• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.257-266
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• 2021

Urban areas can often suffer flood damage because of the more frequent catastrophic rainfall events from climate change. Flood mitigation measures consist of (1) structural and (2) non-structural measures. In this study, the proposed method focused on operating an urban drainage system among non-structural measures. The combined inland-river operation technique estimates the inflow of pump stations based on the water level obtained from a preselected monitoring point, and the pump station expels the stored rainwater to the riverside based on those estimates. In this study, the proposed method was applied to the Mokgam drainage watershed, where catastrophic rainfall events occurred (i.e., 2010- and 2011-years), and severe flood damage was recorded in Seoul. Using the proposed method, the efficiency of flood reduction from the two rainfall events was reduced by 34.9 % and 54.4 %, respectively, compared to the current operation method. Thus, the proposed method can minimize the flood damage in the Mokgam drainage watershed by reserving the additional storage space of a reservoir. In addition, flooding from catastrophic rainfall can be prevented, and citizens' lives and property in urban areas can be protected.

• The Journal of Korean Society for School & Community Health Education
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• v.22 no.4
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• pp.49-60
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• 2021

Objectives: Laboratories have various latent physical, chemical, biological, and ergonomical factors according to the diversification and fusion of research and development activities. This study aims to investigate the chemical exposure concentrations of college laboratories and evaluate their health risks, and use them as basic data to promote the health of college students. Methods: The sampling and analysis of harmful chemicals in the air in laboratories were performed using Method 1500 of the U.S. National Institute for Occupational Safety and Health (NIOSH)의 Method 1500. The harmful chemicals in the laboratories were divided into carcinogenic and non-carcinogenic chemicals. Risk assessment was performed using the cancer risk (CR) for carcinogenic chemicals and using the hazard index (HI) for non-carcinogenic chemicals. Results: The harmful chemicals in college laboratories consisted of acetone, diethyl ether, methylene chloride, n-hexane, ethyl acetate, chloroform, tetrahydrofuran, toluene, and xylenes. They showed the highest concentrations in laboratories A (acetone 0.001~2.34ppm), B (chloroform 0.95~6.35ppm), C (diethyl ether 0.08~8.68ppm), and D (acetone 0.07~14.96ppm). The risk assessment result for non-carcinogenic chemicals showed that the HI of methylene chloride was 2.052 for men and 2.333 for women, the HI of N-hexane was 4.442 for men and 5.05 for women. Thus, the HI values were higher than 1. The risk of carcinogenic chemicals is determined by an excess cancer risk (ECR) value of 1.0×10^-5, which means that one in 100,000 people has a cancer risk. The ECRs of chloroform exceeded 1.0×10^-5 for both men and women, indicating the possibility of cancer risk. Conclusion: College laboratories showed the possibility of non-carcinogenic health risks for methylene chloride, n-hexane, tetrahydrofuran (THF), toluene, and xylenes, and carcinogenic health risks for chloroform, methylene chloride. However, this study used the maximum values of measurements to determine the worst case, and assumed that the subjects were exposed to the corresponding concentrations continuously for 8 hours per day for 300 days per year. In consideration of the nature of laboratory environment in which people are intermittently exposed, rather than continuously, to the chemicals, the results of this study has an element of overestimation.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.167-177
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• 2022

In order to obtain satellite image products using the image transmitted to the ground station after capturing the satellite images, many image pre/post-processing steps are involved. During the pre/post-processing, when converting from level 1R images to level 1G images, geometric correction is essential. An interpolation method necessary for geometric correction is inevitably used, and the quality of the level 1G images is determined according to the accuracy of the interpolation method. Also, it is crucial to speed up the interpolation algorithm by the level processor. In this paper, we proposed a lightweight CNN-based interpolation method required for geometric correction when converting from level 1R to level 1G. The proposed method doubles the resolution of satellite images and constructs a deep learning network with a lightweight deep convolutional neural network for fast processing speed. In addition, a feature map fusion method capable of improving the image quality of multispectral (MS) bands using panchromatic (PAN) band information was proposed. The images obtained through the proposed interpolation method improved by about 0.4 dB for the PAN image and about 4.9 dB for the MS image in the quantitative peak signal-to-noise ratio (PSNR) index compared to the existing deep learning-based interpolation methods. In addition, it was confirmed that the time required to acquire an image that is twice the resolution of the 36,500×36,500 input image based on the PAN image size is improved by about 1.6 times compared to the existing deep learning-based interpolation method.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1935-1943
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• 2022

