• The Journal of Engineering Geology
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• v.32 no.1
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• pp.27-40
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• 2022

Every year landslides cause serious casualties and property damages around the world. As the accurate prediction of landslides is important to reduce the fatalities and economic losses, various approaches have been developed to predict them. Prediction methods can be divided into landslide susceptibility analysis, landslide hazard analysis and landslide risk analysis according to the type of the conditioning factors, the predicted level of the landslide dangers, and whether the expected consequence cased by landslides were considered. Landslide susceptibility analyses are mainly based on the available landslide data and consequently, they predict the likelihood of landslide occurrence by considering factors that can induce landslides and analyzing the spatial distribution of these factors. Various qualitative and quantitative analysis techniques have been applied to landslide susceptibility analysis. Recently, quantitative susceptibility analyses have predominantly employed the physically based model due to high predictive capacity. This is because the physically based approaches use physical slope model to analyze slope stability regardless of prior landslide occurrence. This approach can also reproduce the physical processes governing landslide occurrence. This review examines physically based landslide susceptibility analysis approaches.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6B
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• pp.503-512
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• 2009

Watershed models, which are a tool for water cycle mechanism, are classified as the distributed model and the lumped model. Currently, the distributed models have been more widely used than lumped model for many researches and applications. The lumped model estimates the parameters in the conceptual and empirical sense, on the other hand, in the case of distributed model the first-guess value is estimated from the grid-based watershed characteristics and rainfall data. Therefore, the distributed model needs more detailed parameter adjustment in its calibration and also one should precisely understand the model parameters' characteristics and sensitivity. This study uses Jungnang basin as a study area and $Vflo^{TM}$ model, which is a physics-based distributed hydrologic model, is used to analyze its parameters' sensitivity. To begin with, 100 years frequency-design rainfall is derived from Huff's method for rainfall duration of 6 hours, then the discharge is simulated using the calibrated parameters of $Vflo^{TM}$ model. As a result, hydraulic conductivity and overland's roughness have an effect on runoff depth and peak discharge, respectively, while channel's roughness have influence on travel time and peak discharge.

• Analytical Science and Technology
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• v.19 no.3
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• pp.239-243
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• 2006

This method is used for the determination of itraconazole in human plasma by liquid-liquid extraction and high performance liquid chromatography. Felodipine was used as an internal standard. After extraction of the plasma with diethyl ether, the centrifuged upper layer was then transferred. The supernantant was evaporated and then reconsituted with mobile phase. The mobile phase was composed of 10 mM ammonium acetate adjusted to pH 7 by phosphoric acid with a flow rate of 0.2 mL/min. A C18 reversed-phase column with a pre-column was used as the analytial column. Linear detection responses were obtained for itraconzole concentration range for 2~1,000 ng/mL. The correlation coefficient of linear regression($R^2$) was 0.9991, limit of quantification (LOQ) was 2 ng/mL, reproducibility was less than 10.8 %, and accuracy was 97.2~108.2%. This method has been successfully applied to the pharmacokinetic study of itraconazole in human plasma.

• Analytical Science and Technology
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• v.21 no.1
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• pp.34-40
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• 2008

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) method has been developed and validated for the quantitative determination of lercanidipine in human plasma. After addition of internal standard (amlodipine), plasma was precipitated with acetonitrile and the supernatant was evaporated. The residues were dissolved in 50 % acetonitrile and analyzed by LC-MS/MS. Using MS/MS with multiple reaction monitoring(MRM) mode, lercanindipine were selectively detected without severe interference from human plasma. The standard calibration curve for lercanidipine was linear (r = 0.9994) over the concentration range 0.05-20.0 ng/mL in human plasma. The intra- and inter-day precision over the concentration range of lercanidipine was lower than 11.7 % (correlation of variance, CV), and accuracy was between 94.4-114.8 %. This method has been successfully applied to the pharmacokinetic study of lercanidipine in human plasma.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.205-218
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• 2022

Recently, digital transformation in manufacturing has been accelerating. It results in that the data collection technologies from the shop-floor is becoming important. These approaches focus primarily on obtaining specific manufacturing data using various sensors and communication technologies. In order to expand the channel of field data collection, this study proposes a method to automatically collect manufacturing data based on vision-based artificial intelligence. This is to analyze real-time image information with the object detection and tracking technologies and to obtain manufacturing data. The research team collects object motion information for each frame by applying YOLO (You Only Look Once) and DeepSORT as object detection and tracking algorithms. Thereafter, the motion information is converted into two pieces of manufacturing data (production performance and time) through post-processing. A dynamically moving factory model is created to obtain training data for deep learning. In addition, operating scenarios are proposed to reproduce the shop-floor situation in the real world. The operating scenario assumes a flow-shop consisting of six facilities. As a result of collecting manufacturing data according to the operating scenarios, the accuracy was 96.3%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.331-337
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• 2023

