• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1109-1124
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• 2022

This study proposes a marine debris monitoring methods using satellite and drone multispectral images. A multi-layer perceptron (MLP) model was applied to detect marine debris using Sentinel-2 satellite image. And for the detection of marine debris using drone multispectral images, performance evaluation and comparison of U-Net, DeepLabv3+ (ResNet50) and DeepLabv3+ (Inceptionv3) among deep learning models were performed (mIoU 0.68). As a result of marine debris detection using satellite image, the F1-Score was 0.97. Marine debris detection using drone multispectral images was performed on vegetative debris and plastics. As a result of detection, when DeepLabv3+ (Inceptionv3) was used, the most model accuracy, mean intersection over union (mIoU), was 0.68. Vegetative debris showed an F1-Score of 0.93 and IoU of 0.86, while plastics showed low performance with an F1-Score of 0.5 and IoU of 0.33. However, the F1-Score of the spectral index applied to generate plastic mask images was 0.81, which was higher than the plastics detection performance of DeepLabv3+ (Inceptionv3), and it was confirmed that plastics monitoring using the spectral index was possible. The marine debris monitoring technique proposed in this study can be used to establish a plan for marine debris collection and treatment as well as to provide quantitative data on marine debris generation.

• Korean Journal of Remote Sensing
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• v.38 no.6_4
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• pp.1925-1934
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• 2022

Monitoring nuclear activity for inaccessible areas using remote sensing technology is essential for nuclear non-proliferation. In recent years, deep learning has been actively used to detect nuclear-activity-related small objects. However, high-resolution satellite imagery containing small objects can result in class imbalance. As a result, there is a performance degradation problem in detecting small objects. Therefore, this study aims to improve detection accuracy by analyzing the effect of the ratio of small objects related to nuclear activity in the input data for the performance of the deep learning model. To this end, six case datasets with different ratios of small object pixels were generated and a U-Net model was trained for each case. Following that, each trained model was evaluated quantitatively and qualitatively using a test dataset containing various types of small object classes. The results of this study confirm that when the ratio of object pixels in the input image is adjusted, small objects related to nuclear activity can be detected efficiently. This study suggests that the performance of deep learning can be improved by adjusting the object pixel ratio of input data in the training dataset.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1195-1204
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• 2021

Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m³/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.390-400
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• 2022

University Identity can effectively convey the core values for which schools aim by establishing university identity and integrating one unique image. Therefore, most universities are actively implementing promotional strategies such as newly defining university identity or releasing cultural products. Recently, university brands have been continuously exposed and differentiated through SNS such as Instagram, YouTube, and Facebook as well as existing advertisements and homepages. This study analyzes the identities of the top 80 universities in Korea and China, by referring to the rankings of Asian universities in the 2021 QS World University Rankings, and addresses differences in terms of design shape, number of colors, and use of English. Moreover, 'Cohen's Kappa' consistency analysis was applied to secure data accuracy by analyzing the difference in visual expression of university identity between the two countries through quantification and cross-analysis of visualized university identity design of Korean and Chinese universities. As a result of the study, it is creative, irregular, and has a lot of use of blue, red, and green, and most of them can be seen in less than two colors. In addition, it turns out that word marks and abstract forms of expression are used for university identity design. This study can present implications as effective basic data for internationalizing universities and creating differentiated university identity designs in the future.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.1
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• pp.21-30
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• 2022

