• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.7
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• pp.1869-1882
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• 1998

In this paper, we investigae convergence behaviors of a differential method for 2-D motion parameter estimation. While the differential method is widely studied for motion compensated prediction in video coding, little attention hs been paid to its convergence properties. Based on the nonseparable exponential covariance image model, we derive the estimates of update terms for the 2- and 6-parameter motion models. And, the effect of noise, spatial correlation, choice of spatial gradient measures, andthe size of a region, are quantitatively anlyzed in relation to the convergence speed. Some empirical results are presented to verify the analysis.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.1
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• pp.58.2-58.2
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• 2013

We present a photometric study of the globular clusters (GCs) in the giant elliptical galaxy M86 in the Virgo Cluster, using the Washington $CT_1$ images taken at the KPNO 4 m telescope. The color distribution of the GCs in M86 is bimodal. The radial number density profile of the blue GCs decreases more slowly as the galactocentric distance increases than that of the red GCs. The density profile of the red GCs is similar to the surface brightness profile of M86 stellar halo. The blue GCs have a roughly circular spatial distribution, while the red GCs have a spatial distribution somewhat elongated, which is consistent with the distribution of the galaxy stellar light. M86 GCs have the negative radial color gradient because the number ratio of the blue GCs to the red GCs increases as galactocentric radius increases. The mean color of the red GCs is similar to that of the stellar halo. The bright blue GCs in the outer region of M86 reveal a blue tilt that the mean colors of the blue GCs get redder as they get brighter. We discuss these results in comparison with other giant elliptical galaxies in the Virgo Cluster.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.38 no.11
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• pp.861-869
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• 1989

The optimization problems, based on Pontryagin's Maximum Principle, for minimizing (damping) Xenon spatial oscillations in Load Following operations of Pressurized Water Reactor (PWR) is presented. The optimization model is formulated as an optimal tracking problem with quadratic objective functional. The oen-group diffusion equations and Xe-I dynamic equations are defined as equality constraints. By applying the maximum principle, the original problem is decomposed into a single time problem with no constraints. The resultant subproblems are optimized by using the conjugate Gradient Method. The computational results show that the Xenon spatial oscillation is minimized, and the reactor follows the load demand of the electrical power systems while maintaining the desired power distribution.

• Transactions of the Korean Society of Automotive Engineers
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• v.6 no.6
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• pp.132-141
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• 1998

Particle image velocimetry(PIV), a planar measuring technique, is an efficient tool for studying the complicated flow field such as in-cylinder flow, and intake port flow. PIV can be also used for analyzing the integral length scale of turbulence, which is a measure of the size of the large eddies that contain most of the turbulence kinetic energy. In this study, dual color scanning PIV was designed and demonstrated by using a rotating mirror and a beam splitter. This PIV system allowed enlargement of flexibility in the intensity of vectors to be calculated by spatial filtering technique, even in combustion chamber with high velocity gradient and high vorticity$({\sim}1000s^{-1})$. A new color image processing algorithm was developed, which was used to find the direction of particle movement directly from the digital image. These measuring techniques were successfully applied to obtaining the turbulence intensity (~0.1m/s) and the turbulent integral length scale of vorticity(~1mm).

• Journal of the Korean earth science society
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• v.42 no.4
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• pp.401-411
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• 2021

This study provides comprehensive assessment results for the most recent high-resolution regional climatology in the East/Japan Sea by comparing with the various existing climatologies. This new high-resolution climatology is generated based on the Optimal Interpolation (OI) method with individual profiles from the World Ocean Database and gridded World Ocean Atlas provided by the National Centers for Environmental Information (NCEI). It was generated from the recent previous study which had a primary focus to solve the abnormal horizontal gradient problem appearing in the other high-resolution climatology version of NCEI. This study showed that this new OI field simulates well the meso-scale features including closed-curve temperature spatial distribution associated with eddy formation. Quantitative spatial variability was compared to the other four different climatologies and significant variability at 160 km was presented through a wavelet spectrum analysis. In addition, the general improvement of the new OI field except for warm bias in the coastal area was confirmed from the comparison with serial observation data provided by the National Fisheries Research and Development Institute's Korean Oceanic Data Center.

• Journal of Korean Association for Spatial Structures
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• v.21 no.2
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• pp.99-110
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• 2021

This study presents the estimation of crack depth by analyzing temperatures extracted from thermal images and environmental parameters such as air temperature, air humidity, illumination. The statistics of all acquired features and the correlation coefficient among thermal images and environmental parameters are presented. The concrete crack depths were predicted by four different machine learning models: Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB). The machine learning algorithms are validated by the coefficient of determination, accuracy, and Mean Absolute Percentage Error (MAPE). The AB model had a great performance among the four models due to the non-linearity of features and weak learner aggregation with weights on misclassified data. The maximum depth 11 of the base estimator in the AB model is efficient with high performance with 97.6% of accuracy and 0.07% of MAPE. Feature importances, permutation importance, and partial dependence are analyzed in the AB model. The results show that the marginal effect of air humidity, crack depth, and crack temperature in order is higher than that of the others.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.151-151
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• 2021

The alarming climate change impacts are demanding the use of renewable energy sources like never before. Hydropower is one of the most cost-effective and environmental friendly energy technology recognized in the world. Big hydropower projects come up with the requirements of huge investment costs along with environmental impacts, whereas small hydropower(SHP) are considered a best solution for the economical source of energy. SHP, basically Run-of-River (RoR) type plants can be sustainable renewable energy sources and given the nature of perennial rivers flowing from steep gradient and rugged topography, feasibility of such plants is equally high in Nepal. The objective of this study is to determine the primary potential sites for the development of RoR type SHP sites using Geo-spatial Information System(GSIS). The use of GSIS enables precise survey of large area within a short period of time. This study has focused on the determination of locations by establishing defined criterions and methodologies and hence have located multiple locations rather than selecting one best location. The approach is applicable for the rapid initial screening of potential locations and results can facilitate detail feasibility study for the technical and economic analysis of SHP in the basin.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.

• Journal of Ecology and Environment
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• v.35 no.1
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• pp.51-58
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• 2012

In this study, we investigated carabid beetles residing in the wetlands to understand their ecological adaptation and strategy selection associated with restricted resources and habitat limitation. The species richness, abundance, seasonal activity, and spatial distribution of the carabid beetles between the Mujechi Wetlands (wetland sites) and Mt. Jeongjok (mountain sites) have been compared. A total of 1,733 individual beetles from 30 species were collected and classified at the studied sites. The wetland sites were identified as having lower species richness and abundance for carabid beetles when compared with the adjacent mountain sites, whereas these beetles were observed to be dominant in the wetland sites than in the adjacent mountain sites. Calosoma inquisitor cyanescens, Carabus sternbergi sternbergi, and Carabus jankowskii jankowskii species were dominant in both the wetland and mountain sites. These species showed significantly different seasonal activity patterns in the wetland sites relative to the mountain sites. Although the three listed carabid species were observed to be widely distributed throughout the wetland sites, they still showed preference for drier sites, which clearly shows a distinction in their habitats. The results of the spatial-temporal distribution of carabid beetles in the wetland sites reflect their special strategies regarding space and time partitioning for maintaining their population. The distribution patterns of carabid beetles in the wetland sites also showed the desiccation gradient and environmental changes prevalent in wetlands. Ecological surveys, which use carabid beetles in the wetlands, can then be performed when restoring wetlands and for establishing management practices for improving the habitat quality.