• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1193-1201
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• 2020

This study aims to develop a regression model that estimates a low-flow index that can be applied to ungauged basins. A total of 30 midsized basins in South Korea use long-term runoff data provided by the National Integrated Water Management System (NIWMS) to calculate average low-flow, average minimum streamflow, and low-flow index duration and frequency. This information is used in the correlation analysis with 18 basin factors and 3 climate change factors to identify the basin area, average basin altitude, average basin slope, water system density, runoff curve number, annual evapotranspiration, and annual precipitation in the low-flow index regression model. This study evaluates the model's accuracy by using the root-mean-square error (RMSE) and the mean absolute error (MAE) for 10 ungauged, verified basins and compares them with the previous model's low-flow calculations to determine the effectiveness of the newly developed model. Comparative analysis indicates that the new regression model produces average low-flow, attributed to the consideration of varied basin and hydrologic factors during the new model's development.

• Journal of Korean Neurosurgical Society
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• v.63 no.3
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• pp.386-396
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• 2020

Objective : To generate synthetic spine magnetic resonance (MR) images from spine computed tomography (CT) using generative adversarial networks (GANs), as well as to determine the similarities between synthesized and real MR images. Methods : GANs were trained to transform spine CT image slices into spine magnetic resonance T2 weighted (MRT2) axial image slices by combining adversarial loss and voxel-wise loss. Experiments were performed using 280 pairs of lumbar spine CT scans and MRT2 images. The MRT2 images were then synthesized from 15 other spine CT scans. To evaluate whether the synthetic MR images were realistic, two radiologists, two spine surgeons, and two residents blindly classified the real and synthetic MRT2 images. Two experienced radiologists then evaluated the similarities between subdivisions of the real and synthetic MRT2 images. Quantitative analysis of the synthetic MRT2 images was performed using the mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). Results : The mean overall similarity of the synthetic MRT2 images evaluated by radiologists was 80.2%. In the blind classification of the real MRT2 images, the failure rate ranged from 0% to 40%. The MAE value of each image ranged from 13.75 to 34.24 pixels (mean, 21.19 pixels), and the PSNR of each image ranged from 61.96 to 68.16 dB (mean, 64.92 dB). Conclusion : This was the first study to apply GANs to synthesize spine MR images from CT images. Despite the small dataset of 280 pairs, the synthetic MR images were relatively well implemented. Synthesis of medical images using GANs is a new paradigm of artificial intelligence application in medical imaging. We expect that synthesis of MR images from spine CT images using GANs will improve the diagnostic usefulness of CT. To better inform the clinical applications of this technique, further studies are needed involving a large dataset, a variety of pathologies, and other MR sequence of the lumbar spine.

• Journal of the Korean earth science society
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• v.37 no.7
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• pp.420-433
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• 2016

