• Journal of Platform Technology
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• v.11 no.3
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• pp.3-11
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• 2023

Real-time control systems are growing rapidly as infrastructure technologies such as IoT and mobile communication develop and services that value real-time such as factory management and vehicle operation checks increase. Various solutions have been proposed to increase the time sensitivity of this system, but most real-time control systems are currently composed of local servers and multiple clients located in control stations, which are transmitted to local servers where control systems are located. In this paper, we proposed an edge computing-based real-time control model that can reduce the time it takes for the bus information system, one of the real-time control systems, to provide the information to the user at the time it collects the information. Simulating the existing model and the edge computing model, the edge computing model confirmed that the cost for users to receive data is reduced from at least 10% to up to 80% compared to the existing model.

• Nuclear Engineering and Technology
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• v.48 no.1
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• pp.274-284
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• 2016

Purpose: The purpose of this work is to describe the spectrometric analysis of gaseous cloud formation over reactor mixed uranium-and-plutonium (UP) fuel $(U_{0.8}Pu_{0.2})O_2$ samples heated to a temperature $>2,000^{\circ}C$, and thus forecast and evaluate radiation hazards threatening humans who cope with the consequences of any accident at a fission reactor loaded by UP mixed oxide $(U_{0.8}Pu_{0.2})O_2$, such as a mixture of 80% U and 20% Pu in weight. Materials and methods: The UP nuclear fuel samples were heated up to a temperature of over $2,000^{\circ}C$ in a suitable assembly (apparatus) at out-of-pile experiments' implementation, the experimental in-depth study of metabolism of active materials in living organisms by means of artificial irradiation of pigs by plutonium. Spectrometric measurements were carried out on the different exposed organs and tissues of pigs for the further estimation of human internal exposure by nuclear materials released from the core of a fission reactor fueled with UP mixed oxide. Results: The main results of the research described are the following: (1) following the research on the influence of mixed fuel fission products (radioactive isotopes being formed during reactor operation as a result of nuclear decay of elements included into the fuel composition) on living organisms, the authors determined the quantities of plutonium dioxide ($PuO_2$) that penetrated into blood and lay in the pulmonary region, liver, skeleton and other tissues; and (2) experiments confirmed that the output speed of plutonium out of the basic precipitation locations is very small. On the strength of the experimental evidence, the authors suggest that the biological output of plutonium can be disregarded in the process of evaluation of the internal irradiation doses.

• Journal of Korea Multimedia Society
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• v.16 no.2
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• pp.234-240
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• 2013

All around the world, numerous scientists have been carried out researches of e-Science to improve performance of computations and accessibility of their experimental flexibilities for a long times. However, they still have been in difficulty securing high-performance computing facilities. In case of Aerodynamics, for example, a single experiment costs a tremendous amount of budget and requires a span of more than 6 months even though researchers have been developed diverse improved mathematical methods as well as relied on advanced computing technologies to reduce runtime and costs. In this paper, we proposed a multiple infrastructure-based scientific workflow environments for engineering education in fields of design optimization of aircraft and demonstrated the superiority. Since it offers diverse kind of computing resources, it can offer elastic resources regardless of the number of tasks for experiments and limitations of spaces. Also, it can improve education efficiency by using this environment to engineering education.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.1
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• pp.69-78
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• 2015

Recently, due to the development of next-generation computing devices and high-performance network, VDI (Virtual Desktop Infrastructure) is receiving a great deal of attention from IT market as an essential part of cloud computing. VDI enables to host multiple, individual virtual machines that are provisioned from servers located at the data center by using hypervisor. One of the critical issues related to VDI is to reduce the performance difference between virtual machines and physical ones. In this paper, we present a real-time VM monitoring system, called SETMOV, that is able to collect the real-time resource usage information. We also present the performance results using iozone to verify SETMOV.

