• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1209-1213
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• 2007

하천 수치모델링을 통한 흐름, 오염물질 거동, 지형변화 해석 등은 효율적인 하천 수질 관리를 위해서 상당히 중요한 부분은 차지한다. 수질이나 지형변화를 보다 정확하게 예측하기 위해서는 하천 흐름 예측의 정확도 향상이 중요한 역할을 하게 된다. 본 연구는 평면 이차원 하상변동 및 수질예측 수치모형인 KU-RLMS 모형을 이용하여 낙동강 상류의 반변천 합류부의 흐름 특성을 규명하고, 수질 모형을 수행하기 위한 흐름 계산 결과를 제공하기 위해 수행하였다. KU-RLMS 모형은 하천 및 저수지의 국부적인 수리, 수질, 유사이동 해석을 위해 개발된 평면 이차원 비정상 수치모형이다. 직사각형 격자를 사용하는 유한차분법의 단점을 보완하기 위해, 흐름 계산을 위한 지배방정식은 3차원 Reynolds 방정식으로부터 수심적분된 2차원 연속방정식과 운동량방정식을 불규칙한 경계를 현실적으로 모사할 수 있는 직교곡선 좌표계로 변환한 방정식을 사용한다. 수치모형 적용을 위한 현황분석으로 안동 및 임하 조정지댐의 방류량, 안동 수위관측소의 자료를 분석하였다. 흐름 모형을 보정하기 위해 안동대교 지점에서 횡유속 분포를 측정하였으며, 이 결과를 사용하여 흐름 모형의 매개변수인 Manning 계수와 공간가중계수를 추정 및 검증하였다. 안동다목적댐과 임하다목적댐의 방류량을 고려하여 수치모의조건을 결정하였으며, 각 조건에 대한 흐름 변화 특성을 분석하였다.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.350-350
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• 2017

저수지의 설계홍수량 산정 시 인근의 기상관측 자료를 활용하고 있으나 인근에 기상관측 자료가 없거나 저수지 배후 유역이 큰 경우에는 단일 기상관측 자료를 이용하기에는 한계가 있다. 따라서 실무적으로 지점별 기상관측소의 자료를 이용하여 설계홍수량을 산정할 때에는 각 관측소 자료를 이용하여 확률강우량을 산정하고 Thiessen 가중평균을 한 후 면적우량환산계수 (ARF)를 곱하여 사용하고 있는데, Thiessen 방법의 경우 방법이 간단하지만 지형 고도 효과는 무시되고 우량계의 지배면적에 의한 우량계의 분포 상태만을 고려하게 된다. 그러므로 설계홍수량 산정시 사용되는 Thiessen 방법은 공간적 불확실성을 내포하고 있고, 특히 소규모 저수지의 설계홍수량을 산정하는 경우에는 저수지 유역의 국소적인 특징을 나타내기 어렵다. 본 연구에서는 설계홍수량 산정 시 저수지 위치에 해당하는 확률강우량의 공간적 불확실성을 평가하기 위하여 SIS(Sequential Indicator Simulation) 방법을 이용하였다. SIS 방법은 Kriging 기법과 마찬가지로 베리오그램으로부터 얻어지는 공간적 상관관계를 기반으로 하고 있는 방법으로 Kriging 기법과 달리 공간분포의 국소적인 특성을 평가할 수 있다는 장점을 가지고 있다.

• Journal of the Korean Society for Railway
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• v.20 no.3
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• pp.413-422
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• 2017

In this study we investigate the importance of the subway station using network centrality measures. For centrality measures, we have used betweenness centrality, closeness centrality, and degree centrality. A new measure called weighted betweenness centrality is proposed, that combines both traditional betweenness centrality and passenger flow between stations. Through correlation analysis and power-law analysis of passenger flow on the Seoul metropolitan subway network, we have shown that weighted betweenness centrality is a meaningful and practical measure. We have also shown that passenger flow between any two stations follows a highly skewed power-law distribution.

• Journal of the Korea Computer Graphics Society
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• v.24 no.4
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• pp.29-38
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• 2018

In this paper, we propose an automatic segmentation method of meniscus in knee MR images by automatic meniscus localization, multi-atlas-based locally-weighted voting, and patch-based edge feature classification. First, after segmenting the bone and knee articular cartilage, the volume of interest of the meniscus is automatically localized. Second, the meniscus is segmented by multi-atlas-based locally-weighted voting taking into account the weights of shape and intensity distribution in the volume of interest of the meniscus. Finally, to remove leakage to the collateral ligaments with similar intensity, meniscus is refined using patch-based edge feature classification considering shape and distance weights. Dice similarity coefficient between proposed method and manual segmentation were 80.13% of medial meniscus and 80.81 % for lateral meniscus, and showed better results of 7.25% for medial meniscus and 1.31% for lateral meniscus compared to the multi-atlas-based locally-weighted voting.