The development and application of a high-resolution soil moisture mapping method using satellite imagery has been considered one of the major research themes in remote sensing. In this study, soil moisture mapping in the test area of Jeju Island was performed. The soil moisture was calculated with optical images using linearly adjusted Synthetic Aperture Radar (SAR) polarization images and incident angle. SAR Backscatter data, Analysis Ready Data (ARD) provided by Google Earth Engine (GEE), was used. In the soil moisture processing process, the optical image was applied to normalized difference vegetation index (NDVI) by surface reflectance of KOMPSAT-3 satellite images and the land cover map of Environmental Systems Research Institute (ESRI). When the SAR image and the optical images are fused, the reliability of the soil moisture product can be improved. To validate the soil moisture mapping product, a comparative analysis was conducted with normalized difference water index (NDWI) products by the KOMPSAT-3 image and those of the Landsat-8 satellite. As a result, it was shown that the soil moisture map and NDWI of the study area were slightly negative correlated, whereas NDWI using the KOMPSAT-3 images and the Landsat-8 satellite showed a highly correlated trend. Finally, it will be possible to produce precise soil moisture using KOMPSAT optical images and KOMPSAT SAR images without other external remotely sensed images, if the soil moisture calculation algorithm used in this study is further developed for the KOMPSAT-5 image.

• Journal of the Korean Society of Radiology
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• v.16 no.7
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• pp.953-960
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• 2022

The measurement of branching ratio of ¹⁶⁷Yb radioactive isotopes from gamma-ray spectrum of ¹⁶⁹Tm(p,3n)¹⁶⁷Yb reaction were performed by using a 100-MeV proton linear accelerator of the Korea Multi-purpose Accelerator Complex (KOMAC). The ¹⁶⁷Yb isotope has a half-life of 17.5 minutes and decays to ¹⁶⁹Tm. The gamma rays generated from the ¹⁶⁷Yb isotope were measured using an HPGe detector gamma ray spectroscopy system. The energy calibration of the detector and the efficiency measurement of the detector were determined using a standard source. The gamma rays of known main energy (62.9, 106.2, 113.3, 143.5 and 176.3 keV) were measured. On the other hand, information about the intensity of the generated gamma rays is very inaccurate. Therefore, in this study, the decay strength of the main gamma rays was accurately measured. Overall, it was different from the previously known results, and in particular, it was found that the intensity of the main decay gamma ray, such as the 113.3 and 106.2 keV gamma ray, was overestimated, and it was found that the gamma ray, such as 62.9, 116.7 and 143.5 keV was underestimated. The present results are considered to be important information in the fields of nuclear fusion, astrophysics and nuclear physics in the future.

• The Korean Society of Law and Medicine
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• v.24 no.1
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• pp.43-65
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• 2023

The healthcare industry is a digital healthcare that combines technology based on the 4th Industrial Revolution, dealing with information on individual health and medical care, and is a fusion of health care services and medical science and technology. It is questionable whether digital healthcare according to the paradigm change can be discussed by the concept of medical practice under the existing Medical Act. There is no clear definition of the concept of medical practice in the Medical Service Act, but the concept is established through precedents. In addition, under the Medical Service Act, the subject of medical practice is limited to medical personnel. However, digital healthcare sometimes diagnoses and treats diseases using digital technology by medical personnel. On the other hand, what is possible by non-medical personnel is digital healthcare. This is because digital healthcare is understood as a concept that includes health care such as exercise, eating habits, and weight control. For this reason, if the concept of medical practice under the "Medical Act" on digital healthcare is included, it is subject to criminal punishment for "unlicensed medical practice" prescribed in Article 27 of the "Medical Act". In the health and medical industry, digital transformation and convergence with information and communication technology are rapidly progressing. As a result, there is a need to newly define it as 'digitalized medical practice' or 'information and communication technology (ICT)-based medical practice' separately from existing medical practices. The concept of medical practice has variability, not a fixed and invariable concept. However, in response to this demand, it is not an infinite expansion of the concept of medical practice, but a request to reset its scope. Therefore, the concept of medical practice should be legislated by reflecting the demand of consumers for the medical service system.

