• International journal of advanced smart convergence
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• v.10 no.4
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• pp.278-288
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• 2021

Recently, IoT systems are cloud-based, so that continuous and large amounts of data collected from sensor nodes are processed in the data server through the cloud. However, in the centralized configuration of large-scale cloud computing, computational processing must be performed at a physical location where data collection and processing take place, and the need for edge computers to reduce the network load of the cloud system is gradually expanding. In this paper, a cluster system consisting of 6 inexpensive Raspberry Pi boards was constructed to perform fast data processing. And we propose "Kubernetes cluster system(KCS)" for processing large data collection and analysis by model distribution and data pipeline method. To compare the performance of this study, an ensemble model of deep learning was built, and the accuracy, processing performance, and processing time through the proposed KCS system and model distribution were compared and analyzed. As a result, the ensemble model was excellent in accuracy, but the KCS implemented as a data pipeline proved to be superior in processing speed..

• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.1-10
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• 2014

The cloud computing paradigm introduced pay-per-use models in which IT services can be created and scaled on-demand. However, service providers are still concerned about the constraints imposed by their physical infrastructures. In order to keep the required QoS and achieve the goal of upholding the SLA, virtualized resources must be efficiently consolidated to maximize system throughput while keeping energy consumption at a minimum. Using ANN, we propose a predictive SLA-aware approach for consolidating virtualized resources in a cloud environment. To maintain the QoS and to establish an optimal trade-off between performance and energy efficiency, the server's utilization threshold dynamically adapts to the physical machine's resource consumption. Furthermore, resource-intensive VMs are prevented from getting underprovisioned by assigning them to hosts that are both capable and reputable. To verify the performance of our proposed approach, we compare it with non-optimized conventional approaches as well as with other previously proposed techniques in a heterogeneous cloud environment setup.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.399-406
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• 2023

Cloud computing has been evolved to support edge computing architecture that combines fog management layer with edge servers. The main reason why it is received much attention is low communication latency for real-time IoT applications. At the same time, various cloud task scheduling techniques based on artificial intelligence have been proposed. Artificial intelligence-based cloud task scheduling techniques show better performance in comparison to existing methods, but it has relatively high scheduling time. In this paper, we propose a deep learning-based dynamic scheduling with multi-agents supporting scalability in edge computing environments. The proposed method shows low scheduling time than previous artificial intelligence-based scheduling techniques. To show the effectiveness of the proposed method, we compare the performance between previous and proposed methods in a scalable experimental environment. The results show that our method supports real-time IoT applications with low scheduling time, and shows better performance in terms of the number of completed cloud tasks in a scalable experimental environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.1
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• pp.1-14
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• 2022

In order to conduct rapid, accurate and safe surveying at the excavation site, In this study, the possibility of underground facility survey using drones and the expected effect of 3D visualization were obtained as follows. Phantom4Pro 20MP drones have a 30m flight altitude and a redundant 85% flight plan, securing a GSD (Ground Sampling Distance) value of 0.85mm and 4points of GCP (Groud Control Point)and 2points of check point were calculated, and 7.3mm of ground control point and 11mm of check point were obtained. The importance of GCP was confirmed when measured with low-cost drones. If there is no ground reference point, the error range of X value is derived from -81.2 cm to +90.0 cm, and the error range of Y value is +6.8 cm to 155.9 cm. This study classifies point cloud data using the Pix4D program. I'm sorting underground facility data and road pavement data, and visualized 3D data of road and underground facilities of actual model through overlapping process. Overlaid point cloud data can be used to check the location and depth of the place you want through the Open Source program CloudCompare. This study will become a new paradigm of underground facility surveying.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.237-250
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• 2023

Orthophotos and DEMs were generated by UAV-based aerial photogrammetry and an attempt was made to apply them to detailed investigations for the production of flood traces. The cultivated area located in Goa-eup, Gumi, where the embankment collapsed and inundated inundation occurred due to the impact of 6th Typhoon Sanba in 2012, was selected as rhe target area. To obtain optimal accuracy of UAV photogrammetry performance, the UAV images were taken under the optimal placement of 19 GCPs and then point cloud, DEM, and orthoimages were generated through image processing using Pix4Dmapper software. After applying CloudCompare's CSF Filtering to separate the point cloud into ground elements and non-ground elements, a finally corrected DEM was created using only non-ground elements in GRASS GIS software. The flood level and flood depth data extracted from the final generated DEM were compared and presented with the flood level and flood depth data from existing data as of 2012 provided through the public data portal site of the Korea Land and Geospatial Informatix Corporation(LX).

