• Earthquakes and Structures
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• v.22 no.6
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• pp.585-595
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• 2022

Using the nonlinear static procedures has become very common in seismic codes to achieve the nonlinear response of the structure during an earthquake. The capacity spectrum method (CSM) adopted in ATC-40 is considered as one of the most known and useful procedures. For this procedure the seismic demand can be approximated from the maximum deformation of an equivalent linear elastic Single-Degree-of-Freedom system (SDOF) that has an equivalent damping ratio and period by using an iterative procedure. Data from the results of this procedure are plotted in acceleration- displacement response spectrum (ADRS) format. Different improvements have been made in order to have more accurate results compared to the Non Linear Time History Analysis (NL-THA). A new procedure is presented in this paper where the iteration process shall not be required. This will be done by estimation the ductility demand response spectrum (DDRS) and the corresponding effective damping of the bilinear system based on a new parameter of control, called normalized yield strength coefficient (η), while retaining the attraction of graphical implementation of the improved procedure of the FEMA-440. The proposed procedure accuracy should be verified with the NL-THA analysis results as a first implementation. The comparison shows that the new procedure provided a good estimation of the nonlinear response of the structure compared with those obtained when using the NL-THA analysis.

• Geomechanics and Engineering
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• v.30 no.2
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• pp.123-138
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• 2022

Present study computes the ultimate bearing capacity of an embedded strip footing situated on the rock slope subjected to seismic loading. Influences of embedment depth of strip footing, horizontal seismic acceleration coefficient, rock slope angle, Geological Strength Index, normalized uniaxial compressive strength of rock mass, disturbance factor, and Hoek-Brown material constant are studied in detail. To perform the analysis, the lower bound finite element limit analysis method in combination with the semidefinite programming is utilized. From the results of the present study, it can be found that the magnitude of the bearing capacity factor reduces quite substantially with an increment in the seismic loading. In addition, with the increment in slope angle, further reduction in the value of the bearing capacity factor is observed. On the other hand, with an increment in the embedment depth, an increment in the value of the bearing capacity factor is found. Stress contours are presented to describe the combined failure mechanism of the footing-rock slope system in the presence of static as well as seismic loadings for the different embedment depths.

• Nuclear Engineering and Technology
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• v.54 no.9
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• pp.3380-3389
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• 2022

We evaluated the performance of an improved hybrid median filter (IHMF) applied to X-ray computed tomography (CT) images obtained using a high-resolution photon-counting cadmium zinc telluride (CZT) detector. To study how the proposed approach improves the image quality, we measured the noise levels and the overall CT-image quality. We established a CZT imaging system with a detector length of 5.12 cm and thickness of 0.3 cm and acquired phantom images. To evaluate the efficacy of the proposed filter, we first modeled two conventional median filters. Subsequently, we were able to achieve a normalized noise power spectrum result of ~10^-8 mm², and furthermore, the proposed method improved the contrast-to-noise ratio by a factor of ~1.51 and the coefficient of variation by 1.55 relative to the counterpart values of the no-filter image. In addition, the IHMF exhibited the best performance among the three filters considered as regards the peak signal-to-noise ratio and no-reference-based image-quality evaluation parameters. Thus, our results demonstrate that the IHMF approach provides a superior image performance over conventional median filtering methods when applied to actual CZT X-ray CT images.

• Journal of Soil and Groundwater Environment
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• v.27 no.6
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• pp.11-21
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• 2022

Sorption of phenanthrene (PHE) and pyrene (PYR) in several sorbents, i.e., natural soil, BionSoil^®, Pahokee peat, vermicompost and Devonian Ohio Shale and a surfactant (hexadecyltrimethyl ammonium chloride)-modified montmorillonite (HDTMA-M) were investigated. Pyrene exhibited higher sorption tendency than phenanthrene, as predicted by its higher octanol to water partition coefficient (K_ow). Several sorption models: linear, Freundlich, solubility-normalized Freundlich model, and Polanyi-Manes model (PMM) were used to analyze sorption isotherms. Linear isotherms were observed for natural soil, BionSoil^®, Pahokee peat, vermicompost, while nonlinear Freundlich isotherms fitted for Ohio shale and HDTMA-M. The relationship between sorption model parameters, organic carbon content (f_oc), and elemental C/N ratio was studied. In the binary competitive sorption of phenanthrene and pyrene in natural soil, competition between the solutes caused reduction in the sorption of each solute compared with that in the single-solute system. The ideal adsorbed solution theory (IAST) coupled with the single-solute Freundlich model was not successful in describing the binary competitive sorption equilibria. This was due to the inherent nature of linear sorption of phenanthrene and pyrene in natural soil. The result indicates that the applicability of IAST for the prediction of binary competitive sorption is limited when the sorption isotherms are inherently linear.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2587-2605
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• 2022