In this paper, we propose a deep learning model to detect lumbar 3 (L3) CT images to determine the occurrence and degree of sarcopenia. In addition, we would like to propose an optimization technique that uses oversampling ratio and class weight as design parameters to address the problem of performance degradation due to data imbalance between L3 level and non-L3 level portions of CT data. In order to train and test the model, a total of 150 whole-body CT images of 104 prostate cancer patients and 46 bladder cancer patients who visited Gangneung Asan Medical Center were used. The deep learning model used ResNet50, and the design parameters of the optimization technique were selected as six types of model hyperparameters, data augmentation ratio, and class weight. It was confirmed that the proposed optimization-based L3 level extraction model reduced the median L3 error by about 1.0 slices compared to the control model (a model that optimized only 5 types of hyperparameters). Through the results of this study, accurate L3 slice detection was possible, and additionally, we were able to present the possibility of effectively solving the data imbalance problem through oversampling through data augmentation and class weight adjustment.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1225-1234
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• 2023

The intensity of crop water stress caused by moisture deficit is affected by growth and heat conditions. For more accurate detection of crop water stress state using remote sensing techniques, it is necessary to select vegetation indices sensitive to crop response and to understand their changes considering not only soil moisture deficit but also heat conditions. In this study, we measured the MERIS terrestrial chlorophyll index (MTCI) and chlorophyll/carotenoid index (CCI) under drought and heat wave conditions. The MTCI, sensitive to chlorophyll concentration, sensitively decreased on non-irrigation conditions and the degree was larger with heat waves. On the other hand, the CCI, correlated with photosynthesis efficiency, showed less sensitivity to water deficit but had decreased significantly with heat waves. After re-irrigation, the MTCI was increased than before damage and CCI became more sensitive to heat stress. These results are expected to contribute to evaluating the intensity of crop water stress through remote sensing techniques.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1427-1435
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• 2023

Vegetation indices based on selected wavelength reflectance measurements are used to represent crop growth and physiological conditions. However, the anisotropic properties of the crop canopy surface can govern spectral reflectance and vegetation indices. In this study, we applied an ensemble of bidirectional reflectance distribution function (BRDF) models to high-resolution Sentinel-2 satellite imagery and compared the differences between correction results before and after reflectance. In the red and near-infrared (NIR) band reflectance images, BRDF-corrected outlier values appeared in certain urban and paddy fields of farmland areas and forest shadow areas. These effects were equally observed when calculating the normalized difference vegetation index (NDVI) and 2-band enhanced vegetation index (EVI2). Furthermore, the outlier values in corrected NIR band were shown in pixels shadowed by mountain terrain. These results are expected to contribute to the development and improvement of BRDF models in high-resolution satellite images.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1529-1539
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• 2023

The data from Geostationary Ocean Color Imager-II (GOCI-II), which observes the color of the sea to monitor marine environments, undergoes various correction processes in the ground station system, producing data from Raw to Level-2 (L2). Quality issues arising at each processing stage accumulate step by step, leading to an amplification of errors in the satellite data. To address this, improvements were made to the GOCI-II ground station system to measure potential optical quality and geolocation accuracy errors in the Level-1A/B (L1A/B) data. A newly established Radiometric and Geometric Performance Assessment Module (RGPAM) now measures five optical quality factors and four geolocation accuracy factors in near real-time. Testing with GOCI-II data has shown that RGPAM's functions, including data processing, display and download of measurement results, work well. The performance metrics obtained through RGPAM are expected to serve as foundational data for real-time radiometric correction model enhancements, assessment of L1 data quality consistency, and the development of reprocessing strategies to address identified issues related to the GOCI-II detector's sensitivity degradation.

• Clean Technology
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• v.29 no.4
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• pp.244-248
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• 2023

Recently, displays play an important role in quickly delivering a lot of information. Research is underway to reproduce various colors close to natural colors. In particular, research is being conducted on the light emitting structure of displays as a method of expressing accurate and rich colors. Due to the advancement of technology and the miniaturization of devices, the need for small but high visibility displays with high efficiency in energy consumption continues to increase. Efforts are being made in various ways to improve OLED efficiency, such as improving carrier injection, structuring devices that can efficiently recombine electrons and holes in a numerical balance, and developing materials with high luminous efficiency. In this study, the electrical and optical properties of the seven-layer stacked structure rear-light emitting blue OLED device were analyzed. 4,4'-Bis(carazol-9-yl)biphenyl:Ir(difppy)2(pic), a blue light emitting material that is easy to manufacture and can be highly efficient and brightened, was used. OLED device manufacturing was performed via the in-situ method in a high vacuum state of 5×10-8 Torr or less using a Sunicel Plus 200 system. The experiment was conducted with a seven-layer structure in which an electron or hole blocking layer (EBL or HBL) was added to a five-layer structure in which an electron or hole injection layer (EIL or HIL) or an electron or hole transport layer (ETL or HTL) was added. Analysis of the electrical and optical properties showed that the device that prevented color diffusion by inserting an EBL layer and a HBL layer showed excellent color purity. The results of this study are expected to greatly contribute to the R&D foundation and practical use of blue OLED display devices.