• Biomolecules & Therapeutics
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• v.30 no.5
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• pp.427-434
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• 2022

The drug repurposing strategy has been applied to the development of emergency COVID-19 therapeutic medicines. Current drug repurposing approaches have been directed against RNA polymerases and viral proteases. Recently, we found that the inhibition of the interaction between the SARS-CoV-2 structural nucleocapsid (N) and spike (S) proteins decreased viral replication. In this study, drug repurposing candidates were screened by in silico molecular docking simulation with the SARS-CoV-2 structural N protein. In the ChEMBL database, 1994 FDA-approved drugs were selected for the in silico virtual screening against the N terminal domain (NTD) of the SARS-CoV-2 N protein. The tyrosine 109 residue in the NTD of the N protein was used as the center of the ligand binding grid for the docking simulation. In plaque forming assays performed with SARS-CoV-2 infected Vero E6 cells, atovaquone, abiraterone acetate, and digoxin exhibited a tendency to reduce the size of the viral plagues without affecting the plaque numbers. Abiraterone acetate significantly decreased the accumulation of viral particles in the cell culture supernatants in a concentration-dependent manner. In addition, abiraterone acetate significantly decreased the production of N protein and S protein in the SARS-CoV-2-infected Vero E6 cells. In conclusion, abiraterone acetate has therapeutic potential to inhibit the viral replication of SARS-CoV-2.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.2
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• pp.135-145
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• 2023

Developing high-fidelity Computational Fluid Dynamics (CFD) simulation methods used to evaluate the airwake characteristics along a flight deck of a large ship, the various kind of data such as actual ship measurement and wind tunnel results are required to verify the accuracy of CFD simulation. Inflow velocity profile at the bow, local unsteady flow field data around the flight deck, and highly reliable wind tunnel data which were measured after reviewing Atmospheric Boundary Layer (ABL) simulation and Reynolds Number effects were also used to determine the key parameters such as turbulence model, time resolution and accuracy, grid resolution and type, inflow condition, domain size, simulation length, and so on in STAR CCM⁺. Velocity ratio and turbulent intensity difference between Full-scale CFD and actual ship measurement at the measurement points show less than 2% and 1.7% respectively. And differences in velocity ratio and turbulence intensity between wind tunnel test and small-scale CFD are both less than 2.2%. Based upon this fact, the selected parameters in CFD simulation are highly reliable for a specific wind condition.

• Journal of Sensor Science and Technology
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• v.32 no.2
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• pp.110-117
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• 2023

Reconstructing underwater geometry in real time with forward-looking sonar is critical for applications such as localization, mapping, and path planning. Geometrical data must be repeatedly calculated and overwritten in real time because the reliability of the acoustic data is affected by various factors. Moreover, scattering of signal data during the coordinate conversion process may lead to geometrical errors, which lowers the accuracy of the information obtained by the sensor system. In this study, we propose a three-step data processing method with low computational cost for real-time operation. First, the number of data points to be interpolated is determined with respect to the distance between each point and the size of the data grid in a Cartesian coordinate system. Then, the data are processed with a nonlinear interpolation so that they exhibit linear properties in the coordinate system. Finally, the data are transformed based on variations in the position and orientation of the sonar over time. The results of an evaluation of our proposed approach in a simulation show that the nonlinear interpolation operation constructed a continuous underwater geometry dataset with low geometrical error.

• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.182-189
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• 2023

Depth estimation is a key technology in 3D map generation for autonomous driving of vehicles, robots, and drones. The existing sensor-based method has high accuracy but is expensive and has low resolution, while the camera-based method is more affordable with higher resolution. In this study, we propose self-attention-based unsupervised monocular depth estimation for UAV camera system. Self-Attention operation is applied to the network to improve the global feature extraction performance. In addition, we reduce the weight size of the self-attention operation for a low computational amount. The estimated depth and camera pose are transformed into point cloud. The point cloud is mapped into 3D map using the occupancy grid of Octree structure. The proposed network is evaluated using synthesized images and depth sequences from the Mid-Air dataset. Our network demonstrates a 7.69% reduction in error compared to prior studies.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.279-290
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• 2022

A superconducting fault current limiter(SFCL) is a power device that exploits superconducting transition to control currents and enhances the flexibility, stability and reliability of the power system within a few milliseconds. With a high phase transition speed, high critical current densities and little AC loss, high-temperature superconducting (HTS) wires are suitable for a resistive-type SFCL. However, HTS wires due to the lack of optimization research are rather inefficient to directly apply to a fault current limiter in terms of the design and capacity, for the existing method relied the characteristics. Therefore, in order to develop a suitable wire for an SFCL, it is necessary to enhance critical current uniformity, select optimal stabilizer materials and conducted research on the development of uniform stabilizer layering technology. The high temperature superconducting wires manufactured by this study get an average critical current of 804 A/12mm-width at the length of 710m; therefore, conducted research was able to secure economic performance by improving efficiency, reducing costs, and reducing size.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.4
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• pp.19-28
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• 2020

