• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.665-679
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• 2019

Mesoscale soil moisture measurement from the promising Cosmic-Ray Neutron Probe (CRNP) is expected to bridge the gap between large scale microwave remote sensing and point-based in-situ soil moisture observations. Traditional calibration based on $N_0$ method is used to convert neutron intensity measured at the CRNP to field scale soil moisture. However, the static calibration parameter $N_0$ used in traditional technique is insufficient to quantify long term soil moisture variation and easily influenced by different time-variant factors, contributing to the high uncertainties in CRNP soil moisture product. Consequently, in this study, we proposed a modified traditional calibration method, so-called Dynamic-$N_0$ method, which take into account the temporal variation of $N_0$ to improve the CRNP based soil moisture estimation. In particular, a nonlinear regression method has been developed to directly estimate the time series of $N_0$ data from the corrected neutron intensity. The $N_0$ time series were then reapplied to generate the soil moisture. We evaluated the performance of Dynamic-$N_0$ method for soil moisture estimation compared with the traditional one by using a weighted in-situ soil moisture product. The results indicated that Dynamic-$N_0$ method outperformed the traditional calibration technique, where correlation coefficient increased from 0.70 to 0.72 and RMSE and bias reduced from 0.036 to 0.026 and -0.006 to $-0.001m^3m^{-3}$. Superior performance of the Dynamic-$N_0$ calibration method revealed that the temporal variability of $N_0$ was caused by hydrogen pools surrounding the CRNP. Although several uncertainty sources contributed to the variation of $N_0$ were not fully identified, this proposed calibration method gave a new insight to improve field scale soil moisture estimation from the CRNP.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1135-1147
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• 2021

In order to geometrically correct high-resolution satellite imagery, the sensor modeling process that restores the geometric relationship between the satellite sensor and the ground surface at the image acquisition time is required. In general, high-resolution satellites provide RPC (Rational Polynomial Coefficient) information, but the vendor-provided RPC includes geometric distortion caused by the position and orientation of the satellite sensor. GCP (Ground Control Point) is generally used to correct the RPC errors. The representative method of acquiring GCP is field survey to obtain accurate ground coordinates. However, it is difficult to find the GCP in the satellite image due to the quality of the image, land cover change, relief displacement, etc. By using image maps acquired from various sensors as reference data, it is possible to automate the collection of GCP through the image matching algorithm. In this study, the RPC of KOMPSAT-3A satellite image was corrected through the extracted matching point using the UAV (Unmanned Aerial Vehichle) imagery. We propose a pre-porocessing method for the extraction of matching points between the UAV imagery and KOMPSAT-3A satellite image. To this end, the characteristics of matching points extracted by independently applying the SURF (Speeded-Up Robust Features) and the phase correlation, which are representative feature-based matching method and area-based matching method, respectively, were compared. The RPC adjustment parameters were calculated using the matching points extracted through each algorithm. In order to verify the performance and usability of the proposed method, it was compared with the GCP-based RPC correction result. The GCP-based method showed an improvement of correction accuracy by 2.14 pixels for the sample and 5.43 pixelsfor the line compared to the vendor-provided RPC. In the proposed method using SURF and phase correlation methods, the accuracy of sample was improved by 0.83 pixels and 1.49 pixels, and that of line wasimproved by 4.81 pixels and 5.19 pixels, respectively, compared to the vendor-provided RPC. Through the experimental results, the proposed method using the UAV imagery presented the possibility as an alternative to the GCP-based method for the RPC correction.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.275-290
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• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.

• Membrane Journal
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• v.28 no.6
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• pp.432-443
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• 2018

In this study, propylene/propane separation behavior of Na-type faujasite zeolite membranes is predicted by observing gravimetric adsorptions of propylene and propane on zeolite 13X. The gravimetric adsorptions were measured by using a magnetic suspension balance (MSB) at temperatures of 323, 343, 363 K and a pressure range of 0.02-1 bar. The pressure was increased at 0.1 bar intervals. As adsorption temperature increased, adsorptions of propylene and propane decreased and propylene/propane adsorption selectivity increased. Also, the diffusion coefficients of propylene and propane were increased as the adsorption temperature increased, following the Arrhenius equation. The maximum propylene/propane diffusion selectivity was 0.9753 at 323 K. The perm-selectivity was calculated from the adsorption data of zeolite 13X and compared with the perm-selectivity measured in the single gas permeation experiment for the Na-type faujasite zeolite membrane. The maximum values for the calculated and measured perm-selectivities were observed at a temperature of 323 K. It could be concluded that the prediction of propylene/propane separation of surface diffusion-based membrane by using gravimetric adsorption data is reasonable. Therefore, it is expected that this prediction method can be applied to the screening of adsorption-based microporous membrane for propylene/propane separation.

