• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.689-692
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• 2006

This paper presents the developments of Grid computing architecture which is use many data and resources from distributed and parallel system for construction test equipments, i.e., large scale computer networks meant to provide access to massive computational facilities for very large communities of users, drawing upon experiences of existing Grids architecture. In this paper, we present an efficient way to construct a construction test equipment infrastructure.

• Educational Technology International
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• v.7 no.2
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• pp.69-92
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• 2006

The research question in this study was to see whether there were similarities and differences in HR practices of IT-based companies in Korea. Interviews with 12 HR executives and a survey of 51 IT-based companies showed that there were IT industry-specific HR practices, but no group differences except one. The only one difference between two groups (small- and medium-sized firms versus multinational corporations) indicated that the latter was better positioned in various HR practices. Four HR themes emerged from analyses of interviews and a survey data: business environment, workforce management, HR practices, and HR for competitive advantage. The fact that none from large companies was found to have female HR executive calls for an in-depth study of what is occurring and what could be done to better the workplace in terms of diversity. A large-scale study using diverse methods in the future will produce more meaningful findings on HR practices in IT-based firms in Korea.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.11a
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• pp.237-238
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• 2023

Urbanization and the increase in building scale have amplified the complexity of M.E.P design. Traditional design methods face limitations when considering intricate pathways and variables, leading to an emergent need for research in automated design. Initial algorithmic approaches encountered challenges in addressing complex architectural structures and the diversity of M.E.P types. However, with the launch of OpenAI's ChatGPT-3.5 beta version in 2022, new opportunities in the automated design sector were unlocked. ChatGPT, based on the Large Language Model (LLM), has the capability to deeply comprehend the logical structures and meanings within training data. This study analyzed the potential application and latent value of LLMs in M.E.P design. Ultimately, the implementation of LLM in M.E.P design will make genuine automated design feasible, which is anticipated to drive advancements across designs in the construction sector.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.225-234
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• 2015

Nowadays a Virtual Power Plant (VPP) represents an aggregation of distributed energy resource such as Distributed Generation (DG), Combined Heat and Power generation (CHP), Energy Storage Systems (ESS) and load in order to operate as a single power plant by using Information and Communication Technologies, ICT. The VPP has been developed and verified based on a single virtual plant platform which is connected with a number of various distributed energy resources. As the VPP's distributed energy resources increase, so does the number of data from distributed energy. Moreover, it is obviously inefficient in the aspects of technique and cost that a virtual plant platform operates in a centralized manner over widespread region. In this paper the concept of the large-scale VPP which can reduce a error probability of system's load and increase the robustness of data exchange among distributed energy resources will be proposed. In addition, it can directly control and supervise energy resource by making small size's virtual platform which can make a optimal resource scheduling to consider of variable and sensitive load in the large-scale VPP. It makes certain the result is verified by simulation.

• Journal of Korea Multimedia Society
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• v.8 no.7
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• pp.924-942
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• 2005

Recently, large-scale directories have been developed for LINUX cluster file systems to store and retrieve huge amount of data. One of them, GFS directory, has attracted much attention because it is based on extendible hashing, one of dynamic hashing techniques, to support fast access to files. One distinctive feature of the GFS directory is the flat structure where all the leaf nodes are located at the same level of the tree. Hut one disadvantage of the mode structure is that the height of the mode tree has to be increased to make the tree flat after a byte is inserted to a full tree which cannot accommodate it. Thus, one byte addition makes the height of the whole mode tree grow, and each data block of the new tree needs one more link access than the old one. Another dynamic hashing technique which can be used for directories is linear hashing and a couple of researches have shown that it can get better performance at file access times than extendible hashing. [n this research, we have designed and implemented an extendible hashing directory and a linear hashing directory for large-scale LINUX cluster file systems and have compared performance between them. We have used the semi-flat structure which is known to have better access performance than the flat structure. According to the results of the performance evaluation, the linear hashing directory has shown slightly better performance at file inserts and accesses in most cases, whereas the extendible hashing directory is somewhat better at space utilization.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.2
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• pp.103-116
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• 2017

In this paper, we present the design and implementation of a large-scale qualitative spatial reasoner, which can derive new qualitative spatial knowledge representing both topological and directional relationships between two arbitrary spatial objects in efficient way using Aparch Spark SQL. Apache Spark SQL is well known as a distributed parallel programming environment which provides both efficient join operations and query processing functions over a variety of data in Hadoop cluster computer systems. In our spatial reasoner, the overall reasoning process is divided into 6 jobs such as knowledge encoding, inverse reasoning, equal reasoning, transitive reasoning, relation refining, knowledge decoding, and then the execution order over the reasoning jobs is determined in consideration of both logical causal relationships and computational efficiency. The knowledge encoding job reduces the size of knowledge base to reason over by transforming the input knowledge of XML/RDF form into one of more precise form. Repeat of the transitive reasoning job and the relation refining job usually consumes most of computational time and storage for the overall reasoning process. In order to improve the jobs, our reasoner finds out the minimal disjunctive relations for qualitative spatial reasoning, and then, based upon them, it not only reduces the composition table to be used for the transitive reasoning job, but also optimizes the relation refining job. Through experiments using a large-scale benchmarking spatial knowledge base, the proposed reasoner showed high performance and scalability.

