• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1258-1260
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• 2003

Cloud's properties can be showed on different spectral channel. The 0.65${\mu}$m reflectance is mainly function of cloud optical thickness and reflectance of 1.6${\mu}$m is sensitive to cloud phase and particle size distribution. So we can use multi-spectral information to analysis cloud's microphysical properties.

• Journal of Information Technology Services
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• v.11 no.3
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• pp.287-304
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• 2012

Cloud computing, which means a new way of deploying information technology(IT) in organizations as a service and charging per use, has a deep impact on organizations' IT accessibility, agility and efficiency of its usage. More than that, the emergence of cloud computing surpasses a mere technological innovation, making business model innovation possible. We call this innovation realized by could computing a cloud business model. This study develops a comprehensive framework of business model, first, and then defines and analyzes the cloud business model through this framework. This study also examines the case of M-Pesa mobile payment as a cloud business model in which a new value creation and profit realization schemes have been realized and industry value network has changed. Finally, this study discusses the business implications from this new business model.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.E2
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• pp.78-88
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• 2006

In order to study the characteristics of cloud, a real-scale experiment for cloud generation was carried out using an extinct vertical mine (430 m height) located in the northeastern Honshu, Japan. The dry particles generated from the three-step concentrations of NaCl solutions were used for cloud generation. The number size distributions of initial dry particles and cloud droplets were monitored by Scanning Mobility Particle Sizer (SMPS) and Forward Scattering Spectrometer Probe (FSSP) at bottom and upper sites of pit, respectively. The polymeric water absorbent film (PWAF) method was employed to measure liquid water content ($W_L$) as a function of droplet size. Moreover the chemical properties of individual droplet replicas were determined by micro-PIXE. The CCN number concentration shows the lognormal form in dependence of the particle size, while the number size distributions of droplets are bimodal showing the peaks around $9{\mu}m$ and $20{\mu}m$ for every case. In comparison to background mineral particles, right shifting of size distribution line for NaCl particles was occurred. When NaCl solutions with three-step different concentrations were neulized, $W_L$ shows the strong droplet size dependence. It varied from $10.0mg\;m^{-3}$ up to $13.6mg\;m^{-3}$ with average $11.6mg\;m^{-3}$. A good relationship between $W_L$ and cloud droplet number concentration was obtained. Both chemical inhomogeneities (mixed components with mineral and C1) and homogeneities (only mineral components or C1) in individual droplet replicas were obviously observed from micro-PIXE elemental images.

• The Journal of Bigdata
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• v.4 no.1
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• pp.129-142
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• 2019

Construction projects have experienced many failures due to incomplete production environments. Thus, the purpose of this study is to use ICT resources leased during the construction period at the construction site and to introduce the Mobile Cloud Computing Service, which utilizes Cloud Computing Service and mobile devices such as smart phones, tablet PCs, and notebooks instead of physically wired communication networks. The characteristics of Mobile Cloud, such as rapid accuracy, shared collaboration, and ubiquity, will affect the social network among various construction site participants. we conducted empirical research on the introduction of Mobile Cloud to promote information exchange and convergence among the participants and mutual trust, ultimately improving the project performance.

• Journal of Service Research and Studies
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• v.2 no.1
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• pp.61-68
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• 2012

Much technologies building cloud computing environment and supporting sevices on the cloud computing have been developing. Through the environment, accessing new services and sharing knowledge become easy. So far, they have just focused on companies which can support services and people who can use those services. In other words, the environment and models for cloud computing are the most important issue. However, the environment changes rapidly, mobile devices that are connected with each other not only will replace the computing environment based on desktop, but also can create Big data. Therefore, technologies and models are need to follow the trend including mobile based cloud computing environment. In this paper, we explain the cloud computing technologies and trend. Also we address Machine to Machine(M2M) technology and Web of things(WoT) in order to apply those into the cloud computing environment because these two concepts will enhance effectiveness and service reusability in the coming days.

• Journal of the Korea Society for Simulation
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• v.21 no.3
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• pp.35-42
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• 2012

Defense Modeling and Simulation aims to provide a computerized war environment where we can analyze weapon systems realistically. As we invest significant efforts to represent weapon systems and their operational environments on the computer, there has been an increasing need to reuse predefined weapon models. In this paper, we introduce OB-Cloud (Ontology-Based Cloud storage) to utilize predefined weapon models. OB-Cloud has been implemented as a repository for OpenSIM (Open Simulation engine for Interoperable Models), which is an integrated simulation environment for aiding weapons effectiveness analysis, under the development of our research team. OB-Cloud uses weapon ontology and thesaurus dictionaries to provide semantic search for reusable models. In this paper, we present repository services of OB-Cloud, including registration of weapon models and semantic retrieval of similar models, and illustrate how we can improve reusability of weapon models, through an example.