• Journal of Cadastre & Land InformatiX
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• v.48 no.1
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• pp.169-186
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• 2018

The social influence of the elderly population will accelerate in a rapidly aging society. The purpose of this study is to establish a methodology for extracting vulnerable districts of the welfare of the aged through machine learning(ML), artificial neural network(ANN) and geospatial analysis. In order to establish the direction of analysis, this progressed after an interview with volunteers who over 65-year old people, public officer and the manager of the aged welfare facility. The indicators are the geographic distance capacity, elderly welfare enjoyment, officially assessed land price and mobile communication based on old people activities where 500 m vector areal unit within 15 minutes in Yongin-city, Gyeonggi-do. As a result, the prediction accuracy of 83.2% in the support vector machine(SVM) of ML using the RBF kernel algorithm was obtained in simulation. Furthermore, the correlation result(0.63) was derived from ANN using backpropagation algorithm. A geographically weighted regression(GWR) was also performed to analyze spatial autocorrelation within variables. As a result of this analysis, the coefficient of determination was 70.1%, which showed good explanatory power. Moran's I and Getis-Ord Gi coefficients are analyzed to investigate spatially outlier as well as distribution patterns. This study can be used to solve the welfare imbalance of the aged considering the local conditions of the government recently.

• Transactions of the Korean Society of Mechanical Engineers
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• v.14 no.6
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• pp.1652-1660
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• 1990

Combined radiative-convective heat transfer in a hot gas tube flow has been investigated numerically and experimentally. In the numerical analysis, a standard k-.epsilon. model is used for the evaluation of turbulent shear stresses and spherical harmonics method with the Weighted Sum of Gray Gases Model for the solution of radiative transfer equation. In the experimental study measured are the velocity and temperature of the hot gas flow generated by the propane gas combustion, and tude wall heat flux distribution. Numerical results are compared with experimental ones and it is confirmed that P-3 provides quite reliable results in the analysis of the combined radiation-convection system.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.127-133
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• 2009

This paper presents an approach to classify normal and epilepsy from electroencephalogram(EEG) using a neural network with weighted fuzzy membership functions(NEWFM). To extract input features used in NEWFM, wavelet transform is used in the first step. In the second step, the frequency distribution of signal and the amount of changes in frequency distribution are used for extracting twenty-four numbers of input features from coefficients and approximations produced by wavelet transform in the previous step. NEWFM classifies normal and epilepsy using twenty four numbers of input features, and then the accuracy rate is 98%.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.424-434
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• 2020

This paper describes an algorithm that calculates Absolute Sound Level (ASL) for contents platform. ASL is a single volume representing individual sound sources and is a concept designed to integrate and utilize the sound level units in digital sound source and physical domain from a speaker in practical areas. For this concept to be used in content platforms and others, it is necessary to automatically derive the ASL without having to go through a hearing of mastering engineers. The key parameters of which a person recognizes the representative sound level of an individual single sound source are the areas of "frequency, maximum energy, energy variation coefficient, and perceived energy distribution," and the ASL was calculated through the normalizing of the weights.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.27-34
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• 2010

This paper presents a methodology for classifying left and right motor imagery using a neural network with weighted fuzzy membership functions (NEWFM) and wavelet-based feature extraction. Wavelet coefficients are extracted from electroencephalogram(EEG) signal by wavelet transforms in the first step. In the second step, sixty numbers of initial features are extracted from wavelet coefficients by the frequency distribution and the amount of variability in frequency distribution. The distributed non-overlap area measurement method selects the minimized number of features by removing the worst input features one by one, and then minimized six numbers of features are selected with the highest performance result. The proposed methodology shows that accuracy rate is 86.43% with six numbers of features.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.259-271
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• 2019

A statistical downscaling method was adopted in order to establish the high-resolution wave prediction system in the East Sea coastal area. This system used forecast data from the Global Wave Watch (GWW) model, and the East Sea and Busan Coastal Wave Watch (CWW) model operated by the Korea Meteorological Administration (KMA). We used the CWW forecast data until three days and the GWW forecast data from three to seven days to implement the statistical downscaling method (inverse distance weight interpolation and conditional merge). The two-dimensional and station wave heights as well as sea surface wind speed from the high-resolution coastal prediction system were verified with statistical analysis, using an initial analysis field and oceanic observation with buoys carried out by the KMA and the Korea Hydrographic and Oceanographic Agency (KHOA). Similar to the predictive performance of the GWW and the CWW data, the system has a high predictive performance at the initial stages that decreased gradually with forecast time. As a result, during the entire prediction period, the correlation coefficient and root mean square error of the predicted wave heights improved from 0.46 and 0.34 m to 0.6 and 0.28 m before and after applying the statistical downscaling method.