• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.109-110
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• 2022

Currently, interest in the quality of concrete is increasing. Among the important factors for evaluating the quality of concrete, interest in unit-water content is also increasing. Currently, the air-meter method, the microwave oven drying method, the capacitance method, and the microwave penetration method are used to measure the unit-water content of concrete.. Among the above methods, except for the microwave method, the measurement method is complicated, portability is reduced, and economic efficiency is reduced. This research aims to measure a unit-water content by using a Frequency Domain Reflectometry(FDR) sensor that is economical, simple to measure, and portable among microwave methods. In addition, it is an experimental study to determine the accuracy of unit-water content using a single input residual model during deep learning to solve the limitations of the FDR sensor.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.29-30
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• 2022

The unit-water content has a very significant effect on the durability of the construction structure and the quality of concrete. Although there are various methods for measuring the unit-water content, there are problems of time required for measurement, precision, and reproducibility. Recently, there is an FDR sensor capable of measuring moisture content in real time through an apparent dielectric constant change of electromagnetic waves. In addition, various artificial intelligence techniques that can non-linearly supplement the accuracy of FDR sensors are being studied. In this study, the accuracy of unit-water content measurement was compared and evaluated using machine learning and deep learning techniques after normalizing the data secured in concrete using frequency domain reflectometry (FDR) sensors used to measure soil moisture at home and abroad. The result of comparing the accuracy of machine learning and deep learning is judged to be excellent in the accuracy of deep learning, which can well express the nonlinear relationship between FDR sensor data and concrete unit-water content.

• Journal of Biosystems Engineering
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• v.37 no.3
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• pp.209-213
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• 2012

Purpose: This study was performed to develop a simple, disposable thin-film optical nitrate sensor. Methods: The sensor was fabricated by applying a nitrate-selective polymer membrane on the surface of a thin polyester film. The membrane was composed of polyvinylchloride (PVC), plasticizer, fluorescent dye, and nitrate-selective ionophore. Fluorescence intensity of the sensor increased on contact with a nitrate solution. The fluorescence response of the optical nitrate sensor was measured with a commercial fluorospectrometer. Results: The optical sensor exhibited linear response over four concentration decades. Conclusions: Nitrate ion concentrations in plant nutrient solutions can be determined by direct optical measurements without any conditioning before measurements.

• Journal of Sensor Science and Technology
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• v.21 no.5
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• pp.374-378
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• 2012

Biofouling causes many problem in industrial processes, medical health industries, water utilities and our daily life. So detecting formation of biofilm on the surface of medical appliance, water pipe and industrial utility is highly important to prevent the problem caused by biofouling. In this study, we suggest an electrochemical sensor for detecting biofilm. We fabricated the electrochemical sensor in MEMS process and cultivated two different kinds of Pseudomonas aeruginosa RpoN type and Wild type on the surface of electrochemical sensor. Each group of Pseudomonas aeruginosa was cultivated according to the hours of 2, 4, 6, 8, 12 and 24. Then we investigated changes in degree of biofilm cultivation using cyclic voltammetry. As a result, it was observed that peak of the cyclic voltammetry curve is increased according as the biofilm growth on the surface of electrochemical sensor. Also we can discern between Pseudomonas aeruginosa RpoN type and Wild type.

• Corrosion Science and Technology
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• v.4 no.5
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• pp.178-190
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• 2005

In order to develop a new corrosion sensor for detecting and monitoring the external and internal corrosion damage of buried pipeline, the electrochemical property of sensors and the correlation of its output to corrosion rate of steel pipe, were evaluated by electrochemical methods in two soils of varying resistivity (5,000 ohm-cm, 10,000 ohm-cm) and synthetic tap water environments. In this paper, two types of galvanic probes were manufactured: copper-pipeline steel (Cu-CS) and stainless steel-pipeline steel (SS-CS). The corrosion behavior in synthetic groundwater and synthetic tap water for the different electrodes was investigated by potentiodynamic test. The comparison of the sensor output and corrosion rates revealed that a linear relationship was found between the probe current and the corrosion rates. In the soil resistivity of $5,000{\Omega}-cm$ and tap water environments, only the Cu-CS probe had a good linear quantitative relationship between the sensor output current and the corrosion rate of pipeline steel. In the case of $10,000{\Omega}-cm$, although the SS-CS probe showed a better linear correlation than that of Cu-CS probe, the Cu-CS probe is more suitable than SS-CS probe due to the high current output.

