• The Journal of the Convergence on Culture Technology
• /
• v.9 no.3
• /
• pp.153-158
• /
• 2023

The purpose of current study was to identify relations between pregnancy-childbirth infrastructures and intention to childbirth and relocation. We conducted a logistic regression analysis to determine the influence of the pregnancy and childbirth infrastructure level over the people's intentions to have any additional pregnancy and to relocate to other city. The results have showed that the younger the age and the higher the income is, the stronger the intention to have an additional pregnancy becomes, and that of the pregnancy and childbirth infrastructure, only the level of pediatrics service would affect the intention to have another pregnancy. As for the intention to relocate or move to another locations, the results have shown that such intention tends to decline where there are overall sufficient and good pregnancy and childbirth infrastructure in place.

• Proceedings of the Korean Information Science Society Conference
• /
• 2012.06a
• /
• pp.259-261
• /
• 2012

클라우드 컴퓨팅은 정보가 인터넷 상의 서버에 영구적으로 저장되고 데스크탑이나 테이블 컴퓨터, 노트북, 벽걸이 컴퓨터, 휴대용 기기 등과 같은 클라이언트에는 일시적으로 보관되는 패러다임을 뜻한다. 가용성과 사용상의 편의에 대한 요구의 증가로 최근들어 빠른 속도로 발전하는 모습을 보이고 있다. 클라우드 컴퓨팅은 제공하는 서비스의 유형에 따라 IaaS(Infrastructure as a Service), PaaS(Platform as a Service), SaaS(Software as a Service)로 나뉜다. 이 중 IaaS는 인프라를 서비스로 제공하는 모델이다. 이를 구현한 오픈소스 클라우드 플랫폼으로 오픈스택과 클라우드 스택이 대표적이다. 시트릭스는 자사가 개발중인 클라우드 플랫폼인 클라우드스택을 아파치 라이센스로 전환하겠다고 발표했다. 이전까지 같은 종류의 클라우드 플랫폼인 오픈스택의 회원사로 참여하고 있던 시트릭스가 오픈스택 그룹에서 탈퇴하고 클라우드스택을 지원한다는 발표는 큰 관심을 받았다. 본 논문에서는 여러 오픈소스 클라우드 플랫폼 중 오픈스택과 클라우드스택을 비교해보고, 이를 통해 향후 오픈소스 클라우드 플랫폼의 발전가능성에 대해 고찰해보고자 한다.

• International Journal of Internet, Broadcasting and Communication
• /
• v.8 no.1
• /
• pp.33-41
• /
• 2016

Big data management is becoming an increasingly important issue in both industry and academia of information science community today. One of the important categories of big data generated from software systems is log data. Log data is generally used for better services in various service providers and can also be used to improve system reliability. In this paper, we propose a novel big data management architecture specialized for log data. The proposed architecture provides a scalable log management system that consists of client and server side modules for efficient handling of log data. To support large and simultaneous log data from multiple clients, we adopt the Hadoop infrastructure in the server-side file system for storing and managing log data efficiently. We implement the proposed architecture to support various client environments and validate the efficiency through measurement studies. The results show that the proposed architecture performs better than the existing logging architecture by 42.8% on average. All components of the proposed architecture are implemented based on open source software and the developed prototypes are now publicly available.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.8 no.4
• /
• pp.83-90
• /
• 2012

With the rapid growth of internet infrastructure, World Wide Web is evolving recently into various services such as cloud computing, social network services. It simply go beyond the sharing of information. It started to provide new services such as E-business, remote control or management, providing virtual services, and recently it is evolving into new services such as cloud computing and social network services. These kinds of communications through World Wide Web have been interested in and have developed user-centric customized services rather than providing provider-centric informations. In these environments, it is very important to check and analyze the user requests to a website. Especially, estimating user preferences is most important. For these reasons, analyzing web logs is being done, however, it has limitations that the most of data to analyze are based on page unit statistics. Therefore, it is not enough to evaluate user preferences only by statistics of specific page. Because recent main contents of web page design are being made of media files such as image files, and of dynamic pages utilizing the techniques of CSS, Div, iFrame etc. In this paper, large log analyzer was designed and executed to analyze web server log to estimate web contents preferences of users. With mapreduce which is based on Hadoop, large logs were analyzed and web contents preferences of media files such as image files, sounds and videos were estimated.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.13 no.2
• /
• pp.101-107
• /
• 2018

As indoor pollutants such as carbon dioxide and dust mainly affect the respiratory and circulatory systems, there is an increasing need for real-time indoor / outdoor environmental monitoring. In this paper, we have developed a real - time environmental monitoring system using the cloud-based 3G integrated environmental sensor module for environmental monitoring. A highly reliable environmental information monitoring system requires various IT technologies such as infrastructure (server, commercial software, etc.), service application software, security, and authentication. A real-time environment monitoring system based on cloud service that can provide reliable service satisfying these configuration requirements is proposed and implemented. It is expected that this system can be applied to various technologies such as indoor automatic window opening/closing system based on the Internet.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.2
• /
• pp.439-449
• /
• 2019

The development of information technology represented by ICBMA (IoT, Cloud, Big Data, Mobile, AI), is leading to a surge in data and a numerical and quantitative increase in data centers to accommodate it. As the data center is recognized as a social infrastructure, It is very important to identify physical security threats in advance in order to secure safety, such as responding to a terrorist attack. In this paper, we develop physical security threat breakdown structure (PS-TBS) for easy identification and classification of threats, and verify the feasibility and effectiveness of the PS-TBS through expert questionnaires. In addition, we intend to contribute to the improvement of physical security level by practical use in detailed definition on items of PS-TBS.

