• Economic and Environmental Geology
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• v.35 no.3
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• pp.273-284
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• 2002

The Gravity-Geologic Method (GGM) was implemented for bathymetric determinations in the Drake Passage, Antarctica, using global marine Free-air Gravity Anomalies (FAGA) data sets by Sandwell and Smith (1997) and local echo sounding measurements. Of the 6548 bathymetric sounding measurements, two thirds of these points were used as control depths, while the remaining values were used as checkpoints. A density contrast of 9.0 gm/㎤ was selected based on the checkpoints predictions with changes in the density contrast assumed between the seawater and ocean bottom topographic mass. Control depths from the echo soundings were used to determine regional gravity components that were removed from FAGA to estimate the gravity effects of the bathymetry. These gravity effects were converted to bathymetry by inversion. In particular, a selective merging technique was developed to effectively combine the echo sounding depths with the GGM bathymetiy to enhance high frequency components along the shipborne sounding tracklines. For the rugged bathymetry of the research area, the GGM bathymetry shows correlation coefficients (CC) of 0.91, 0.92, and 0.85 with local shipborne sounding by KORDI, GEODAS, and a global ETOPO5 model, respectively. The enhanced GGM by selective merging shows imploved CCs of 0.948 and 0.954 with GEODAS and Smith & Sandwell (1997)'s predictions with RMS differences of 449.8 and 441.3 meters. The global marine FAGA data sets and other bathymetric models ensure that the GGM can be used in conjunction with shipborne bathymetry from echo sounding to extend the coverage into the unmapped regions, which should generate better results than simply gridding the sparse data or relying upon lower resolution global data sets such as ETOPO5.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.6
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• pp.53-60
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• 2008

The cross-sections of continuous multi-span beams sometimes suddenly increase, or become stepped, at the interior supports of continuous beams to resist high negative moments. The three-dimensional finite-element program ABAQUS (2007) was used to analytically investigate the inelastic lateral-torsional buckling behavior of stepped beams subjected to linear moment gradient and resulted in the development of design equations. The ratios of the flange thickness, flange width, and stepped length of beam are considered for the analytical parameters. Two groups of 27 cases and 36 cases, respectively, were analyzed for doubly and singly stepped beams in the inelastic buckling range. The combined effects of residual stresses and geometrical imperfection on inelastic lateral-torsional buckling of beams are considered. First, the distributions of residual stress of the cross-section is same as shown in Pi and Trahair (1995), and the initial geometric imperfection of the beam is set by central displacement equal to 0.1% of the unbraced length of beam. The new proposed equations definitely improve current design methods for the inelastic lateral-torsional buckling problem and increase efficiency in building and bridge design.

• Journal of Wetlands Research
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• v.14 no.2
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• pp.243-254
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• 2012

Recently, the use of radar rainfall data that can help tracking of the development and movement of rainfall's spatial distribution is drawing much attention in hydrology. The reliability of existing radar rainfall compared to gauge rainfall data on the ground has not yet been confirmed and so we have difficulties to apply the radar rainfall in hydrology. The radar rainfall for the applications in hydrology are adjusted merging method derived from gage. This study uses the Mean-Field Bias (MFB) and Statistical Objective Analysis (SOA) as correction methods to create adjusted grid-based radar rainfall data which can represent the temporal and spatial distribution of rainfall. This study used a storm event occurred in August 2010 for the adjustment of radar rainfall. In addition, the grid-based distributed rainfall-runoff model (Vflo), which enables more detailed examinations of spatial flux changes in the basin rather than the lumped hydrological models, has been applied to Gamcheon river basin which is a tributary of Nakdong River located in south-eastern part of the Korean peninsular and the basin area is $1005km^2$. The simulated runoff was compared with the observed runoff in an attempt to evaluate the usability of radar rainfall data and the reliability of the correction methods. The error range of peak discharge using each correction method was within 20 percent and the efficiency of the model was between 60 and 80 percent. In particular, the SOA method showed better results than MFB method. Therefore, the SOA method could be used for the adjustment of grid-based radar rainfall and the adjusted radar rainfall can be used as an input data of rainfall-runoff models.

