• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.318-327
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• 2014

Today, Competition in employment among the youth is more intensified and this phenomena lead to youth unemployment problems. This study was conducted to found the influence of youth employment effort on labor participation. We studied what variables could affect motivation of employment preparation especially among undergraduate and graduated students. We used 3rd~5th(2009~2011) 'Youth Panel Data' designed by Korean Employment Information Service. Data we adjusted were male(3,481) female(3,770). We applied the Generalized Estimating Equations to Panel logit model. We found that job education and training, career guidance, job shadowing program, getting new certification, sex, age and change of gross income affected employment preparation with controling education factors, socio-economic factors. This study found the effort of employment preparation was significant impact on labor participation and showed an influence on each variable empirically. We suggest that the youth-unemployment problem there is a need to approach fundamental aspects.

• The Journal of the Korea Contents Association
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• v.15 no.1
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• pp.519-526
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• 2015

This article presents detailed explanations of the depreciation method using the accumulated depreciation rate function(ADRF) proposed by the author previously, for the practical application. ADRF provides a value of accumulated depreciation rate on the total depreciation charge at any given time, therefore it can be used in a time-based depreciation method. Since the depreciation charge of each period can be systematically computed with ADRF, in every case where the charge is constant or decreasing or increasing, we can choose diverse rational depreciation types for the characteristic of every asset. Also, since the ADRF is the continuous function of time, we can compute the depreciation charge with consistency in cases where assets are owned for partial period. However, we should determine the value of parameter of ADRF. We give some data on the problem.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.137-141
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• 2007

우리나라는 지역적 특성상 여름철에 강수량이 편중되어 있어 계절별로 하천유량의 편차가 매우 크게 나타나고 있다. 또한 계속되는 도시화와 산업화로 인하여 하천에서 다양한 형태의 취수와 이용이 이루어지고, 국민들의 수질향상 등에 대한 기대가 커짐에 따라 하천유량에 대한 양적 질적 요구가 증대되고 있다. 이에 효율적인 수자원운영을 위하여 다양한 형태의 수자원확보가 이루어지고 있으며, 이중 가장 대표적인 댐을 이용하여 운영에 따른 하류 하천에서의 유황개선효과에 대해서 유역 장기분포모의 모형 중 하나인 SWAT모형을 밀양강유역에 적용하여 일 유출량 모의를 수행하였다. 모형의 구축은 $2000{\sim}2005$년의 밀양강유역 기상자료를 활용하였으며, 건교부에서 운영하는 Wamis를 이용하여 DEM, 토지이용도, 토양도를 취득하였다. 또한 밀양댐, 운문댐 유입량과 밀양2지점의 측정유량을 이용하여, SWAT의 다양한 매개변수 중 SOL_AWC, ESCO, CH_K등을 수정하여 Calibration을 실시하였으며, 밀양강유역 오염총량지점인 밀양A지점과 밀양B지점의 측정유량을 이용하여 Validation을 실시하였다. 그 결과, 댐의 경우 0.9이상의 선형상관관계를 나타내었으며, 오염총량지점의 경우 0.6이상의 선형상관관계를 나타내어 모형의 적용성이 양호함을 알 수 있었다. 과거 39년간의 강수량자료를 이용, 구축된 모형의 유역모의운영을 통해 밀양강 유역의 밀양댐과 운문댐이 있음으로 해서, 댐이 없는 경우와 비교, 하류 지점별 유황개선에 어느 정도 효과가 있는지 댐의 순기능에 대한 정량적인 분석을 수행하였다. 또한 댐별 방류량을 변동하여 하류 주요지점에 미치는 유황개선효과를 정량화하였다. 마지막으로 댐의 효율을 최대화한 하류확보가능하천유지유량을 월별평균량으로 산정하였다. 이는 향후 오염총량제 기준유량 및 환경용수의 법제화를 통한 하천유지용수의 증가시 비구조적 대책의 공급가능 최대량으로 활용가능할 것으로 사료된다.

• Geophysics and Geophysical Exploration
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• v.19 no.4
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• pp.220-227
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• 2016

Marine seismic data have not only primary signals from subsurface but also ghost signals reflected from the sea surface. The ghost decreases temporal resolution of seismic data because it attenuates specific frequency components. For eliminating the ghost signals effectively, the exact ghost delaytimes and reflection coefficients are required. Because of undulation of the sea surface and vertical movements of airguns and streamers, the ghost delaytime varies spatially and randomly while acquiring seismic data. The reflection coefficient is a function of frequency, incidence angle of plane-wave and the sea state. In order to estimate the proper ghost delaytimes considering these characteristics, we compared the ghost delaytimes estimated with L-1 norm, L-2 norm and kurtosis of the deghosted trace and its autocorrelation on synthetic data. L-1 norm of autocorrelation showed a minimal error and the reflection coefficient was calculated using Kirchhoff approximation equation which can handle the effect of wave height. We applied the estimated ghost delaytimes and the calculated reflection coefficients to remove the source and receiver ghost effects. By removing ghost signals, we reconstructed the frequency components attenuated near the notch frequency and produced the migrated stack section with enhanced temporal resolution.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Economic and Environmental Geology
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• v.39 no.6 s.181
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• pp.711-717
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• 2006

