• The Bulletin of The Korean Astronomical Society
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• v.38 no.2
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• pp.76-76
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• 2013

The current hierarchical model of galaxy formation predicts that galaxy halos contain merger relics in the form of long stellar streams. In order to find stellar substructures in galaxy, we focused our investigation on the stellar spatial density around globular clusters and on the quantitative properties of the evolved sequences in the color-magnitude diagrams (CMDs). First, we investigated the spatial configuration of stars around five metal-poor globular clusters in halo region (M15, M30, M53, NGC 5053, and NGC 5466) and one metal-poor globular cluster in bulge region (NGC 6626). Our findings indicate that all of these globular clusters show strong evidence of extratidal features in the form of extended tidal tails around the clusters. The orientations of the extratidal features show the signatures of tidal tails tracing the clusters' orbits and the effects of dynamical interactions with the galaxy. These features were also confirmed by the radial surface density profiles and azimuthal number density profiles. Our results suggest that these six globular clusters are potentially associated with the satellite galaxies merged into the Milky Way. Second, we derived the morphological parameters of the red giant branch (RGB) from the near-infrared CMDs of 12 metal-poor globular clusters in the Galactic bulge. The photometric RGB shape indices such as colors at fixed magnitudes, magnitudes at fixed colors, and the RGB slope were measured for each cluster. The magnitudes of the RGB bump and tip were also estimated. The derived RGB parameters were used to examine the overall behavior of the RGB morphology as a function of cluster metallicity. The behavior of the RGB shape parameters was also compared with the previous observational calibration relation and theoretical predictions of the Yonsei-Yale isochrones. Our results of studies for stellar spatial distribution around globular clusters and the morphological properties of RGB stars in globular clusters could add further observational evidence of merging scenario of galaxy formation.

• Publications of The Korean Astronomical Society
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• v.7 no.1
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• pp.89-96
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• 1992

Recent spectroscopic observations indicate concentration of dark masses in the nuclei of nearby galaxies. This has been usually interpreted as the presence of massive black holes in these nuclei. Alternative explanations such as the dark cluster composed of low mass stars (brown dwarfs) or dark stellar remnants are possible provided that these systems can be stably maintained for the age of galaxies. For the case of low mass star cluster, mass of individual stars can grow to that of conventional stars in collision time scale. The requirement of collision time scale being shorter than the Hubble time gives the minimum cluster size. For typical conditions of M31 or M32, the half-mass radii of dark clusters can be as small as 0.1 arcsecond. For the case of clusters composed of stellar remnants, core-collapse and post-collapse expansion are required to take place in longer than Hubble time. Simple estimates reveal that the size of these clusters also can be small enough that no contradiction with observational data exists for the clusters made of white dwarfs or neutron stars. We then considered the possible outcomes of interactions between the black hole and the surrounding stellar system. Under typical conditions of M31 or M32, tidal disruption will occur every $10^3$ to $10^4$ years. We present a simple scenario for the evolution of stellar debris based on basic principles. While the accretion of stellar material could produce large amount of radiation so that the mass-to-light ratio can become too small compared to observational values it is too early to rule out the black hole model because the black hole can consume most of the stellar debris in time scale much shorter than mean time between two successive tidal disruptions. Finally we outline recent effort to simulate the process of tidal disruption and subsequent evolution of the stellar debris numerically using Smoothed Particle Hydrodynamics technique.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.41.2-41.2
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• 2017

Discovered as the second highest S/N detection of the Planck SZ survey, PLCK G287.0+32.9 is a massive galaxy cluster that belongs to a rare collection of merging clusters that exhibit two radio relics and a radio halo. A feature that makes this cluster even more unique is the separation of the radio relics with one $\sim 400$ kpc to the north-west of the X-ray peak and the other $\sim 2.8$ Mpc to the south-east. This asymmetric configuration requires a complex merging scenario. A key to gaining insight into the events that caused the formation of the merging features is to understand the dark matter mass distribution. Using a weak-lensing technique on deep Subaru and Hubble Space Telescope observations, we map the dark matter mass distribution of PLCK G287.0+32.9. Our investigation detects five significant mass structures. The mass is dominated by a primary structure that is centered near the X-ray peak of the intracluster medium. Four lesser mass structures are detected with two located within $\sim 1\arcmin$ of the primary mass structure, a third to the north-west, and a fourth near the south-east radio relic. Along with these detections, we estimate the mass of each structure and relate their distributions to the intracluster medium and galaxy distributions. In addition, we discuss the relation of the mass structures to the formation of the relics and plausible merging scenarios.

