• Journal of Wetlands Research
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• v.18 no.2
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• pp.132-147
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• 2016

In this study the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) sensor onboard the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission-Water (GCOM-W1) based soil moisture retrievals were revised to obtain better accuracy of soil moisture and higher data acquisition rate over East Asia. These satellite-based soil moisture products are revised against a reference land model data set, called Global Land Data Assimilation System (GLDAS), using Cumulative Distribution Function (CDF) matching and regression approach. Since MIRAS sensor is perturbed by radio frequency interferences (RFI), the worst part of soil moisture retrieval, East Asia, constantly have been undergoing loss of data acquisition rate. To overcome this limitation, the threshold of RFI, DQX, and composite days were suggested to increase data acquisition rate while maintaining appropriate data quality through comparison of land surface model data set. The revised MIRAS and AMSR2 products were compared with in-situ soil moisture and land model data set. The results showed that the revising process increased correlation coefficient values of SMOS and AMSR2 averagely 27% 11% and decreased the root mean square deviation (RMSD) decreased 61% and 57% as compared to in-situ data set. In addition, when the revised products' correlation coefficient values are calculated with model data set, about 80% and 90% of pixels' correlation coefficients of SMOS and AMSR2 increased and all pixels' RMSD decreased. Through our CDF-based revising processes, we propose the way of mutual supplementation of MIRAS and AMSR2 soil moisture retrievals.

• 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.

• Journal of Sensor Science and Technology
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• v.32 no.3
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• pp.140-146
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• 2023

Water quality is crucial for human health and the environment. Accurate measurement of the quantity of organic carbon in water is essential for water quality evaluation, identification of water pollution sources, and appropriate implementation of water treatment measures. Total organic carbon (TOC) analysis is an important tool for this purpose. Although other methods, such as chemical oxygen demand (COD) and biochemical oxygen demand (BOD) are also used to measure organic carbon in water, they have limitations that make TOC analysis a more favorable option in certain situations. For example, COD requires the use of toxic chemicals, and BOD is time-consuming and can produce inconsistent and unreliable results. In contrast, TOC analysis is rapid and reliable, providing accurate measurements of organic carbon content in water. However, common methods for TOC analysis can be complex and energy-intensive because of the use of high-temperature heaters for liquid-to-gas phase transitions and the use of acid, which present safety risks. This study focuses on a TOC analysis method using TiO₂ photocatalysis, which has several advantages over conventional TOC analysis methods, including its low cost and easy maintenance. For TiO₂, rutile and anatase powders are mixed with an inorganic binder and spray-coated onto a glass fiber substrate. The TiO₂ powder and inorganic binder solutions are adjusted to optimize the photocatalytic reaction performance. The TiO₂ photocatalysis method is a simple and low-power approach to TOC analysis, making it a promising alternative to commonly used TOC analysis methods. This study aims to contribute to the development of more efficient and cost-effective approaches for water quality analysis and management by exploring the effectiveness and reliability of the developed equipment.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1049-1051
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• 2003

This study investigated the use of airborne image data to provide estimates of within -field variability in soil properties and crop growth as an alternative to extensive field data collection. Hyperspectral and multispectral images were acquired in 2000, 2001, and 2002 for central Missouri experimental fields. Data were converted to reflectance using chemically-treated reference tarps with known reflectance levels. Geometric distortion of the hyperspectral pushbroom sensor images was corrected with a rubber sheeting transformation. Statistical analyses were used to relate image data to field-measured soil properties and crop characteristics. Results showed that this approach has potential; however, it is important to address a number of implementation issues to insure quality data and accurate interpretations.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.317-320
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• 1997

In the water purification plant, chemicals are injected for quick purification of raw water. It is clear that the amount of chemicals intrinsically depends on the water quality such as turbidity, temperature, pH and alkalinity etc. However, the process of chemical reaction to improve water quality by the chemicals is not yet fully clarified nor quantified. The feedback signal in the process of coagulant dosage, which should be measured (through the sensor of the plant) to compute the appropriate amount of chemicals, is also not available. Most traditional methods focus on judging the conditions of purifying reaction and determine the amounts of chemicals through manual operation of field experts or jar-test results. This paper presents the method of deriving the optimum dosing rate of coagulant, PAC(Polymerized Aluminium Chloride) for coagulant dosing process in water purification system. A neural network model is developed for coagulant dosing and purifying process. The optimum coagulant dosing rate can be derived the neural network model. Conventionally, four input variables (turbidity, temperature, pH, alkalinity of raw water) are known to be related to the process, while considering the relationships to the reaction of coagulation and flocculation. Also, the turbidity in flocculator is regarded as a new input variable. And the genetic algorithm is utilized to identify the neural network structure. The ability of the proposed scheme validated through the field test is proved to be of considerable practical value.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.44 no.5
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• pp.30-37
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• 2007

Hardware for monitoring the water quality is developed using water fleas. Water flea is a frequently used biological sensor for monitoring the water quality. Water fleas quickly respond to the incoming toxic water by changing their activity when they are exposed. By measuring the activity of water fleas, the incoming toxic water is instantly detected in real time. So far the measurement of activity of water fleas has been done with a system equipped with a light source of LED and a light detector of photo transistor. Water flea itself is, however, sensitive to light resulting in incorrect response and the system has two inconvenient separate parts of the light source and the detector. This paper suggests a system using a CCD camera instead of a light source and a detector. The suggested system processes the image data from the CCD camera in real time without any delay. The developed system becomes a part of the remote water monitoring embedded system.