• Journal of Cadastre & Land InformatiX
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• v.49 no.2
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• pp.67-82
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• 2019

The purpose of this study is to analyze the issues and perceptions of smart cities. First, based on the big data analysis platform, big data analysis on smart cities were conducted to derive keywords by year, word cloud, and frequency of generation of smart city keywords by time. Second, trend and flow by area were analyzed by reclassifying major keywords by year based on meta-keywords. Third, emotional recognition flow for smart cities and major emotional keywords were derived. While U-City in the past is mostly centered on creating infrastructure for new towns, recent smart cities are focusing on sustainable urban construction led by citizens, according to the analysis. In addition, it was analyzed that while infrastructure, service, and technology were emphasized in the past, management and methodology were emphasized recently, and positive perception of smart cities was growing. The study could be used as basic data for the past, present and future of smart cities in Korea at a time when smart city services are being built across the country.

• Journal of Korean Society of Disaster and Security
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• v.5 no.2
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• pp.81-87
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• 2012

Critical infrastructures (energy, information technology and communications, banking, transportation, public government services, etc.) are now more vital to modern society. Citizens, businesses and governments all rely on an array of interlinked physical and information infrastructures to satisfy their needs and perform their daily operations. At the same time, these infrastructures are becoming increasingly interdependent, such that failure of one of them can often propagate and result in domino effects. Recent dramatic episodes, from 9/11 to the Madrid train bombings, the April 2010 ash cloud the power cuts in Korea in 2011, and the cyber-attacks have highlighted the need for a comprehensive, internationally coordinated policy for the protection of critical infrastructures. For the purposes of this report, we define critical infrastructure as infrastructure whose failure would result in substantial damage to society and/or the economy.

• Ecology and Resilient Infrastructure
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• v.8 no.4
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• pp.233-244
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• 2021

This study investigated the application of terrestrial light detection and ranging (LiDAR) to inspect the defects of the vegetated levee. The accuracy of vegetation filtering techniques was compared by applying filtering techniques on photogrammetric point clouds of a vegetated levee generated by terrestrial LiDAR. Representative 10 vegetation filters such as CIVE, ExG, ExGR, ExR, MExG, NGRDI, VEG, VVI, ATIN, and ISL were applied to point cloud data of the Imjin River levee. The accuracy order of the 10 techniques based on the results was ISL, ATIN, ExR, NGRDI, ExGR, ExG, MExG, VVI, VEG, and CIVE. Color filters show certain limitations in the classification of vegetation and ground and classify grass flower image as ground. Morphological filters show a high accuracy of the classification, but they classify rocks as vegetation. Overall, morphological filters are superior to color filters; however, they take 10 times more computation time. For the improvement of the vegetation removal, combined filters of color and morphology should be studied.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.4
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• pp.661-670
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• 2023

With the emergence of serverless computing paradigm and the innovations of cloud technology, the structure of backend server infrastructure has evolved from on-premises to container-based serverless computing. However, an access control on the server still heavily relies on the traditional SSH protocol, which poses limitations in terms of security and scalability. This hampers user convenience and productivity in managing server infrastructure. A web shell is an interface that allows easy access to servers and execution of commands from any device with a web browser. While hackers often use it to exploit vulnerabilities in servers, we pay attention to the high portability of web shell technology for server management. This study proposes a novel proxy-based server management framework utilizing web shell technology. Our evaluation demonstrates that the proposed framework addresses the drawbacks of SSH without additional overhead, and efficiently operates large-scale infrastructures in diverse computing environments.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.71-84
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• 2013