• Journal of Korean Society of Rural Planning
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• v.21 no.4
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• pp.35-43
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• 2015

The purpose of the research is to analyze damages of TYLCV (Tomato Yellow Leaf Curl Virus) in the context of climate changes and to find the spatial distribution of the damages and characteristics of regions. A TYLCV is generally known for a plant disease related to temperature. Its occurrence rate increases when temperature rises. This disease first occurred in 2008 and rapidly spread nationwide. Due to the spread of a TYLCV, a number of Tomato farms in Korea were damaged severely. To analyze damages of the pest in the context of climate changes, this research estimated production loss under the current situation and RCP scenarios. Additionally, Hot Spot Analysis, LISA, and Cluster analysis were conducted to find spatial distribution and properties of largely damaged regions under RCP scenarios. The results explained that additional production loss was estimated differently by regions with the same temperature rising scenario. Also, largely damaged regions are spatially clustered and factors causing large damages were different across regional cluster groups. It means that certain regions can be damaged more than others by diseases and pests. Furthermore, pest management policy should reflect the properties of each region such as climate conditions, cultivate environment and production technologies. The findings from this research can be utilized for developing rural management plans and pest protection policies.

• Journal of the Korean Society of Safety
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• v.36 no.4
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• pp.71-79
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• 2021

In this study, a real-scale fuel-cell room of volume 1.36 m³ is constructed to confirm the explosion characteristics of hydrogen-air mixture gas in a hydrogen-powered house. A volume concentration of 40% is applied in the fuel-cell room as the worst-case scenario to examine the most severe accident possible, and two types of doors (made of plastic sheet and wood) are fabricated to observe their effects on the overpressure and impulse. The peak overpressure and impulse based on distance from the ignition source are experimentally observed and assessed. The maximum and minimum overpressures with a plastic-sheet door are about 20 and 6.7 kPa and those with a wooden door are about 46 and 13 kPa at distances of 1 and 5 m from the ignition source, respectively. The ranges of impulses for distances of 1-5 m from the ignition source are about 82-28 Pa·s with a plastic-sheet door and 101-28 Pa·s with a wooden door. The amount of damage to people, buildings, and property due to the peak overpressure and impulse is presented to determine the safe distance; accordingly, the safe distance to prevent harm to humans is about 5 m based on the 'injuries' class, but the structural damage was not serious.

• Journal of Korean Society of Forest Science
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• v.105 no.3
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• pp.351-359
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• 2016

This study was conducted to analyze the effect of climate change on the tree-ring growth of Pinus koraiensis in Korea. Annual tree-ring growth data of P. koraiensis collected by the $5^{th}$ National Forest Inventory were first organized to analyze yearly growth patterns of the species. When tree-ring growth data were analyzed through cluster analysis based on similarity of climatic conditions, five clusters were identified. Yearly growing degree days and standard precipitation index based on daily mean temperature and precipitation data from 1951 to 2010 were calculated by cluster. Using the information, yearly temperature effect index(TEI) and precipitation effect index(PEI) by cluster were estimated to analyze the effect of climatic conditions on the growth of the species. Tree-ring growth estimation equations by cluster were developed by using the product of yearly TEI and PEI as independent variable. The tree-ring growth estimation equations were applied to the climate change scenarios of RCP 4.5 and RCP 8.5 for predicting the changes in tree-ring growth by cluster of P. koraiensis from 2011 to 2100. The results of this study are expected to provide valuable information necessary for estimating local growth characteristics of P. koraiensis and for predicting changes in tree-ring growth patterns caused by climate change.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.30-40
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• 2024