Objectives: The purpose of this study is to suggest an optimal method by comparing the analysis methods of work environment measurement datasets including left-censored data where one or more measurements are below the limit of detection (LOD). Methods: A computer program was used to generate left-censored datasets for various combinations of censoring rate (1% to 90%) and sample size (30 to 300). For the analysis of the censored data, the simple substitution method (LOD/2), β-substitution method, maximum likelihood estimation (MLE) method, Bayesian method, and regression on order statistics (ROS)were all compared. Each method was used to estimate four parameters of the log-normal distribution: (1) geometric mean (GM), (2) geometric standard deviation (GSD), (3) 95th percentile (X95), and (4) arithmetic mean (AM) for the censored dataset. The performance of each method was evaluated using relative bias and relative root mean squared error (rMSE). Results: In the case of the largest sample size (n=300), when the censoring rate was less than 40%, the relative bias and rMSE were small for all five methods. When the censoring rate was large (70%, 90%), the simple substitution method was inappropriate because the relative bias was the largest, regardless of the sample size. When the sample size was small and the censoring rate was large, the Bayesian method, the β-substitution method, and the MLE method showed the smallest relative bias. Conclusions: The accuracy and precision of all methods tended to increase as the sample size was larger and the censoring rate was smaller. The simple substitution method was inappropriate when the censoring rate was high, and the β-substitution method, MLE method, and Bayesian method can be widely applied.

• Journal of Adhesion and Interface
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• v.23 no.3
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• pp.87-93
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• 2022

To analyze the interfacial property between the fiber and the matrix, work of adhesion was used generally that was calculated by surface energies. In this paper, it was determined what types of contact angle measurement methods were more accurate between static and dynamic contact angle measurements. 4 types of glass fiber and epoxy resin were used each other to measure the contact angle. The contact angle was measured using two types, static and dynamic contact angle methods, and work of adhesion, W_a was calculated to compare interfacial properties. The interfacial property was evaluated using microdroplet pull-out test. Generally, the interfacial property was proportional to work of adhesion. In the case of static contact angle, however, work of adhesion was not consistent with interfacial property. It is because that dynamic contact angle measurement comparing to static contact angle could delete the error due to microdroplet size to minimize the surface area as well as the meniscus measuring error.

• Journal of Korea Water Resources Association
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• v.55 no.5
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• pp.383-392
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• 2022

The restoration of the Nakdong River estuary is one of the most important projects of the Ministry of Environment, Republic of Korea. A real-scale experiment of gate operation was executed from 2019 to 2020, and a pilot operation was performed in 2021. The gate of Nakdong River Estuary Barrier (NEB) is supposed to be continuously opened based on the experiment results. Many critical decisions should be made immediately during the experiment based on the real-time measured data and numerical analysis considering the seawater inflows. The decision-making sequence was made systematically with the accurate estimation of seawater inflow. The estimation of seawater inflow is the main research objective and the equations of seawater inflow were developed, reflecting the structural characteristics of NEB. The inflow equations were developed in two forms, overflow and underflow. ADCP (Acoustic Doppler Current Profiler) was used to measure seawater inflow, check the accuracy of the developed equations, and derive the flow coefficient. The comparison error of the developed equations was about 3% compared to the measured data.

• The Korea Journal of Herbology
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• v.28 no.6
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• pp.47-51
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• 2013

Objectives : The Illicii Veri Fructus was not only traditional medicine but also food in Asia. The aim of this study was selection of optimum solvent in the fruit of Illicii Veri Fructus because an appropriate solvent affect a medicinal effect. Methods : Illicii Veri Fructus was carried out ultrasonic-assisted extraction as various solvents. Two main compounds, p-anisaldehyde and anethole, were successfully analyzed by high performance liquid chromatography-photodiode array detector (HPLC-PDA) and carried out method validation according to ICH guideline. The optimum solvent selected by comparing with yields of two main ingredients. Results : The p-anisaldehyde and anethole were detected at approximately 8.0 min and 19.8 min, respectively. It was all below 5.0% that RSD of retention time and peak area for two main peaks. Calibration curves of two compounds were good linearity as $R^2$ >0.9999. All of the precisions and accuracy were good intra-day and inter-day as below 5.0% RSD. Limited of detection (LOD) of p-anisaldehyde and anethole were analyzed as $0.134{\mu}g/mL$ and $4.286{\mu}g$, respectively. Limited of quantification (LOQ) of two compounds were $0.407{\mu}g$ and $12.989{\mu}g$, respectively. As a result of this study, p-anisladehyde was detected as 0.209 ~ 0.467%, however anethole was not detected in the distilled water. Conclusions : Anethole was main component as 5.329 ~ 6.815% except for water extraction. Methanol extraction among various solvents was detected the highest contents of p-anisaldehyde and anethole as 0.467(${\pm}0.008$)% and 6.815(${\pm}0.220$)%, respectively.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.275-292
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• 2022

