• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.716-717
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• 2017

In this paper, we propose a modeling approach for marine environment monitoring by leveraging open-source software to link IoT and Cloud together. The proposed model can be scale out by employing Apache Hadoop-based time-series database so that it can handle collected data increase with a resource pool of the same computers. It can also support the analyze monitored data of marine environment by visualizing collected data.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.123-141
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• 2019

The goal of this study is to review the major research trend on the convergence studies of AI and healthcare technologies. For the study, 15,260 English articles on AI and healthcare related topics were collected from Scopus for 55 years from 1963, and text mining techniques were conducted. As a result, seven key research topics were defined : "AI for Clinical Decision Support System (CDSS)", "AI for Medical Image", "Internet of Healthcare Things (IoHT)", "Big Data Analytics in Healthcare", "Medical Robotics", "Blockchain in Healthcare", and "Evidence Based Medicine (EBM)". The result of this study can be utilized to set up and develop the appropriate healthcare R&D strategies for the researchers and government. In this study, text mining techniques such as Text Analysis, Frequency Analysis, Topic Modeling on LDA (Latent Dirichlet Allocation), Word Cloud, and Ego Network Analysis were conducted.

• Water for future
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• v.28 no.6
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• pp.229-236
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• 1995

The physically based heavy rainfall model developed by Ceon(1994) for storm events is modified in this study. The main parts of this paper are composed of modeling saturation vapor pressure, cloud thickness, cloud top pressure. In a different way from the previous model, cloud top temperature and albedo measured by satellite are used as input data to the model. In this paper, the defect of saturation vapor pressure equation in the previous model was improved. Furthermore, the parameters for temperature and pressure on cloud top are eliminated as well as the time of calculation in the model is decreased. Also, the results show that there are very small gab between the hourly calculated.

• Knowledge Management Research
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• v.16 no.1
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• pp.117-142
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• 2015

Recently, the personal cloud computing service has been being spotlighted as an individual tool of productivity enhancement. However, compared to the rosy forecast, its diffusion rate in the domestic (Korean) market is much slower than expected. In order to find the reason for the slow growth of personal cloud computing service, we attempt to identify influencing factors on user's adoption resistance, while most prior research has focused on the factors affecting its adoption. Based on both the person-technology fit model and the privacy calculus model, we propose technostress and perceived value as key antecedents of adoption resistance. In addition, we identify (1) technical (pace of change and complexity) and personal (self-efficacy) influencing factors on technostress, and (2) beneficial (perceived mobility and perceived availability) and harmful (perceived vulnerability) influencing factors on perceived value. To validate our research model, 133 individual samples were gathered from undergraduate and graduate students who had actual experience of using at least one of personal cloud computing services. The results of the structural equation modeling confirm that both technostress and perceived value have significant effects on adoption resistance, but they have different influencing mechanisms to different types of adoption resistance (indifference, postponement, and rejection). Theoretical and practical contributions are discussed in the conclusion.

• Journal of KIBIM
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• v.11 no.4
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• pp.1-9
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• 2021

Comparing to general scanning data, the 3D digital map for large construction sites and complex buildings consists of millions of points. The large construction site needs to be scanned multiple times by drone photogrammetry or terrestrial laser scanner (TLS) survey. The scanned point cloud data are required to be registrated with high resolution and high point density. Unlike the registration of 2D data, the matrix of translation and rotation are used for registration of 3D point cloud data. Archiving high accuracy with 3D point cloud data is not easy due to 3D Cartesian coordinate system. Therefore, in this study, iterative closest point (ICP) registration method for improve accuracy of 3D digital map was employed by different overlap ratio on 3D digital maps. This study conducted the accuracy test using different overlap ratios of two digital maps from 10% to 100%. The results of the accuracy test presented the optimal overlap ratios for an ICP registration method on digital maps.