The Urban Heat Island (UHI) Effect has intensified due to urbanization and heat management at the urban level is treated as an important issue. Green space improvement projects and environmental policies are being implemented as a way to alleviate Urban Heat Islands. Several studies have been conducted to analyze the correlation between urban green areas and heat with linear regression models. However, linear regression models have limitations explaining the correlation between heat and the multitude of variables as heat is a result of a combination of non-linear factors. This study evaluated the Heat Island alleviating effects in Seoul during the summer by using a deep neural network model methodology, which has strengths in areas where it is difficult to analyze data with existing statistical analysis methods due to variable factors and a large amount of data. Wide-area data was acquired using Landsat 8. Seoul was divided into a grid (30m × 30m) and the heat island reduction variables were enter in each grid space to create a data structure that is needed for the construction of a deep neural network using ArcGIS 10.7 and Python3.7 with Keras. This deep neural network was used to analyze the correlation between land surface temperature and the variables. We confirmed that the deep neural network model has high explanatory accuracy. It was found that the cooling effect by NDVI was the greatest, and cooling effects due to the park size and green space proximity were also shown. Previous studies showed that the cooling effects related to park size was 2℃-3℃, and the proximity effect was found to lower the temperature 0.3℃-2.3℃. There is a possibility of overestimation of the results of previous studies. The results of this study can provide objective information for the justification and more effective formation of new urban green areas to alleviate the Urban Heat Island phenomenon in the future.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.6B
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• pp.531-539
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• 2011

It has been widely acknowledged that climate system associated with extreme rainfall events was difficult to understand and extreme rainfall simulation in climate model was more difficult. This study developed a new model for extracting rainfall filed associated with extreme events as a way to characterize large scale climate system. Main interests are to derive location, size and direction of the rainfall field and this study developed an algorithm to extract the above characteristics from global climate data set. This study mainly utilized specific humidity and wind vectors driven by NCEP reanalysis data to define the rainfall field. Geometric first and second moments have been extensively employed in defining the rainfall field in selected zone, and an ellipsoid based model were finally introduced. The proposed geometric moments based ellipsoid model works equally well with regularly and irregularly distributed synthetic grid data. Finally, the proposed model was applied to space-time real rainfall filed. It was found that location, size and direction of the rainfall field was successfully extracted.

• Journal of the Ergonomics Society of Korea
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• v.31 no.2
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• pp.309-317
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• 2012

Objective: The aim of this paper is to inquire into the influences form factors and control types affect a search time and comfort at list menu of full touch phone. Background: Various studies have been proceeded that are related to the optimum touch area for enhancing usability of control and legibility in mobile touch device. In the environment of list menu which is widely used to provide various information effectively, however, not only comprehensive consideration for legibility and control is to be seek but also research for control type which is to scroll a list. Method: This study executed form factor experiment to inquire into the influence that font size, height of row and fixed area affect searching time and comfort in the while information processing even if the information on the list is unsorted in alphabetical order. Among the result of form factor experiment, control type experiment was executed by selecting shortest performance time, highest legibility comfort and control comfort. Control type experiment was implemented to figure out the influence which existing flicking type, scrolling bar type, newly established button page type and button raw types affect performance time and subjective comfort depending on location of the information. Results: Font size 12pt, height of row 7mm and fixed area 15mm was shortest performance time and got highest comfort and legibility score in form factor experiment. A Button page which was newly proposed type was shortest performance time and got highest comprehensive comfort in control type experiment. Conclusion: Form factor experiment showed similar results with the study through reading a long passage of character or controlling a grid icon type. However, height of row turned out to affect not only touch area for control but also legibility by ruling space between the lines. Button page type which was newly proposed showed shortest performance time and got highest comprehensive comfort. Because Button page type needs few finger movements than other control types and implements search in the fixed form, unlikely other type which list keeps moving. Application: This study should be applied in deciding form factors and control type for scroll when designing a list menu of full touch phone.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.47 no.4
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• pp.390-402
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• 2011

A preliminary study on species composition of a Korean purse seine catch landed at cannery was conducted in April 2011. In the cannery, all tuna catch are sliding through a sorting grid panel that filters and drops fish in the buckets by size class (above 9kg, 3.4-9kg, 1.8-3.4kg, 1.4-1.8kg and below 1.4kg). In cannery processing, species sorting was made for skipjack tuna and yellowfin tuna only from catches greater than 3.4kg during filtering but not for bigeye tuna because of difficulties in species identification between bigeye tuna and yellowfin tuna under frozen state. As no species identification was carried out for catch groups less than 3.4kg in the cannery process, this study focused on sorting out skipjack tuna and yellowfin tuna from these groups and then identifying bigeye tuna from all size groups of yellowfin tuna. Using the mixture rate of species obtained from the samples taken, species composition of the landed catch was estimated. As results, cannery research showed 95% for skipjack tuna, 3% for yellowfin tuna and 2% for bigeye tuna in species composition, while vessel logbook data represented 96%, 3% and 1% for skipjack tuna, yellowfin tuna and bigeye tuna, respectively. The proportion of bigeye tuna identified in the cannery was slightly higher than shown in logbook data by 1%.