• Journal of Korean Society of Water and Wastewater
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• v.33 no.2
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• pp.151-158
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• 2019

Objectives of this study were to identify the hotspot for displacement of the on-line water quality sensors, in order to detect illicit discharge of untreated wastewater. A total of twenty-six water quality parameters were measured in sewer networks of the industrial complex located in Daejeon city as a test-bed site of this study. For the water qualities measured on a daily basis by 2-hour interval, the self-organizing maps(SOMs), one of the artificial neural networks(ANNs), were applied to classify the catchments to the clusters in accordance with patterns of water qualities discharged, and to determine the hotspot for priority sensor allocation in the study. The results revealed that the catchments were classified into four clusters in terms of extent of water qualities, in which the grouping were validated by the Euclidean distance and Davies-Bouldin index. Of the on-line sensors, total organic carbon(TOC) sensor, selected to be suitable for organic pollutants monitoring, would be effective to be allocated in D and a part of E catchments. Pb sensor, of heavy metals, would be suitable to be displaced in A and a part of B catchments.

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.1015-1024
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• 2020

Recently, water pollution accidents have continued to occur in pipelines such as red water Incheon and Seoul. In order to recognize this water quality problem, it is necessary to install a water quality sensor in an appropriate location and measure it in advance to detect or block the water supply to the water faucet of the shelter. However, there are limitations, such as maintenance costs, to installing multiple water meters in all pipelines. Therefore, this study proposed a methodology for determining and prioritizing the installation location of flow-based water quality sensor for the recognition of water quality problems in pipelines. We applied the proposed procedure to the pipe break scenario. The results of the determination of the location of the water quality sensor were presented by applying it to the pipe network that actually operates the emergency pipe in Korea. The result of the decision showed that in the event of abnormal situation caused by the destruction of individual pipes, the flow rate of the pipes around the aqueduct and the tank may change rapidly, resulting in water quality accidents caused by turbidity. In the future, it is expected that the water quality monitoring point selection method, such as establishing an external pipe operation plan for pipe cleaning, will utilize the procedure for determining the location of the water quality sensor according to the velocity.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.17-18
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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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.6-7
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• 2020

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.

• Journal of Korean Society of Rural Planning
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• v.21 no.4
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• pp.177-186
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• 2015

The main objective of this study was to assess the applicability of IoT (Internet of Things)-based flood management under climate change by developing intelligent water level monitoring platform based on IoT. In this study, Arduino Uno was selected as the development board, which is an open-source electronic platform. Arduino Uno was designed to connect the ultrasonic sensor, temperature sensor, and data logger shield for implementing IoT. Arduino IDE (Integrated Development Environment) was selected as the Arduino software and used to develop the intelligent algorithm to measure and calibrate the real-time water level automatically. The intelligent water level monitoring platform consists of water level measurement, temperature calibration, data calibration, stage-discharge relationship, and data logger algorithms. Water level measurement and temperature calibration algorithm corrected the bias inherent in the ultrasonic sensor. Data calibration algorithm analyzed and corrected the outliers during the measurement process. The verification of the intelligent water level measurement algorithm was performed by comparing water levels using the tape and ultrasonic sensor, which was generated by measuring water levels at regular intervals up to the maximum level. The statistics of the slope of the regression line and $R^2$ were 1.00 and 0.99, respectively which were considered acceptable. The error was 0.0575 cm. The verification of data calibration algorithm was performed by analyzing water levels containing all error codes in a time series graph. The intelligent platform developed in this study may contribute to the public IoT service, which is applicable to intelligent flood management under climate change.