In order to study gas hydrate, potential future energy resources, Korea Institute of Geoscience and Mineral Resources has conducted seismic reflection survey in the East Sea since 1997. one of evidence for presence of gas hydrate in seismic reflection data is a bottom simulating reflector (BSR). The BSR occurs at the interface between overlaying higher velocity, hydrate-bearing sediment and underlying lower velocity, free gas-bearing sediment. That is often characterized by large reflection coefficient and reflection polarity reverse to that of seafloor reflection. In order to apply depth migration to seismic reflection data. we need high performance computers and a parallelizing technique because of huge data volume and computation. Phase shift plus interpolation (PSPI) is a useful method for migration due to less computing time and computational efficiency. PSPI is intrinsically parallelizing characteristic in the frequency domain. We conducted conventional data processing for the gas hydrate data of the Ease Sea and then applied prestack depth migration using message-passing-interface PSPI (MPI_PSPI) that was parallelized by MPI local-area-multi-computer (MPI_LAM). Velocity model was made using the stack velocities after we had picked horizons on the stack image with in-house processing tool, Geobit. We could find the BSRs on the migrated stack section were about at SP 3555-4162 and two way travel time around 2,950 ms in time domain. In depth domain such BSRs appear at 6-17 km distance and 2.1 km depth from the seafloor. Since energy concentrated subsurface was well imaged we have to choose acquisition parameters suited for transmitting seismic energy to target area.

• The Journal of the Korea Contents Association
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• v.12 no.4
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• pp.457-467
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• 2012

The purpose of the study was to explore the general characteristics of sports for all through the patterns of sports for all policy and analysis of causal relation of the determinants. To achieve this goal, 26 countries among OECD 30 members which provide useful data sources were selected. The data were analyzed by Qualitative Comparative Analysis(QCA) with cluster analysis. GDP, leisure time, social expenditure, Gini's coefficient, poverty rate and tax burden ratio were used as casual variables for Qualitative Comparative Analysis. The findings of this study were as follows. First, three patterns were examined and Korea was classified into the pattern which has low sports for all participation and sportsclub participation. Second, as a result of Qualitative Comparative Analysis for analyzing the determinants of sports for all patterns, the pattern in which includes Korea showed that GDP, leisure time, social expenditure, tax burden ratio had negative relationship and Gini's coefficient, poverty rate had positive relationship.

• The Journal of the Acoustical Society of Korea
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• v.40 no.4
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• pp.382-390
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• 2021

Synthetic aperture sonars as well as side scan sonars or multibeam echo sounders have been commercialized and are widely used for seafloor imaging. In Korea related research such as the development of a towed synthetic aperture sonar system is underway. In order to obtain high-resolution synthetic aperture sonar images, it is necessary to accurately estimate the platform motion on which it is installed, and a precise underwater navigation system is required. In this paper we are going to provide reference data for determining the required navigation accuracy and precision of navigation sensors by quantitatively analyzing how much distortion of the sonar images occurs according to motion characteristics of the platform equipped with the synthetic aperture sonar. Five types of motions are considered and normalized root mean square error is defined for quantitative analysis. Simulation for error analysis with parameter variation of motion characteristics results in that yaw and sway motion causes the largest image distortion whereas the effect of pitch and heave motion is not significant.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.433-442
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• 2018

The reservoir area is defined as the area surrounded by the planned flood level of the dam or the land under the planned flood level of the dam. In this study, supervised classification based on RF (Random Forest), which is a representative machine learning technique, was performed to detect cropland in the reservoir area. In order to classify the cropland in the reservoir area efficiently, the GLCM (Gray Level Co-occurrence Matrix), which is a representative technique to quantify texture information, NDWI (Normalized Difference Water Index) and NDVI (Normalized Difference Vegetation Index) were utilized as additional features during classification process. In particular, we analyzed the effect of texture information according to window size for generating GLCM, and suggested a methodology for detecting croplands in the reservoir area. In the experimental result, the classification result showed that cropland in the reservoir area could be detected by the multispectral, NDVI, NDWI and GLCM images of UAV, efficiently. Especially, the window size of GLCM was an important parameter to increase the classification accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.93-103
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• 2019

This study analyzed the impact of smartphone usage on walking speed during walking on two pedestrian walkways in Daejeon Metropolitan City. For the analysis, the video data about the actual use of smartphone was acquired and the walking speed was calculated based on the walking density of the pedestrian Level Of Service(LOS) presented in the Road Capacity Manual. Multiple regression analysis and decision tree using machine learning were used to analyze the impact of smartphone usage on walking speed, and as the explanatory variables, gender, disable smartphone, use of smartphone using auditory function, use of smartphone using visual function, LOS A, LOS B, LOS C were adopted. The result showed that LOS C had the highest impact on walking speed change and the women's group using their visual function was founded to have the slowest walking speed in LOS C. In particular, the author found that walking speed significantly decreased in the case of use of visual function rather than listening to music or the hearing on the phone.