Building an accurate 3-D spatial road network model has become an active area of research now-a-days that profess to be a new paradigm in developing Smart roads and intelligent transportation system (ITS) which will help the public and private road impresario for better road mobility and eco-routing so that better road traffic, less carbon emission and road safety may be ensured. Dealing with such a large scale 3-D road network data poses challenges in getting accurate elevation information of a road network to better estimate the CO₂ emission and accurate routing for the vehicles in Internet of Vehicle (IoV) scenario. Clustering and regression techniques are found suitable in discovering the missing elevation information in 3-D spatial road network dataset for some points in the road network which is envisaged of helping the public a better eco-routing experience. Further, recently Explainable Artificial Intelligence (xAI) draws attention of the researchers to better interprete, transparent and comprehensible, thus enabling to design efficient choice based models choices depending upon users requirements. The 3-D road network dataset, comprising of spatial attributes (longitude, latitude, altitude) of North Jutland, Denmark, collected from publicly available UCI repositories is preprocessed through feature engineering and scaling to ensure optimal accuracy for clustering and regression tasks. K-Means clustering and regression using Support Vector Machine (SVM) with radial basis function (RBF) kernel are employed for 3-D road network analysis. Silhouette scores and number of clusters are chosen for measuring cluster quality whereas error metric such as MAE ( Mean Absolute Error) and RMSE (Root Mean Square Error) are considered for evaluating the regression method. To have better interpretability of the Clustering and regression models, SHAP (Shapley Additive Explanations), a powerful xAI technique is employed in this research. From extensive experiments , it is observed that SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions SHAP analysis validated the importance of latitude and altitude in predicting longitude, particularly in the four-cluster setup, providing critical insights into model behavior and feature contributions with an accuracy of 97.22% and strong performance metrics across all classes having MAE of 0.0346, and MSE of 0.0018. On the other hand, the ten-cluster setup, while faster in SHAP analysis, presented challenges in interpretability due to increased clustering complexity. Hence, K-Means clustering with K=4 and SVM hybrid models demonstrated superior performance and interpretability, highlighting the importance of careful cluster selection to balance model complexity and predictive accuracy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1569-1584
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• 2013

Ubiquitous interaction in a pervasive environment is the main attribute of smart spaces. Pervasive systems are weaving themselves in our daily life, making it possible to collect user information invisibly, in an unobtrusive manner by known and even unknown parties. Huge number of interactions between users and pervasive devices necessitate a comprehensive trust model which unifies different trust factors like context, recommendation, and history to calculate the trust level of each party precisely. Trusted computing enables effective solutions to verify the trustworthiness of computing platforms. In this paper, we elaborate Unified Trust Model (UTM) which calculates entity's trustworthiness based on history, recommendation, context and platform integrity measurement, and formally use these factors in trustworthiness calculation. We evaluate UTM behaviour by simulating in different scenario experiments using a Trust and Reputation Models Simulator for Wireless Sensor Networks. We show that UTM offers responsive behaviour and can be used effectively in the low interaction environments.

• Korean Management Science Review
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• v.10 no.2
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• pp.187-212
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• 1993

The purpose of this study is to deal with a forecast of probable failures facing up to the existing Korean universities and colleges in the early 21st century and with how each institution should cope with the situation before the failures actually take place. The methods used in this research are Scenario analysis, Factor and Cluster analyses in order to find out major factors having influence upon the operation and management of these universities and colleges. It's turned out that every institution is different from others and in fact unique in its make-up and so each will have different ways to deal with the impending failures and the crisis management problems strategically.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.294-298
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• 2015

In the United States, the completion of Construction Work Zone (CWZ) impact assessments for all federally-funded highway infrastructure improvement projects is mandated, yet it is regarded as a daunting task for state transportation agencies, due to a lack of standardized analytical methods for developing sounder Transportation Management Plans (TMPs). To circumvent these issues, this study aims to create a spatiotemporal modeling framework, dubbed "SWAT" (Spatiotemporal Work zone Assessment for TMPs). This study drew a total of 43,795 traffic sensor reading data collected from heavily trafficked highways in U.S. metropolitan areas. A multilevel-cluster-driven analysis characterized traffic patterns, while being verified using a measurement system analysis. An artificial neural networks model was created to predict potential 24/7 traffic demand automatically, and its predictive power was statistically validated. It is proposed that the predicted traffic patterns will be then incorporated into a what-if scenario analysis that evaluates the impact of numerous alternative construction plans. This study will yield a breakthrough in automating CWZ impact assessments with the first view of a systematic estimation method.