• Composites Research
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• v.34 no.3
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• pp.192-198
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• 2021

Due to enormous market growing of electric vehicles without combustion engine, reducing unwanted BSR (buzz, squeak, and rattle) noise is highly demanded for vehicle quality and performance. Particularly, innerbelt weatherstrips which not only block wind noise, rain, and dust from outside, but also reduce noise and vibration of door glass and vehicle are required to exhibit high damping properties for improved BSR performance of the vehicle. Thermoplastic elastomers (TPEs), which can be recycled and have lighter weight than thermoset elastomers, are receiving much attention for weatherstrip material, but TPEs exhibit low material damping and compression set causing frictional noise and vibration between the door glass and the weatherstrip. In this study, high damping EPDM (ethylene-propylene-diene monomer)/PP (polypropylene) thermoplastic vulcanizates (TPV) were investigated by varying EPDM/PP ratio and ENB (ethylidene norbornene) fraction in EPDM. Viscoelastic properties of TPV materials were characterized by assuming that the material damping is directly related to the viscoelasticity. The optimum material damping factor (tanδ peak 0.611) was achieved with low PP ratio (14 wt%) and high ENB fraction (8.9 wt%), which was increased by 140% compared to the reference (tanδ 0.254). The improved damping is believed due to high fraction of flexible EPDM chains and higher interfacial slippage area of EPDM particles generated by increasing ENB fraction in EPDM. The stick-slip test was conducted to characterize frictional noise and vibration of the TPV weatherstrip. With improved TPV material damping, the acceleration peak of frictional vibration decreased by about 57.9%. This finding can not only improve BSR performance of electric vehicles by designing material damping of weatherstrips but also contribute to various structural applications such as urban air mobility or aircrafts, which require lightweight and high damping properties.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1691-1707
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• 2022

Cheonji Lake, the caldera of Baekdu Mountain, located on the border of the Korean Peninsula and China, alternates between melting and freezing seasonally. There is a magma chamber beneath Cheonji, and variations in the magma chamber cause volcanic antecedents such as changes in the temperature and water pressure of hot spring water. Consequently, there is an abnormal region in Cheonji where ice melts quicker than in other areas, freezes late even during the freezing period, and has a high-temperature water surface. The abnormal area is a discharge region for hot spring water, and its ice gradient may be used to monitor volcanic activity. However, due to geographical, political and spatial issues, periodic observation of abnormal regions of Cheonji is limited. In this study, the degree of ice change in the optimal region was quantified using a Landsat -5/-7/-8 optical satellite image and a Modified U-Net regression model. From January 22, 1985 to December 8, 2020, the Visible and Near Infrared (VNIR) band of 83 Landsat images including anomalous regions was utilized. Using the relative spectral reflectance of water and ice in the VNIR band, unique data were generated for quantitative ice variability monitoring. To preserve as much information as possible from the visible and near-infrared bands, ice gradient was noticed by applying it to U-Net with two encoders, achieving good prediction accuracy with a Root Mean Square Error (RMSE) of 140 and a correlation value of 0.9968. Since the ice change value can be seen with high precision from Landsat images using Modified U-Net in the future may be utilized as one of the methods to monitor Baekdu Mountain's volcanic activity, and a more specific volcano monitoring system can be built.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.609-625
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• 2022

