• 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.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.59-60
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• 2021

The unit-water content has a major problem in concrete structures which leads to micro cracks on the concrete during drying time. Thus, the compressive strength and durability of the concrete structures are significantly reduced. Several techniques have been developed to measure the unit-water content in concrete structures such as heating drying, unit volume mass, and capacitance measurements. However, these techniques have problems in during measurement such as longer time, expensive and difficult in analysis of data. Frequency Domain Reflectivity (FDR) is one of the sensors which used to measure the water content. This method has several advantages including easy to measure, inexpensive, and capable of measuring moisture in real time. In this study, an attempt has been made to evaluate the unit-water content in concrete using the FDR sensor and interpret the data with deep learning method.

• 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.

• 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 Environmental Science International
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• v.31 no.10
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• pp.869-881
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• 2022

In this study, a field study was conducted to investigate the relationship between high-resolution remote images and the volumetric moisture, and the number of compaction. Changes in the shape of the surface and soil moisture content were observed and correlated with the number of compactions using roller equipment. As the compaction is repeated, the surface is flattened and the terrain curvature decreases and converges to zero. In particular, the tangential curvature changes as the number of compactions increase. Due to soil compaction, the vegetation index changed from a positive to a negative value, and most of the test site area was homogenized with a negative index. This suggests a decrease in porosity and an increase in volumetric water content associated with increasing soil compaction. Soil moisture, measured using a frequency domain reflectometry(FDR) sensor, tends to increase proportionately with the number of vibration compactions, but the correlation between the number of compactions and soil moisture is unclear. This study suggests that while it is necessary to consider the reproducibility of the experiments performed, the compaction quality of the soil can be evaluated using high-resolution terrain factors and soil moisture.

• Atmosphere
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• v.19 no.2
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• pp.213-226
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• 2009

In order to investigate the characteristics of Changma over the Korean peninsula, KEOP-2007 IOP (Intensive Observing Period) was conducted from 15 June 2007 to 15 July 2007. KEOP-2007 IOP is high spatial and temporal radiosonde observations (RAOB) which consisted of three special stations (Munsan, Haenam, and Ieodo) from National Institute of Meteorological Research, five operational stations (Sokcho, Baengnyeongdo, Pohang, Heuksando, and Gosan) from Korea Meteorological Administration (KMA), and two operational stations (Osan and Gwangju) from Korean Air Force (KAF) using four different types of radiosonde sensors. The error statistics of the sensor of radiosonde were investigated using quality control check. The minimum and maximum error frequency appears at the sensor of RS92-SGP and RS1524L respectively. The error frequency of DFM-06 tends to increase below 200 hPa but RS80-15L and RS1524L show vice versa. Especially, the error frequency of RS1524L tends to increase rapidly over 200 hPa. Systematic biases of radiosonde show warm biases in case of temperature and dry biases in case of relative humidity compared with ECMWF (European Center for Medium-Range Weather Forecast) analysis data and precipitable water vapor from GPS. The maximum and minimum values of systematic bias appear at the sensor of DFM-06 and RS92-SGP in case of temperature and RS80-15L and DFM-06 in case of relative humidity. The systematic warm and dry biases at all sensors tend to increase during daytime than nighttime because air temperature around sensor increases from the solar heating during daytime. Systematic biases of radiosonde are affected by the sensor type and the height of the sun but random errors are more correlated with the moisture conditions at each observation station.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.5
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• pp.1183-1188
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• 2011

The novel metal position detection method is proposed where conventional techniques, in high temperature, moisture and particle environment, are not able to be applied. It is known that electronic devices, utilizing microwave, ultrasonic or optical technique, are hard to apply for sensing application where temperature is exceeding above 300 degree centigrade. Metal position detection technique, which was consisted with passive elements facing hot sensing surface, utilizing electromagnetic wave was investigated, and the metal detection sensitivity was measured by varying sensor frequency and sensing distance. Measurement result in laboratory test set-up showed position measurement resolution up to 1mm, when distance between two sensing elements were 500mm, and possibility to measure position of hot metal sheet having very high surface temperature.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.315-318
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• 2001

In this paper recommends the development of automated silo dry & storage system. Most of existing round shaped iron silo arc used for only storage after dry and have many problems in storing. So We have developed an automated dry system which used the previously equipped silo and enables it to be used for storage after dry. The system features high frequency-resistance water sensor in Hopper scale that is in general use within the country its silo that is manufactured by the nerve DSP(TMS320C32) system water compensator and visualization that is finally realized with a personal computer.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.292-299
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• 2022

In precision agriculture (PA), the differences of the agriculture related parameters such as temperature, humidity, soil moisture among different fields are considered and analyzed to precisely utilize water, pesticides, fertilizer, seeds, etc. in fields. Hence, it becomes possible to increase the profit, reduce waste and maintain quality products. This paper suggests a framework for RFID sensor network in view of PA, especially, associated with Container-grown seedlings(CGS), and presents the analysis and simulation by using Ultra High Frequency (UHF) RFID tag system. The simulation is divided into the transmitter and receiver part using Matlab/Simulink. The architecture of the model is flexible to achieve different modulation and encoding types. Finally, some results of the simulation are presents.