• Journal of the Economic Geographical Society of Korea
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• v.7 no.1
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• pp.97-115
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• 2004

The purpose of this study is to investigate the spatial pattern of retail services and the consumer behaviors. For the purpose we select Jeju Island as the study area, because it retains relatively little distorted retail service systems by it's locational isolation. The retail service systems comprise three types: large-scale modern marts, conventional markets, and periodic markets. This study attempts to examine the interrelationships between these three different types, of retail services, and to figure out the spatial characteristics of consumer behaviors for each of them. We performed questionnaire surveys for getting the data of consumer behaviors. We applied several statistical methods to analyze the survey data. Most of retail services are located in two urban centers, Jeju City and Seoguipo City. We found that the locations of retail services are determined strongly by population size. The selection of market type and the location to go for shopping are related strongly with the types of goods. However, there is a wide difference in the consumer behaviors according to the consumer's socio-economic characteristics. Young wives tend to go shopping to large-scale marts in Jeju City which is the higher level central place, while old wives go shopping to conventional markets and periodic markets. They also show different shopping behaviors according to the household income levels. Low income groups prefer to go conventional markets located near to their residence, middle income groups go to large-scale marts in Jeju, and high income group go out of the Jeju Island. However, the consumer behavior does not show big difference according to the size of family. There are also no difference in the selection for shopping location according to the consumer's resident locations.

• Convergence Security Journal
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• v.21 no.2
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• pp.11-18
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• 2021

To protect tangible and intangible assets, most of the companies are conducting information protection monitoring by using various security equipment in the IT service network. As the security equipment that needs to be protected increases in the process of upgrading and expanding the service network, it is difficult to monitor the possible exposure to the attack for the entire service network. As a countermeasure to this, various studies have been conducted to detect external attacks and illegal communication of equipment, but studies on effective monitoring of the open service ports and construction of illegal communication monitoring system for large-scale service networks are insufficient. In this study, we propose a framework that can monitor information leakage and illegal communication attempts in a wide range of service networks without large-scale investment by analyzing 'Netflow statistical information' of backbone network equipment, which is the gateway to the entire data flow of the IT service network. By using machine learning algorithms to the Netfllow data, we could obtain the high classification accuracy of 94% in identifying whether the Telnet service port of operating equipment is open or not, and we could track the illegal communication of the damaged equipment by using the illegal communication history of the damaged equipment.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1231-1244
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• 2022

The frequency of exposure of field crops to stress situations is increasing due to abnormal weather conditions. In South Korea, large-scale diseases in representative paddy rice cultivation area were happened. There are limits to field investigation on the crop damage due to large-scale. Satellite-based remote sensing techniques are useful for monitoring crops in cities and counties, but the sensitivity of vegetation index measured from satellite under abnormal growth of crop should be evaluated. The goal is to evaluate satellite-based normalized difference vegetation index (NDVI) retrieved from different spatial scales using drone imagery. In this study, Sentinel-2 and Landsat-8 satellites were used and they have spatial resolution of 10 and 30 m. Drone-based NDVI, which was resampled to the scale of satellite data, had correlation of 0.867-0.940 with Sentinel-2 NDVI and of 0.813-0.934 with Landsat-8 NDVI. When the effects of bias were minimized, Sentinel-2 NDVI had a normalized root mean square error of 0.2 to 2.8% less than that of the drone NDVI compared to Landsat-8 NDVI. In addition, Sentinel-2 NDVI had the constant error values regardless of diseases damage. On the other hand, Landsat-8 NDVI had different error values depending on degree of diseases. Considering the large error at the boundary of agricultural field, high spatial resolution data is more effective in monitoring crops.

• Journal of the Korean Geotechnical Society
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• v.23 no.10
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• pp.97-107
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• 2007

Accurate evaluation of the slope stability by assuming failure block as the entire slope is considered to be apposite for the small scale slope, whereas it is not the case for the large scale slope. Hence, appropriate estimation of a failure block size is required since the safety factor and the joint strength parameters are the function of the failure block size. In this paper, the size of failure block was investigated by generating 3-dimensional rock joint system based on statistical data of joints obtained from research slope, such as joint orientation, spacing and 3-dimensional joint intensity. The result indicates that 33 potential failure blocks exist in research slope, as large as 1.4 meters at least and 38.7 meters at most, and average block height is 15.2 meters. In addition, the data obtained from 3 dimensional joint system were directly applicable to the probability analysis and 2 and 3 dimensional discontinuity analysis.