The purpose of this study is to improve the calibration matrixes of 2-D and 3-D convective rainfall rates (CRR) using the brightness temperature of the infrared $10.8{\mu}m$ channel (IR), the difference of brightness temperatures between infrared $10.8{\mu}m$ and vapor $6.7{\mu}m$ channels (IR-WV), and the normalized reflectance of the visible channel (VIS) from the COMS satellite and rainfall rate from the weather radar for the period of 75 rainy days from April 22, 2011 to October 22, 2011 in Korea. Especially, the rainfall rate data of the weather radar are used to validate the new 2-D and 3-DCRR calibration matrixes suitable for the Korean peninsula for the period of 24 rainy days in 2011. The 2D and 3D calibration matrixes provide the basic and maximum CRR values ($mm\;h^{-1}$) by multiplying the rain probability matrix, which is calculated by using the number of rainy and no-rainy pixels with associated 2-D (IR, IR-WV) and 3-D (IR, IR-WV, VIS) matrixes, by the mean and maximum rainfall rate matrixes, respectively, which is calculated by dividing the accumulated rainfall rate by the number of rainy pixels and by the product of the maximum rain rate for the calibration period by the number of rain occurrences. Finally, new 2-D and 3-D CRR calibration matrixes are obtained experimentally from the regression analysis of both basic and maximum rainfall rate matrixes. As a result, an area of rainfall rate more than 10 mm/h is magnified in the new ones as well as CRR is shown in lower class ranges in matrixes between IR brightness temperature and IR-WV brightness temperature difference than the existing ones. Accuracy and categorical statistics are computed for the data of CRR events occurred during the given period. The mean error (ME), mean absolute error (MAE), and root mean squire error (RMSE) in new 2-D and 3-D CRR calibrations led to smaller than in the existing ones, where false alarm ratio had decreased, probability of detection had increased a bit, and critical success index scores had improved. To take into account the strong rainfall rate in the weather events such as thunderstorms and typhoon, a moisture correction factor is corrected. This factor is defined as the product of the total precipitable waterby the relative humidity (PW RH), a mean value between surface and 500 hPa level, obtained from a numerical model or the COMS retrieval data. In this study, when the IR cloud top brightness temperature is lower than 210 K and the relative humidity is greater than 40%, the moisture correction factor is empirically scaled from 1.0 to 2.0 basing on PW RH values. Consequently, in applying to this factor in new 2D and 2D CRR calibrations, the ME, MAE, and RMSE are smaller than the new ones.

• Journal of Korean Society on Water Environment
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• v.35 no.6
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• pp.580-588
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• 2019

The flow of the middle and downstream of the Yeongsan River is stagnant by two weirs of Seungchon and Juksan and the estuary dam and maintained in freshwater. In this study, the Environmental Fluid Dynamics Code-National Institute of Environment Research(EFDC-NIER) model was applied to the Yeongsan River to simulate water flow, temperature, and salinity stratification. The EFDC-NIER model is an improved model which can simulate multi-functional weirs operation, multiple algal species, and the vertical movement mechanism of algal based on the EFDC model. The simulation results for the water level, water temperature, velocity, and salinity reproduced the observed values well. The mean absolute error(MAE) of the model calibration in the annual variations of the water level was 0.1-0.3 m, water temperature was 0.8-1.7 ℃, velocity was 4.5-7.1 cm/sec, and salinity was 1.5 psu, respectively. In the case of scenario simulation for the full opening of the estuary dam, the water level of the estuary dam was directly impacted by the tide so it was predicted to rise - 1.35 m to 0.2 m on average sea level. The velocity was also predicted to increase from 2.7 cm/sec to 50.8 cm/sec, and the flow rate to increase from 53 ㎥/sec to 5,322 ㎥/sec.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.7
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• pp.821-827
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• 2014

In this study, an experimental study was performed to predict the two-phase frictional pressure drop of FC-72 in parallel micro-channels. The parallel micro-channels consist of 15 channels with depth 0.2 mm, width 0.45 mm and length 60 mm. And tests were performed in the ranges of mass fluxes from 152.2 to $584.2kg/m^2s$ and heat fluxes from 7.5 to $28.3kW/m^2$. The experimental data was compared and analyzed with existing correlations to predict the pressure drop. The existing methods to predict the pressure drop used the homogeneous model and the separated model. In this study, the new correlation was proposed by modified existing correlation using the separated model, and the new correlation predicted consequently with the experimental data within MAE of 9.6%.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1417-1426
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• 2007

As one of schemes to protect multimedia content. it is the selective encryption scheme to encrypt partially multimedia content. Compared AES(advanced encryption standard) of traditional encryption, the selective encryption scheme provides low security but is applicable to applications of multimedia content not to require high secrecy. In this paper, we improve the image scrambling scheme proposed by Van De Ville which scrambles an image without bandwidth expansion using DPSS(discrete prolate spheroidal sequence) to make it more secure based on Shujun's research which verifies the secrecy of Van De Ville's scheme. The proposed method utilizes an orthonormalized random matrix instead of Hadamard matrix for secret matrix and to add it for providing high secrecy against statistical attack or known-plaintext attack using some statistical property or estimate of secret matrix from a scrambled image. The experimental results show that the proposed method is more secure than the existing scheme. In addition, we show that the proposed method can be applied to access control or copy control of watermarking application.