• Economic and Environmental Geology
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• v.56 no.4
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• pp.409-419
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• 2023

Acid mine drainage(AMD) treatment is classified as both passive and active treatment. During the treatment, about 5,000 tons of neutralization sludge is generated as a by-product per year in Korea. This study was conducted to evaluate the characteristics of sludge generated from physico·chemical treatment processes as an active treatment from 5 different sources (D, H, S, T, Y) and the possibility of the sludges being recycled. The sludges have a pH range of 5.86 ~ pH 7.89, and a water content range of 51% ~ 82%. Most of particle sizes were less than 25 ㎛. In analysis of inorganic elements, the concentration of Al, Fe, and Mn were between 1,189 mg/kg ~ 129,344 mg/kg, 106,132 mg/kg ~ 338,011 mg/kg, and 3,472 mg/kg ~ 11,743 mg/kg, respectively. The concentration of As and Zn in sludge-T, Cd in sludge-D, Ni in sludge-H, Zn in sludge-S, and Cd in sludge-Y exceeded the soil contamination standards of Korea. The results from 2 separate kinds of leaching test, the Korea Standard Leaching Test(KSLT) and Toxicity Characteristic Leaching Procedure(TCLP), showed that all the sludges met the Korea groundwater standards. From the XRD and SEM-EDS analysis, the peaks of calcite and quartz were found in the sludges. The sludge also had a high proportion of Fe and O, and the majority of the composition was amorphous iron hydroxide.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.9-18
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• 2023

Predicting the compressive strength of high-performance concrete (HPC) is challenging because of the use of additional cementitious materials; thus, the development of improved predictive models is essential. The purpose of this study was to develop an HPC compressive-strength prediction model using an ensemble machine-learning method of combined bagging and stacking techniques. The result is a new ensemble technique that integrates the existing ensemble methods of bagging and stacking to solve the problems of a single machine-learning model and improve the prediction performance of the model. The nonlinear regression, support vector machine, artificial neural network, and Gaussian process regression approaches were used as single machine-learning methods and bagging and stacking techniques as ensemble machine-learning methods. As a result, the model of the proposed method showed improved accuracy results compared with single machine-learning models, an individual bagging technique model, and a stacking technique model. This was confirmed through a comparison of four representative performance indicators, verifying the effectiveness of the method.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.599-608
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• 2023

The increasing interest in soil moisture data using satellite data for applications of hydrology, meteorology, and agriculture has led to the development of methods for generating soil moisture maps of variable resolution. This study demonstrated the capability of generating soil moisture maps using Sentinel-1 and Sentinel-2 data provided by Google Earth Engine (GEE). The soil moisture map was derived using synthetic aperture radar (SAR) image and optical image. SAR data provided by the Sentinel-1 analysis ready data in GEE was applied with normalized difference vegetation index (NDVI) based on Sentinel-2 and Environmental Systems Research Institute (ESRI)-based Land Cover map. This study produced a soil moisture map in the research area of Victoria, Australia and compared it with field measurements obtained from a previous study. As for the validation of the applied method's result accuracy, the comparative experimental results showed a meaningful range of consistency as 4-10%p between the values obtained using the algorithm applied in this study and the field-based ones, and they also showed very high consistency with satellite-based soil moisture data as 0.5-2%p. Therefore, public open data provided by GEE and the algorithm applied in this study can be used for high-resolution soil moisture mapping to represent regional land surface characteristics.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1185-1193
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• 2023

The increasing interest in soil moisture data from satellite imagery for applications in hydrology, meteorology, and agriculture has led to the development of methods to produce variable-resolution soil moisture maps. Research on accurate soil moisture estimation using satellite imagery is essential for remote sensing applications. The purpose of this study is to generate a soil moisture estimation map for a test area using KOMPSAT-3/3A and KOMPSAT-5 SAR imagery and to quantitatively compare the results with soil moisture data from the Soil Moisture Active Passive (SMAP) mission provided by NASA, with a focus on accuracy validation. In addition, the Korean Environmental Geographic Information Service (EGIS) land cover map was used to determine soil moisture, especially in agricultural and forested regions. The selected test area for this study is the western part of Jeju, South Korea, where input data were available for the soil moisture estimation algorithm based on the Water Cloud Model (WCM). Synthetic Aperture Radar (SAR) imagery from KOMPSAT-5 HV and Sentinel-1 VV were used for soil moisture estimation, while vegetation indices were calculated from the surface reflectance of KOMPSAT-3 imagery. Comparison of the derived soil moisture results with SMAP (L-3) and SMAP (L-4) data by differencing showed a mean difference of 4.13±3.60 p% and 14.24±2.10 p%, respectively, indicating a level of agreement. This research suggests the potential for producing highly accurate and precise soil moisture maps using future South Korean satellite imagery and publicly available data sources, as demonstrated in this study.