• Journal of ILASS-Korea
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• v.6 no.1
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• pp.18-24
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• 2001

Liquid fossil fuel contaminated by water can cause trouble in the combustion processes and affect the endurance of a combustion system. Using an optical sensor to monitor the water content instantaneously in a fuel pipeline is an effective means of controlling the fuel quality in a combustion system. In two component liquid flows of oil and water, the flow pattern and characteristics of water droplets are changed with various flow conditions. Additionally, the light scattering of the optical sensor measuring the water content is also dependent on the flow patterns and droplet characteristics. Therefore, it is important to investigate the detailed behavior of water droplets in the pipeline of the fuel transportation system. In this study, the flow patterns and characteristics of water droplets in the turbulent pipe flow of two component liquids of gasoline and water were investigated using optical measurements. The dispersion of water droplets in the gasoline flow was visualized, and the size and velocity distributions of water droplets were simultaneously measured by the phase Doppler technique. The Reynolds number of the gasoline pipe flow varied in the range of $4{\times}10^{4}\;to\;1{\times}10^{3}$, and the water content varied in the range of 50 ppm to 300 ppm. The water droplets were spherical and dispersed homogeneously in all variables of this experiment. The velocity of water droplets was not dependent on the droplet size and the mean velocity of droplets was equal to that of the gasoline flow. The mean diameter of water droplets decreased and the number density increased with the Reynolds number of the gasoline flow.

• 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.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.61-73
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• 2014

This study reviewed an application of water quality estimation using an Airborne Hyperspectral Imagery (A-HSI) and tested a part of Han River water quality (especially suspended solid) estimation with available in-situ data. The estimation of water quality was processed two methods. One is using observation data as downwelling radiance to water surface and as scattering and reflectance into water body. Other is linear regression analysis with water quality in-situ measurement and upwelling data as at-sensor radiance (or reflectance). Both methods drive meaningful results of RS estimation. However it has more effects on the auxiliary dataset as water quality in-situ measurement and water body scattering measurement. The test processed a part of Han River located Paldang-dam downstream. We applied linear regression analysis with AISA eagle hyperspectral sensor data and water quality measurement in-situ data. The result of linear regression for a meaningful band combination shows $-24.847+0.013L_{560}$ as 560 nm in radiance (L) with 0.985 R-square. To comparison with Multispectral Imagery (MSI) case, we make simulated Landsat TM by spectral resampling. The regression using MSI shows -55.932 + 33.881 (TM1/TM3) as radiance with 0.968 R-square. Suspended Solid (SS) concentration was about 3.75 mg/l at in-situ data and estimated SS concentration by A-HIS was about 3.65 mg/l, and about 5.85mg/l with MSI with same location. It shows overestimation trends case of estimating using MSI. In order to upgrade value for practical use and to estimate more precisely, it needs that minimizing sun glint effect into whole image, constructing elaborate flight plan considering solar altitude angle, and making good pre-processing and calibration system. We found some limitations and restrictions such as precise atmospheric correction, sample count of water quality measurement, retrieve spectral bands into A-HSI, adequate linear regression model selection, and quantitative calibration/validation method through the literature review and test adopted general methods.

• Journal of Biosystems Engineering
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• v.41 no.2
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• pp.138-144
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• 2016

Purpose: Phosphorus is an essential element for water quality control. Excessive amounts of phosphorus causes algal bloom in water, which leads to eutrophication and a decline in water quality. It is necessary to maintain the optimum amount of phosphorus present. During the last decades, various studies have been conducted to determine phosphorus content in water. In this study, we present a comprehensive overview of colorimetric, electrochemical, fluorescence, microfluidic, and remote sensing technologies for the measurement of phosphorus in water, along with their working principles and limitations. Results: The colorimetric techniques determine the concentration of phosphorus through the use of color-generating reagents. This is specific to a single chemical species and inexpensive to use. The electrochemical techniques operate by using a reaction of the analyte of interest to generate an electrical signal that is proportional to the sample analyte concentration. They show a good linear output, good repeatability, and a high detection capacity. The fluorescence technique is a kind of spectroscopic analysis method. The particles in the sample are excited by irradiation at a specific wavelength, emitting radiation of a different wavelength. It is possible to use this for quantitative and qualitative analysis of the target analyte. The microfluidic techniques incorporate several features to control chemical reactions in a micro device of low sample volume and reagent consumption. They are cheap and rapid methods for the detection of phosphorus in water. The remote sensing technique analyzes the sample for the target analyte using an optical technique, but without direct contact. It can cover a wider area than the other techniques mentioned in this review. Conclusion: It is concluded that the sensing technologies reviewed in this study are promising for rapid detection of phosphorus in water. The measurement range and sensitivity of the sensors have been greatly improved recently.