In order to efficiently detect community structure in complex networks, community detection algorithms can be designed from the perspective of node similarity. However, the appropriate parameters should be chosen to achieve community division, furthermore, these existing algorithms based on the similarity of common neighbors have low discrimination between node pairs. To solve the above problems, a noval community detection algorithm using closeness similarity based on common neighbor node clustering entropy is proposed, shorted as CSCDA. Firstly, to improve detection accuracy, common neighbors and clustering coefficient are combined in the form of entropy, then a new closeness similarity measure is proposed. Through the designed similarity measure, the closeness similar node set of each node can be further accurately identified. Secondly, to reduce the randomness of the community detection result, based on the closeness similar node set, the node leadership is used to determine the most closeness similar first-order neighbor node for merging to create the initial communities. Thirdly, for the difficult problem of parameter selection in existing algorithms, the merging of two levels is used to iteratively detect the final communities with the idea of modularity optimization. Finally, experiments show that the normalized mutual information values are increased by an average of 8.06% and 5.94% on two scales of synthetic networks and real-world networks with real communities, and modularity is increased by an average of 0.80% on the real-world networks without real communities.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.230-230
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• 2023

딥러닝 알고리즘 중 과거의 정보를 저장하는 문제(장기종속성 문제)가 있는 단순 RNN(Simple Recurrent Neural Network)의 단점을 해결한 LSTM(Long short-term memory)이 등장하면서 특정한 유역의 강우-유출 모형을 구축하는 연구가 증가하고 있다. 그러나 하나의 모형으로 모든 유역에 대한 유출을 예측하는 지역화 강우-유출 모형은 서로 다른 유역의 식생, 지형 등의 차이에서 발생하는 수문학적 행동의 차이를 학습해야 하므로 모형 구축에 어려움이 있다. 따라서, 본 연구에서는 국내 12개의 유역에 대하여 LSTM 기반 분포형 지역화 강우-유출 모형을 구축한 이후 강우 이외의 보조 자료에 따른 정확도를 살펴보았다. 국내 12개 유역의 7년 (2012.01.01-2018.12.31) 동안의 49개 격자(4km²)에 대한 10분 간격 레이더 강우, MODIS 위성 이미지 영상을 활용한 식생지수 (Normalized Difference Vegetation Index), 10분 간격 기온, 유역 평균 경사, 단순 하천 경사를 입력자료로 활용하였으며 10분 간격 유량 자료를 출력 자료로 사용하여 LSTM 기반 분포형 지역화 강우-유출 모형을 구축하였다. 이후 구축된 모형의 성능을 검증하기 위해 학습에 사용되지 않은 3개의 유역에 대한 자료를 활용하여 Nash-Sutcliffe Model Efficiency Coefficient (NSE)를 확인하였다. 식생지수를 보조 자료를 활용하였을 경우 제안한 모형은 3개의 검증 유역에 대하여 하천 흐름을 높은 정확도로 예측하였으며 딥러닝 모형이 위성 자료를 통하여 식생에 의한 차단 및 토양 침투와 같은 동적 요소의 학습이 가능함을 나타낸다.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.44.1-44.1
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• 2019

We generate new intermediate images between observed consecutive solar images using NVIDIA's SuperSloMo that is a novel video interpolation method. This technique creates intermediate frames between two successive frames to form a coherent video sequence for both spatially and temporally. By using SuperSloMo, we create 600 images (12-second interval) using the observed 121 SDO/AIA 304 Å images (1-minute interval) of a filament eruption event on December 3, 2012. We compare the generated images with the original 12-second images. For the generated 480 images the correlation coefficient (CC), the relative error (R1), and the normalized mean square error (R2) are 0.99, 0.40, and 0.86, respectively. We construct a video made of the generated images and find a smoother erupting movement. In addition, we generate nonexistent 2.4-second interval images using the original 12-second interval images, showing slow motions in the eruption. We will discuss possible applications of this method.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.28-28
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• 2022