• Applied Chemistry for Engineering
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• v.32 no.2
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• pp.168-173
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• 2021

In this study, we measured total flavonoids after extracting the total flavonoids from lemongrass which is known to have a high content of antioxidant ingredients when using microwave energy. Also, optimal extraction conditions of active ingredients using central composite design-response surface methodology (CCD-RSM) were presented. Both ultrapure water and alcohol were used as extraction solvents and the volume ratio of ethanol/ultrapure water, microwave irradiation time, and microwave irradiation power were set as independence variables. And the extraction yield and total flavonoids were measured. The optimal extraction conditions using CCD-RSM were the volume ratio of ethanol/ultrapure water = 56.3 vol.%, the microwave irradiation time = 6.1 min, and the microwave irradiation power = 574.6 W. We could also obtain expected results of yield = 17.2 wt.% and total flavonoids = 44.7 ㎍ QE/mL dw under the optimum conditions. The comprehensive satisfaction degree of this formula was 0.8562. The P-value was calculated for the yield of 0.037 and the total flavonoids content of 0.002. The average error from actual experiments established for the verification of conclusions was lower than 2.5%. Therefore, a high favorable level could be obtained when the CCD-RSM was applied to the optimization of extraction process.

• Applied Chemistry for Engineering
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• v.34 no.2
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• pp.137-143
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• 2023

In this study, natural surfactants were extracted from Medicago sativa L. The O/W emulsification processes with the extracted natural surfactants were optimized using central composite design model-response surface methodology (CCD-RSM) and a 95% confidence interval was used to confirm the reasonableness of the optimization. Herein, independent parameters were the ratio of saponins to total surfactant (P), amount of surfactant (W), and emulsification speed (R), whereas the reaction parameters were the emulsion stability index (ESI), mean droplet size (MDS), and viscosity (V). Using the multiple reaction, the optimal conditions for the ratio of saponins to total surfactant, amount of surfactant, and emulsification speed for O/W emulsification were 49.5%, 9.1 wt%, and 6559.5 rpm, respectively. Under these optimal conditions, the expected values of ESI, MDS, and V as the reaction parameters were 89.9%, 1058.4 nm, and 1522.5 cP, respectively. The values of ESI, MDS, and V from these expected values were 88.7%, 1026.4 nm, and 1486.5 cP, respectively, and the average experimental error for validating the accuracy was about 2.3 (± 0.4)%. Therefore, it was possible to design an optimization process for evaluating the O/W emulsion process with Medicago sativa L. using CCD-RSM.

• 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 Korean Society of Coastal and Ocean Engineers
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• v.33 no.6
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• pp.265-274
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• 2021

Outlier detection research in ocean data has traditionally been performed using statistical and distance-based machine learning algorithms. Recently, AI-based methods have received a lot of attention and so-called supervised learning methods that require classification information for data are mainly used. This supervised learning method requires a lot of time and costs because classification information (label) must be manually designated for all data required for learning. In this study, an autoencoder based on unsupervised learning was applied as an outlier detection to overcome this problem. For the experiment, two experiments were designed: one is univariate learning, in which only SST data was used among the observation data of Deokjeok Island and the other is multivariate learning, in which SST, air temperature, wind direction, wind speed, air pressure, and humidity were used. Period of data is 25 years from 1996 to 2020, and a pre-processing considering the characteristics of ocean data was applied to the data. An outlier detection of actual SST data was tried with a learned univariate and multivariate autoencoder. We tried to detect outliers in real SST data using trained univariate and multivariate autoencoders. To compare model performance, various outlier detection methods were applied to synthetic data with artificially inserted errors. As a result of quantitatively evaluating the performance of these methods, the multivariate/univariate accuracy was about 96%/91%, respectively, indicating that the multivariate autoencoder had better outlier detection performance. Outlier detection using an unsupervised learning-based autoencoder is expected to be used in various ways in that it can reduce subjective classification errors and cost and time required for data labeling.