• The Journal of Information Systems
• /
• v.29 no.4
• /
• pp.123-135
• /
• 2020

Purpose The purpose of this study is to investigate the development process and the effectiveness of the EISS (epidemiological investigation support system), which prevents the spread of infectious diseases like a novel corona virus disease, COVID-19. Design/methodology/approach This study identified the existing epidemiological support system for MERS through prior research and studied the case of the development of a newly developed epidemiological support system based on cloud computing infrastructure for COVID-19 through inter-ministerial collaboration in 2020. Findings The outbreak of COVID-19 drove the Korean Government began the development of the EISS with private companies. This system played a significant role in flattening the spread of infection during several waves in which the number of confirmed cases increased rapidly in Korea, However, we need to be careful in handling confirmed patients' private data affecting their privacy.

• Journal of KIBIM
• /
• v.13 no.4
• /
• pp.54-63
• /
• 2023

In recent years, the mandatory use of BIM has been actively promoted due to the digital transformation of the construction industry. However, the CDE (Common Data Environment) system, which is an essential element for operating BIM, has not been established in accordance with the domestic situation. To solve this problem, this study analyzed the results of previous studies, including the ISO 19650 standard and domestic CDE system requirements, and developed BIM-based collaboration functions that are suitable for the domestic construction industry through functional analysis of domestic and foreign commercial CDE solutions. And we developed a BIM collaboration framework to provide BIM-based collaboration functions as a service by using cloud technologies such as IaaS, PaaS, and SaaS to provide infrastructure resources flexibly and flexibly. The BIM collaboration framework developed in this study meets most of the CDE requirements of ISO1965, so it can secure competitiveness when bidding for overseas BIM projects. Also, because the BIM collaboration functions can be selectively applied to build a BIM-based collaboration platform, it is expected that the utilization of the BIM collaboration framework will be high, as it can minimize not only the time to build the platform but also the operating costs, and the usability is higher than that of existing commercial BIM CDE solutions.

• KIPS Transactions on Computer and Communication Systems
• /
• v.10 no.10
• /
• pp.261-268
• /
• 2021

Cloud computing is a computing paradigm in which users can utilize computing resources in a pay-as-you-go manner. In a cloud system, resources can be dynamically scaled up and down to the user's on-demand so that the total cost of ownership can be reduced. The Modeling and Simulation (M&S) technology is a renowned simulation-based method to obtain engineering analysis and results through CAE software without actual experimental action. In general, M&S technology is utilized in Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Multibody dynamics (MBD), and optimization fields. The work procedure through M&S is divided into pre-processing, analysis, and post-processing steps. The pre/post-processing are GPU-intensive job that consists of 3D modeling jobs via CAE software, whereas analysis is CPU or GPU intensive. Because a general-purpose desktop needs plenty of time to analyze complicated 3D models, CAE software requires a high-end CPU and GPU-based workstation that can work fluently. In other words, for executing M&S, it is absolutely required to utilize high-performance computing resources. To mitigate the cost issue from equipping such tremendous computing resources, we propose HEMOS-Cloud service, an integrated cloud and cluster computing environment. The HEMOS-Cloud service provides CAE software and computing resources to users who want to experience M&S in business sectors or academics. In this paper, the economic ripple effect of HEMOS-Cloud service was analyzed by using industry-related analysis. The estimated results of using the experts-guided coefficients are the production inducement effect of KRW 7.4 billion, the value-added effect of KRW 4.1 billion, and the employment-inducing effect of 50 persons per KRW 1 billion.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_3
• /
• pp.939-951
• /
• 2022

The most important factor in designing autonomous driving systems is to recognize the exact location of the vehicle within the surrounding environment. To date, various sensors and navigation systems have been used for autonomous driving systems; however, all have limitations. Therefore, the need for high-definition (HD) maps that provide high-precision infrastructure information for safe and convenient autonomous driving is increasing. HD maps are drawn using three-dimensional point cloud data acquired through a mobile mapping system (MMS). However, this process requires manual work due to the large numbers of points and drawing layers, increasing the cost and effort associated with HD mapping. The objective of this study was to improve the efficiency of HD mapping by segmenting semantic information in an MMS point cloud into six classes: roads, curbs, sidewalks, medians, lanes, and other elements. Segmentation was performed using various machine learning techniques including random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and gradient-boosting machine (GBM), and 11 variables including geometry, color, intensity, and other road design features. MMS point cloud data for a 130-m section of a five-lane road near Minam Station in Busan, were used to evaluate the segmentation models; the average F1 scores of the models were 95.43% for RF, 92.1% for SVM, 91.05% for GBM, and 82.63% for KNN. The RF model showed the best segmentation performance, with F1 scores of 99.3%, 95.5%, 94.5%, 93.5%, and 90.1% for roads, sidewalks, curbs, medians, and lanes, respectively. The variable importance results of the RF model showed high mean decrease accuracy and mean decrease gini for XY dist. and Z dist. variables related to road design, respectively. Thus, variables related to road design contributed significantly to the segmentation of semantic information. The results of this study demonstrate the applicability of segmentation of MMS point cloud data based on machine learning, and will help to reduce the cost and effort associated with HD mapping.