• Information Systems Review
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• v.17 no.1
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• pp.217-238
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• 2015

Much of the process in Social Science Research can be expedited with use of an automation systems that can lead to research efficiency and dramatic improvement of the research process. This study proposes use of a social science research automation system based on the cloud, which generates questionnaires, supports data collection, and intuitively processes statistical analyses of the data collected. The Cloud-based Social Science Research Automation System is developed with GNU/GPL-based open source software. We also integrate R for statistical computing to enable advanced statistical analyses such as PLS structural equation modeling, mediate effect analysis, compare between groups, and complete general statistics. The Cloud-based Social Science Research Automation system developed in this study is expected to play an important role in improving the social science research process and in performing the social science studies efficiently.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.1
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• pp.1-8
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• 2021

Vapor cloud explosions show different characteristics from that caused by ordinary TNT explosives and their loading effect is similar to pressure waves. Typical methods used for blast pressure calculations are the TNT-equivalent method and multi-energy method. The TNT-equivalent method is based on shock waves, similar to a detonation phenomenon, and multi-energy method is based on pressure waves, similar to a deflagration phenomenon. This study was conducted to derive an appropriate blast pressure by applying various plant explosion cases. SDOF analysis and nonlinear dynamic analysis were performed to compare the degree of deformation and damage of the selected structural members for the explosion cases. The results indicated that the multi-energy method was more exact than the TNT-equivalent method in predicting the blast pressure of vapor cloud explosions. The blast pressure of vapor cloud explosion in plants can be more accurately calculated by assuming the charge strength of multi-energy method as 7 or 8.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.48.1-48.1
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• 2014

We investigate the evolution of galaxies in mid-infrared (MIR) $[3.4{\mu}m]-[12{\mu}m]$ color versus $12{\mu}$ luminosity diagram using Wide-field Infrared Survey Explorer data for member galaxies of the A2199 supercluster at $z{\simeq}0.03$. In the MIR color-luminosity diagram, we classify galaxies into three MIR classes: MIR blue cloud (massive, quiescent and mostly early-type), MIR star-forming sequence (mostly late-type), and MIR green valley galaxies. Both MIR green valley galaxies and MIR blue cloud galaxies are optically red sequence populations, and there is no significant difference in star formation rates and stellar masses between them. We compare cumulative distribution functions of surface galaxy number density and of cluster/group-centric distance between three MIR classes. However, when considering only early-type galaxies, the difference between MIR blue cloud galaxies and MIR green valley galaxies disappears. In contrast, the intermediate trend of MIR green valley galaxies is still found for late-type galaxies. We discuss our results concerning the difference of evolution between early- and late-type galaxies and the connection to environment.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.66.2-66.2
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• 2018

The probability distribution function of column density (N-PDF) has been used for studying the characteristics of molecular clouds. In particular, the properties of N-PDF can reveal the nature of turbulence and gravity inside the molecular cloud. We use the dust continuum emission at $450{\mu}m$ and $850{\mu}m$ observed as part of the JCMT Gould Belt Survey (GBS) (Mairs et al. 2016), the 12CO J=1-0 line observed with the 45 m telescope at Nobeyama Radio Observatory (NRO) (Shimajiri et al. 2011), 13CO, C18O and HCO+ J=1-0 observed with the 13.7 m telescope at Taeduk Radio Astronomy Observatory (TRAO), as part of the TRAO key science project, "mapping Turbulent properties In star-forming MolEcular clouds down to the Sonic scale" (TIMES; PI: Jeong-Eun Lee). We here present the N-PDFs derived from the continuum and the molecular line emission toward the Orion A molecular cloud and compare their behaviors in order to investigate the chemical and optical depth effects on the N-PDF.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.51.1-51.1
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• 2016

We revisited a recent dust emission observed at a main-belt asteroid P/2010 A2 in terms of dynamical properties of dust particles and large fragments. This is a continued research that we made a presentation at the Korean Astronomical Society 2016 Spring Meeting, but we have strengthened the dynamical analysis of the ejecta to afford the conclusive evidence for the enigmatic phenomenon. We thus constructed a model to reproduce the morphology of the dust cloud based on the dust dynamics, and succeeded in reproducing the observed morphologies in different epochs over several years. For further analysis, we reconstructed the proper motion of large fragments with respect to the dust emission source estimated from our dust model. We found that (i) the dust cloud morphologies and (ii) observed trajectories of fragments are reasonably explained only when we assumed that both were ejected from a position where no object was detected from any observations. This result suggests that the original body was shattered by an impact, leaving only debris into space. In this presentation, we will compare our results with impact laboratory studies and provide an impact interpretation of the P/2010 A2 activity.