Validation of satellite data products is critical for subsequent analysis that is based on the data. Particularly, performance of ocean color products in turbid and shallow near-land ocean areas has been questioned for long time for its difficulty that stems from the complex optical environment with varying distribution of water constituents. Furthermore, validation with ship-based or station-based measurements has also exhibited clear limitation in its spatial scale that is not compatible with that of satellite data. This study firstly performed validation of major GOCI-II products such as remote sensing reflectance, chlorophyll-a concentration, suspended particulate matter, and colored dissolved organic matter, using the in-situ measurements collected from ship-based field campaign. Secondly, this study also presents preliminary analysis on the use of drone images for product validation. Multispectral images were acquired from a MicaSense RedEdge camera onboard a UAV to compensate for the significant scale difference between the ship-based measurements and the satellite data. Variation of water radiance in terms of camera altitude was analyzed for future application of drone images for validation. Validation conducted with a limited number of samples showed that GOCI-II remote sensing reflectance at 555 nm is overestimated more than 30%, and chlorophyll-a and colored dissolved organic matter products exhibited little correlation with in-situ measurements. Suspended particulate matter showed moderate correlation with in-situ measurements (R²~0.6), with approximately 20% uncertainty.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Journal of Food Hygiene and Safety
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• v.39 no.4
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• pp.304-311
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• 2024

The present study aimed to compare the accuracy of the compact dry and culture plate methods for natural flora (yeasts/molds, coliforms, and total bacterial count) and artificial inoculum (Escherichia coli) in dosirak (meat-based), meal kits (seafood-based), and Doenjang (traditional food). Compact dry TC, EC, CF, and YMR were compared with culture plate methods using a suitable medium for each bacterium. The total bacterial count, coliforms, yeasts/molds, and E. coli were assessed with 3M Petrifilm (aerobic bacterial, coliform, yeast/mold, and E. coli count plates) using culture plate methods. Analysis of the recovery rates of target microorganisms in the three food samples showed that the compact dry method for total bacteria, coliforms, and E. coli exhibited recovery capabilities equivalent to those of the culture plate and Petrifilm methods, with no significant differences (P>0.05). Overall, compact dry TC, CF, and EC showed a good correlation between the methods used in this study, indicating rapid and convenient microbial enumeration by saving time and requiring less space.

• The Korean Journal of Nuclear Medicine
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• v.37 no.6
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• pp.382-392
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• 2003

Purpose: Uptakes of Tc-99m MIBI (MIBI) and Tc-99m tetrofosmin (tetrofosmin) in human non-small cell lung cancer A549, multidrug-resistance associated protein (MRP) expressing cell, were investigated in vitro and in vivo. Materials and Methods: Western blot analysis and immunohistochemistry were used for detection of MRP in A549 cells with anti-MRPr1 antibody. Cellular uptakes of two tracers were evaluated at $100{\mu}M$ of verapamil (Vrp), $50{\mu}M$ of cyclosporin A (CsA) and $25{\mu}M$ of butoxysulfoximide (BSO) after incubation with MIBI and tetrofosmin for 30 and 50 min at $37^{\circ}C$, using single cell suspensions at $1{\times}10^6cells/ml$. Radioactivities in supernatants and pellets were measured with gamma well counter. A549 cells were inoculated in each flanks of 24 nude mice. Group 1 (Gr1) and Gr3 mice were treated with only MIBI or tetrofosmin, and Gr2 and Gr4 mice were treated with 70mg/kg of CsA i.p. for 1 hour before injection of 370KBq of MIBI or tetrofosmin. Mice were sacrificed at 10, 60 and 240 min. Radioactivities of organs and tumors were expressed as percentage injected dose per gram of tissue (%ID/gm). Results: Western blot analysis of the A549 cells detected expression of MRPr1 (190 kDa) and immunohistochemical staining of tumor tissue for MRPr1 revealed brownish staining in cell membrane but not P-gp. Upon incubating A549 cells for 60 min with MIBI and tetrofosmin, cellular uptake of MIBI was higher than that of tetrofosmin. Coincubation with modulators resulted in an increase in cellular uptakes of MIBI and tetrofosmin. Percentage increase of MIBI was higher than that of tetrofosmin with Vrp by 623% and 427%, CsA by 753% and 629% and BSO by 219% and 140%, respectively. There was no significant difference in tumoral uptakes of MIBI and tetrofosmin between Gr1 and Gr3. Percentage increases in MIBI (114% at 10 min, 257% at 60 min, 396% at 240 min) and tetrofosmin uptake (110% at 10 min, 205% at 60 min, 410% at 240 min) were progressively higher by the time up to 240 min with CsA. Conclusion: These results indicate that MIBI and tetrofosmin are suitable tracers for imaging MRP-mediated drug resistance in A549 tumors. MIBI may be a better tracer than tetrofosmin for evaluating MRP reversal effect of modulators.