본 연구는 용담댐유역을 포함한 금강 유역 상류 지역을 대상으로 Sentinel-1 SAR (Synthetic Aperture Radar) 위성영상을 기반으로 한 토양수분 산정을 목적으로 하였다. Sentinel-1 영상은 2019년에 대해 12일 간격으로 수집하였고, 영상의 전처리는 SNAP (SentiNel Application Platform)을 활용하여 기하 보정, 방사 보정 및 Speckle 보정을 수행하여 VH (Vertical transmit-Horizontal receive) 및 VV (Vertical transmit-Vertical receive) 편파 후방산란계수로 변환하였다. 토양수분 산정에는 Water Cloud Model (WCM)이 활용되었으며, 모형의 식생 서술자(Vegetation descriptor)는 RVI (Radar Vegetation Index)와 NDVI (Normalized Difference Vegetation Index)를 활용하였다. RVI는 Sentinel-1 영상의 VH 및 VV 편파자료를 이용해 산정하였으며, NDVI는 동기간에 대해 10일 간격으로 수집된 Sentinel-2 MSI (MultiSpectral Instrument) 위성영상을 활용하여 산정하였다. WCM의 검정 및 보정은 한국수자원공사에서 제공하는 10 cm 깊이의 TDR (Time Domain Reflectometry) 센서에서 실측된 6개 지점의 토양수분 자료를 수집하여 수행하였으며, 매개변수의 최적화는 비선형 최소제곱(Non-linear least square) 및 PSO (Particle Swarm Optimization) 알고리즘을 활용하였다. WCM을 통해 산정된 토양수분은 피어슨 상관계수(Pearson's correlation coefficient)와 평균제곱근오차(Root mean square error)를 활용하여 검증을 수행할 예정이다.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1373-1389
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• 2019

KOMPSAT-5 is the first satellite in Korea equipped with X-band Synthetic Aperture Radar (SAR) instrument and has been operated since August 2013. KOMPSAT-5 is used to monitor the global environment according to its observation purpose and the availability of KOMPSAT-5 is also highlighted as the need of high resolution wind data for investigating the coastal region. However, the previous study for the validation of wind derived from KOMPSAT-5 showed that the accuracy is lower than that of other SAR satellites. Therefore, in this study, we developed the correction equation of normalized radar cross section (NRCS or backscattering coefficient) for improvement of wind from the KOMPSAT-5 and validated the effect of the equation using the in-situ measurement of ocean buoys. Theoretical estimated NRCS and observed NRCS from KOMPSAT-5 showed linear relationship with incidence angle. Before applying the correction equation, the accuracy of the estimated wind speed showed the relatively high root-mean-square errors (RMSE) of 2.89 m s^-1 and bias of -0.55 m s^-1. Such high errors were significantly reduced to the RMSE of 1.60 m s^-1 and bias of -0.38 m s^-1 after applying the correction equation. The improvement effect of the correction equation showed dependency relying on the range of incidence angle.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.135-144
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• 2022

Since vegetation provides humans with various ecological spaces and is also very important in terms of water resources and climatic environment, many vegetation monitoring studies using vegetation indexes based on near infrared sensors have been conducted. Therefore, if the near infrared sensor is not provided, the vegetation monitoring study has a practical problem. In this study, to improve this problem, the NDVI (Normalized Difference Vegetation Index) was used as a reference to evaluate the accuracy of the vegetation index based on the optical sensor. First, the Kappa coefficient was calculated by overlapping the vegetation survey point surveyed in the field with the NDVI. As a result, the vegetation area with a threshold value of 0.6 or higher, which has the highest Kappa coefficient of 0.930, was evaluated based on optical sensor based vegetation index accuracy. It could be selected as standard data. As a result of selecting NDVI as reference data and comparing with vegetation index based on optical sensor, the Kappa coefficients at the threshold values of 0.04, 0.08, and 0.30 or higher were the highest, 0.713, 0.713, and 0.828, respectively. In particular, in the case of the RGBVI (Red Green Red Vegetation Index), the Kappa coefficient was high at 0.828. Therefore, it was found that the vegetation monitoring study using the optical sensor is possible even in environments where the near infrared sensor is not available.