the nonlinear relationship between high-frequency moisture sensor data and unit quantity than linear regression.

• Agricultural and Biosystems Engineering
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• v.5 no.1
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• pp.10-20
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• 2004

Precision agriculture aims to minimize costs and environmental damage caused by agriculture and to maximize crop yield and profitability, based on information collected at within-field locations. In this process, quantification of soil physical properties, including soil strength, would be useful. To quantify and manage variability in soil strength, there is need for a strength sensor that can take measurements continuously while traveling across the field. In this paper, preliminary analyses were conducted using two datasets available with current technology, (1) cone penetrometer readings collected at different compaction levels and for different soil textures and (2) tillage draft (TD) collected from an entire field. The objective was to provide information useful for design of an on-the-go soil strength profile sensor and for interpretation of sensor test results. Analysis of cone index (CI) profiles led to the selection of a 0.5-m design sensing depth, 10-MPa maximum expected soil strength, and 0.1-MPa sensing resolution. Compaction level, depth, texture, and water content of the soil all affected CI. The effects of these interacting factors on data obtained with the soil strength sensor should be investigated through experiments. Spatial analyses of CI and TD indicated that the on-the-go soil strength sensor should acquire high spatial-resolution, high-frequency ($\ge$ 4 Hz) measurements to capture within-field spatial variability.

• 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 information and communication convergence engineering
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• v.10 no.1
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• pp.11-20
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• 2012

In a marine ubiquitous sensor network (USN) system using expensive sensors in the harsh ocean environment, it is very important to discover failures and devise recovery techniques to deal with such failures. Therefore, in order to perform failure modeling, this study analyzes the USN-based real-time water quality monitoring service of the Gaduri Aqua Farms at Songdo Island of Yeosu, South Korea and devises methods of discovery and recovery of failure by classifying the types of failure into system element failure, communication failure, and data failure. In particular, to solve problems from the perspective of data, this study defines data integrity and data consistency for use in identifying data failure. This study, by identifying the exact type of failure through analysis of the cause of failure, proposes criteria for performing relevant recovery. In addition, the experiments have been made to suggest the duration as to how long the data should be stored in the gateway when such a data failure occurs.

• Journal of Sensor Science and Technology
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• v.23 no.1
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• pp.58-65
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• 2014

We developed a low-cost automatic diagnosis system for water quality in cooling towers to measure the concentrations of key ingredients such as $Ca^{2+}$, $Cl^-$, $PO{_4}^{3-}$, and $Fe^{2+}$. $Ca^{2+}$, and $Cl^-$ are the main factors that cause the generation of scale, corrosion, and sludge in water pipes. $PO{_4}^{3-}$ prevents corrosion, sludge and scale by inhibiting the ions (i.e., $Ca^{2+}$, $Cl^-$) from sticking to the pipes. $Fe^{2+}$ is an indicator of pipe corrosion. The proposed system consists of a microprocessor, a specimen container and heater, a precision pump, relays and valves, LED optical sources, and photo detectors. It automatically collects water samples and carries out pretreatment for determining the concentration of each chemical, and then estimates the concentration of each ion using low-cost LED optical sources and detectors. Experimental results showed that the accuracy of the proposed system is sufficiently high for water quality diagnosis and management of cooling towers, demonstrating the possibility of the proposed system's wide usage in real environments.