In order to detect climate changes using satellite imagery, the GCOS (Global Climate Observing System) defines requirements such as spatio-temporal resolution, stability by the time change, and uncertainty. Due to limitation of GK-2A sensor performance, the level-2 products can not satisfy the requirement, especially for spatial resolution. In this paper, we found the optimal pan-sharpening algorithm for GK-2A products. The six pan-sharpening methods included in CS (Component Substitution), MRA (Multi-Resolution Analysis), VO (Variational Optimization), and DL (Deep Learning) were used. In the case of DL, the synthesis property based method was used to generate training dataset. The process of synthesis property is that pan-sharpening model is applied with Pan (Panchromatic) and MS (Multispectral) images with reduced spatial resolution, and fused image is compared with the original MS image. In the synthesis property based method, fused image with desire level for user can be produced only when the geometric characteristics between the PAN with reduced spatial resolution and MS image are similar. However, since the dissimilarity exists, RD (Random Down-sampling) was additionally used as a way to minimize it. Among the pan-sharpening methods, PSGAN was applied with RD (PSGAN_RD). The fused images are qualitatively and quantitatively validated with consistency property and the synthesis property. As validation result, the GSA algorithm performs well in the evaluation index representing spatial characteristics. In the case of spectral characteristics, the PSGAN_RD has the best accuracy with the original MS image. Therefore, in consideration of spatial and spectral characteristics of fused image, we found that PSGAN_RD is suitable for GK-2A products.

• Wind and Structures
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• v.34 no.1
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• pp.127-136
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• 2022

In this work a multi-fidelity non-intrusive polynomial chaos (MF-NIPC) has been applied to a structural wind engineering problem in architectural design for the first time. In architectural design it is important to design structures that are safe in a range of wind directions and speeds. For this reason, the computational models used to design buildings and bridges must account for the uncertainties associated with the interaction between the structure and wind. In order to use the numerical simulations for the design, the numerical models must be validated by experi-mental data, and uncertainties contained in the experiments should also be taken into account. Uncertainty Quantifi-cation has been increasingly used for CFD simulations to consider such uncertainties. Typically, CFD simulations are computationally expensive, motivating the increased interest in multi-fidelity methods due to their ability to lev-erage limited data sets of high-fidelity data with evaluations of more computationally inexpensive models. Previous-ly, the multi-fidelity framework has been applied to CFD simulations for the purposes of optimization, rather than for the statistical assessment of candidate design. In this paper MF-NIPC method is applied to flow around a rectan-gular 5:1 cylinder, which has been thoroughly investigated for architectural design. The purpose of UQ is validation of numerical simulation results with experimental data, therefore the radius of curvature of the rectangular cylinder corners and the angle of attack are considered to be random variables, which are known to contain uncertainties when wind tunnel tests are carried out. Computational Fluid Dynamics (CFD) simulations are solved by a solver that employs the Finite Element Method (FEM) for two turbulence modeling approaches of the incompressible Navier-Stokes equations: Unsteady Reynolds Averaged Navier Stokes (URANS) and the Large Eddy simulation (LES). The results of the uncertainty analysis with CFD are compared to experimental data in terms of time-averaged pressure coefficients and bulk parameters. In addition, the accuracy and efficiency of the multi-fidelity framework is demonstrated through a comparison with the results of the high-fidelity model.