• Korean Journal of Remote Sensing
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• v.24 no.4
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• pp.333-340
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• 2008

For the indoor spatial modeling by terrestrial LiDAR and the analyzing its positional accuracy result, two terrestrial LiDARs which have different specification each other were used at test site. This paper shows disparity of accuracy between (1) the structural coordinate transformation by point cloud unit using control points and (2) the relative registration among all point cloud units then structural coordinate transformation in bulk, under condition of limited number of control points. As results, the latter had smaller size and distribution of errors than the former although different specifications and acquistion methods are used.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.1
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• pp.1-7
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• 2019

The objective of this study is to examine the trends in big data. Research abstracts were extracted from 4,019 articles, published between 1995 and 2018, on Web of Science and were analyzed using topic modeling and time series analysis. The 20 single-term topics that appeared most frequently were as follows: model, technology, algorithm, problem, performance, network, framework, analytics, management, process, value, user, knowledge, dataset, resource, service, cloud, storage, business, and health. The 20 multi-term topics were as follows: sense technology architecture (T10), decision system (T18), classification algorithm (T03), data analytics (T17), system performance (T09), data science (T06), distribution method (T20), service dataset (T19), network communication (T05), customer & business (T16), cloud computing (T02), health care (T14), smart city (T11), patient & disease (T04), privacy & security (T08), research design (T01), social media (T12), student & education (T13), energy consumption (T07), supply chain management (T15). The time series data indicated that the 40 single-term topics and multi-term topics were hot topics. This study provides suggestions for future research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.4
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• pp.416-422
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• 2019

This paper proposes a GPU-based modeling and rendering of 3D clouds using procedural functions. The formation of clouds is based on modified noise function made with fbm(Fractional Brownian Motion). Those noise values turn into densities of droplets of liquid water, which is a critical parameter for forming the three different types of clouds. At the rendering stage, the algorithm applies the ray marching technique to decide the colors of cloud using density values obtained from the noise function. In this process, all lighting attenuation and scattering are calculated by physically based manner. Once we have the clouds, they are blended on the sky, which is also rendered physically. We also make the clouds moving in the sky by the wind force. All algorithms are implemented and tested on GPU using GLSL.

• International Journal of Contents
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• v.13 no.2
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• pp.29-34
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• 2017

Cloud computing is becoming an effective and efficient way of computing resources and computing service integration. Through centralized management of resources and services, cloud computing delivers hosted services over the internet, such that access to shared hardware, software, applications, information, and all resources is elastically provided to the consumer on-demand. The main enabling technology for cloud computing is virtualization. Virtualization software creates a temporarily simulated or extended version of computing and network resources. The objectives of virtualization are as follows: first, to fully utilize the shared resources by applying partitioning and time-sharing; second, to centralize resource management; third, to enhance cloud data center agility and provide the required scalability and elasticity for on-demand capabilities; fourth, to improve testing and running software diagnostics on different operating platforms; and fifth, to improve the portability of applications and workload migration capabilities. One of the key features of cloud computing is elasticity. It enables users to create and remove virtual computing resources dynamically according to the changing demand, but it is not easy to make a decision regarding the right amount of resources. Indeed, proper provisioning of the resources to applications is an important issue in IaaS cloud computing. Most web applications encounter large and fluctuating task requests. In predictable situations, the resources can be provisioned in advance through capacity planning techniques. But in case of unplanned and spike requests, it would be desirable to automatically scale the resources, called auto-scaling, which adjusts the resources allocated to applications based on its need at any given time. This would free the user from the burden of deciding how many resources are necessary each time. In this work, we propose an analytical and efficient VM-level scaling scheme by modeling each VM in a data center as an M/M/1 processor sharing queue. Our proposed VM-level scaling scheme is validated via a numerical experiment.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1527-1543
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• 2021

In this paper, we explore the applicability and utility of a technology that generating clustered solid building models based on point cloud automatically by applying it to various data. In order to improve the quality of the model of insufficient quality due to the limitations of the automatic building modeling technology, we develop the building shape modification and texture correction technology and confirmed the resultsthrough experiments. In order to explore the applicability of automatic building model generation technology, we experimented using point cloud and LiDAR (Light Detection and Ranging) data generated based on UAV, and applied building shape modification and texture correction technology to the automatically generated building model. Then, experiments were performed to improve the quality of the model. Through this, the applicability of the point cloud data-based automatic clustered solid building model generation technology and the effectiveness of the model quality improvement technology were confirmed. Compared to the existing building modeling technology, our technology greatly reduces costs such as manpower and time and is expected to have strengths in the management of modeling results.