Log data, which record the multitude of information created when operating computer systems, are utilized in many processes, from carrying out computer system inspection and process optimization to providing customized user optimization. In this paper, we propose a MongoDB-based unstructured log processing system in a cloud environment for processing the massive amount of log data of banks. Most of the log data generated during banking operations come from handling a client's business. Therefore, in order to gather, store, categorize, and analyze the log data generated while processing the client's business, a separate log data processing system needs to be established. However, the realization of flexible storage expansion functions for processing a massive amount of unstructured log data and executing a considerable number of functions to categorize and analyze the stored unstructured log data is difficult in existing computer environments. Thus, in this study, we use cloud computing technology to realize a cloud-based log data processing system for processing unstructured log data that are difficult to process using the existing computing infrastructure's analysis tools and management system. The proposed system uses the IaaS (Infrastructure as a Service) cloud environment to provide a flexible expansion of computing resources and includes the ability to flexibly expand resources such as storage space and memory under conditions such as extended storage or rapid increase in log data. Moreover, to overcome the processing limits of the existing analysis tool when a real-time analysis of the aggregated unstructured log data is required, the proposed system includes a Hadoop-based analysis module for quick and reliable parallel-distributed processing of the massive amount of log data. Furthermore, because the HDFS (Hadoop Distributed File System) stores data by generating copies of the block units of the aggregated log data, the proposed system offers automatic restore functions for the system to continually operate after it recovers from a malfunction. Finally, by establishing a distributed database using the NoSQL-based Mongo DB, the proposed system provides methods of effectively processing unstructured log data. Relational databases such as the MySQL databases have complex schemas that are inappropriate for processing unstructured log data. Further, strict schemas like those of relational databases cannot expand nodes in the case wherein the stored data are distributed to various nodes when the amount of data rapidly increases. NoSQL does not provide the complex computations that relational databases may provide but can easily expand the database through node dispersion when the amount of data increases rapidly; it is a non-relational database with an appropriate structure for processing unstructured data. The data models of the NoSQL are usually classified as Key-Value, column-oriented, and document-oriented types. Of these, the representative document-oriented data model, MongoDB, which has a free schema structure, is used in the proposed system. MongoDB is introduced to the proposed system because it makes it easy to process unstructured log data through a flexible schema structure, facilitates flexible node expansion when the amount of data is rapidly increasing, and provides an Auto-Sharding function that automatically expands storage. The proposed system is composed of a log collector module, a log graph generator module, a MongoDB module, a Hadoop-based analysis module, and a MySQL module. When the log data generated over the entire client business process of each bank are sent to the cloud server, the log collector module collects and classifies data according to the type of log data and distributes it to the MongoDB module and the MySQL module. The log graph generator module generates the results of the log analysis of the MongoDB module, Hadoop-based analysis module, and the MySQL module per analysis time and type of the aggregated log data, and provides them to the user through a web interface. Log data that require a real-time log data analysis are stored in the MySQL module and provided real-time by the log graph generator module. The aggregated log data per unit time are stored in the MongoDB module and plotted in a graph according to the user's various analysis conditions. The aggregated log data in the MongoDB module are parallel-distributed and processed by the Hadoop-based analysis module. A comparative evaluation is carried out against a log data processing system that uses only MySQL for inserting log data and estimating query performance; this evaluation proves the proposed system's superiority. Moreover, an optimal chunk size is confirmed through the log data insert performance evaluation of MongoDB for various chunk sizes.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.12-26
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• 2017

The objective of this study was to develop a runoff analysis system of the Nakdong River watershed using the GRM (Grid-based Rainfall-runoff Model), a physically-based distributed rainfall-runoff model, and to assess the system run time performance according to Microsoft Azure VM (Virtual Machine) settings. Nakdong River watershed was divided into 20 sub-watersheds, and GRM model was constructed for each subwatershed. Runoff analysis of each watershed was calculated in separated CPU process that maintained the upstream and downstream topology. MoLIT (Ministry of Land, Infrastructure and Transport) real-time radar rainfall and dam discharge data were applied to the analysis. Runoff analysis system was run in Azure environment, and simulation results were displayed through web page. Based on this study, the Nakdong River real-time runoff analysis system, which consisted of a real-time data server, calculation node (Azure), and user PC, could be developed. The system performance was more dependent on the CPU than RAM. Disk I/O and calculation bottlenecks could be resolved by distributing disk I/O and calculation processes, respectively, and simulation runtime could thereby be decreased. The study results could be referenced to construct a large watershed runoff analysis system using a distributed model with high resolution spatial and hydrological data.