• Journal of The Korean Astronomical Society
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• v.31 no.2
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• pp.161-171
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• 1998

We have mapped about 1.5 square degree regions of Lynds 1299, a well isolated dark cloud in the Outer Galaxy (l = $122^{\circ}$, b = $-7^{\circ}$), in the J = 1- 0 transition of $^{12}CO$ and $^{13}CO$ with the 13.7 m radio telescope at Taeduk Radio Astronomy Observatory (TRAO). We found that there are two velocity components in the molecular emission, at $V_{LSR} = -52 km S^{-1}$ (Cloud A) and -8.8 km $s^{-1}$ (Cloud B), respectively. We have derived physical parameters of two molecular clouds and discussed three different mass estimate techniques. We found that there are large discrepancies between the virial and LTE mass estimates for both clouds. The large virial mass estimate reflects the fact that both are not gravitationally bound. We adopt the mass of $5.6 {\times}10^3 \;M{\bigodot}$ for Cloud A and $1.2{\times}10^3 \;M{\bigodot}$) for Cloud B using conversion factor. Cloud A is found to be associated with a localized star forming site, and its morphology is well matching with that of far-infrared (FIR) dust emission. It shows a clear ring structure with an obvious velocity gradient. We suggest that it may be a remnant cloud from a past episode of massive star formation. Cloud B is found to be unrelated to Cloud A (d = 800 pc) and has no specific velocity structure. The average dust color temperature of the uncontaminated portion of Cloud A is estimated to be 24$\~$27.4 K. The low dust temperature may imply that there is no additional internal heating source within the cloud. The heating of the cloud is probably dominated by the interstellar radiation field except the region directly associated with the new-born B5 star. Overall, the dust properties of Cloud A are similar to those of normal dark cloud even though it does have star forming activity.

• KIPS Transactions on Computer and Communication Systems
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• v.8 no.2
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• pp.41-56
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• 2019

Cloud manufacturing is a new concept of manufacturing process that works like a single factory with connected multiple factories. The cloud manufacturing system is a kind of large-scale CPS that produces products through the collaboration of distributed manufacturing facilities based on technologies such as cloud computing, IoT, and virtualization. It utilizes diverse and distributed facilities based on centralized information systems, which allows flexible composition user-centric and service-oriented large-scale systems. However, the cloud manufacturing system is composed of a large number of highly heterogeneous subsystems. It has difficulties in interconnection, data exchange, information processing, and system verification for system construction. In this paper, we derive the user requirements of various aspects of the cloud manufacturing system, such as functional, human, trustworthiness, timing, data and composition, based on the CPS Framework, which is the analysis methodology for CPS. Next, by analyzing the user requirements we define the system requirements including scalability, composability, interactivity, dependability, timing, interoperability and intelligence. We map the defined CPS system requirements to the requirements of oneM2M, which is the platform standard for IoT, so that the support of the system requirements at the level of the IoT platform is verified through Mobius, which is the implementation of oneM2M standard. Analyzing the verification result, finally, we propose a large-scale cloud manufacturing platform based on oneM2M that can meet the cloud manufacturing requirements to support the overall features of the Cloud Manufacturing CPS with dependability.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.3
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• pp.397-406
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• 2022

Recently, research and development to revitalize smart construction are being actively carried out. Accordingly, 3D mapping technology that digitizes construction site is drawing attention. To create a 3D digital map for construction site a point cloud generation method based on LiDAR(Light detection and ranging) using MMS(Mobile mapping system) is mainly used. The purpose of this study is to analyze the accuracy of MMS LiDAR-based point cloud data. As a result, accuracy of MMS point cloud data was analyzed as dx = 0.048m, dy = 0.018m, dz = 0.045m on average. In future studies, accuracy comparison of point cloud data produced via UAV(Unmanned aerial vegicle) photogrammetry and MMS LiDAR should be studied.

• Asian Journal of Atmospheric Environment
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• v.8 no.4
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• pp.175-183
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• 2014

To visually and chemically verify the rainout of soot particles, a model experiment was carried out with the cylindrical chamber (0.2 m (D) and 4 m (H)) installing a cloud drop generator, a hydrotherometer, a particle counter, a drop collector, a diffusing drier, and an artificial soot particle distributer. The processes of the model experiment were as follows; generating artificial cloud droplets (major drop size : $12-14{\mu}m$) until supersaturation reach at 0.52%-nebulizing of soot particles (JIS Z 8901) with an average size of $0.5{\mu}m$-counting cloud condensation nuclei (CCN) particles and droplets by OPC and the fixation method (Ma et al., 2011; Carter and Hasegawa, 1975), respectively - collecting of individual cloud drops - observation of individual cloud drops by SEM - chemical identifying of residual particle in each individual droplet by SEM-EDX. After 10 minutes of the completion of soot particle inject, the number concentrations of PM of all sizes (> $0.3{\mu}m$) dramatically decreased. The time required to return to the initial conditions, i.e., the time needed to CCN activation for the fed soot particles was about 40 minutes for the PM sized from $0.3-2.0{\mu}m$. The EDX spectra of residual particles left at the center of individual droplet after evaporation suggest that the soot particles seeded into our experimental chamber obviously acted as CCN. The coexistence of soot and mineral particle in single droplet was probably due to the coalescence of droplets (i.e., two droplets embodying different particles (in here, soot and background mineral particles) were coalesced) or the particle capture by a droplet in our CCN chamber.