• Journal of Broadcast Engineering
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• v.4 no.1
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• pp.59-67
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• 1999

In this paper, we study two important topics in stereoscopic image communication system. One is a disparity estimation (DE) method to obtain the depth information of a scene at the transmitter and the other is an intermediate view reconstruction(IVR) method at the receiver. We propose a new DE method using an edge-preserving directional regularization technique. The proposed DE method smooths disparity vectors in smooth regions and preserves edges without over-smoothing problem. It provides better reconstructed stereoscopic images and improved coding efficiency than the existing regularization techniques. In addition. we propose a new IVR method using interpolation and extrapolation techniques. The proposed IVR method preserves edge regions as well as occlusion regions well. Thus. it gives better intermediate views than the existing IVR methods.

• Journal of Korea Multimedia Society
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• v.11 no.9
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• pp.1213-1226
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• 2008

A recommender system supports people in making recommendations finding a set of people who are likely to provide good recommendations for a given person, or deriving recommendations from implicit behavior such as browsing activity, buying patterns, and time on task. We proposed new recommender system which combined SOM(Self-Organizing Map) neural networks with the Collaborative filtering which most recommender systems hat applied First, we segmented user groups according to demographic characteristics and then we trained the SOM with people's preferences as ito inputs. Finally we applied the classic collaborative filtering to the clustering with similarity in which an recommendation seeker belonged to, and therefore we didn't have to apply the collaborative filtering to the whose data set. Experiments were run for EachMovies data set. The results indicated that the predictive accuracy was increased in terms of MAE(Mean-Absolute-Error).

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1187-1198
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• 2021

Sea wind isrecently drawing attraction as one of the sources of renewable energy. Thisstudy describes a new method to produce a 10 m resolution sea wind field using Sentinel-1 images and low-resolution NWP (Numerical Weather Prediction) data with artificial intelligence technique. The experiment for the South East coast in Korea, 2015-2020,showed a 40% decreased MAE (Mean Absolute Error) than the generic CMOD (C-band Model) function, and the CC (correlation coefficient) of our method was 0.901 and 0.826, respectively, for the U and V wind components. We created 10m resolution sea wind maps for the study area, which showed a typical trend of wind distribution and a spatially detailed wind pattern as well. The proposed method can be applied to surveying for wind power and information service for coastal disaster prevention and leisure activities.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.3
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• pp.63-73
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• 2022

A more accurate understanding of the irrigation water supply is necessary for efficient agricultural water management. Although we measure water levels in an irrigation canal using ultrasonic water level gauges, some errors occur due to malfunctions or the surrounding environment. This study aims to apply CNN (Convolutional Neural Network) Deep-learning-based image classification and segmentation models to the irrigation canal's CCTV (Closed-Circuit Television) images. The CCTV images were acquired from the irrigation canal of the agricultural reservoir in Cheorwon-gun, Gangwon-do. We used the ResNet-50 model for the image classification model and the U-Net model for the image segmentation model. Using the Natural Breaks algorithm, we divided water level data into 2, 4, and 8 groups for image classification models. The classification models of 2, 4, and 8 groups showed the accuracy of 1.000, 0.987, and 0.634, respectively. The image segmentation model showed a Dice score of 0.998 and predicted water levels showed R² of 0.97 and MAE (Mean Absolute Error) of 0.02 m. The image classification models can be applied to the automatic gate-controller at four divisions of water levels. Also, the image segmentation model results can be applied to the alternative measurement for ultrasonic water gauges. We expect that the results of this study can provide a more scientific and efficient approach for agricultural water management.