• The Journal of Bigdata
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• v.7 no.1
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• pp.113-124
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• 2022

Various methods using machine learning and big data have been applied to prevent failures in Data Centers. However, there are many limitations to referencing individual equipment-based performance indicators or to being practically utilized as an approach that does not consider the infrastructure operating environment. In this study, the performance indicators of individual infrastructure equipment are integrated monitoring and the performance indicators of various equipment are segmented and graded to make a single numerical value. Data pre-processing based on experience in infrastructure operation. And an ensemble of RRCF (Robust Random Cut Forest) analysis and Prophet analysis model led to reliable analysis results in detecting anomalies. A failure analysis system was implemented to facilitate the use of Data Center operators. It can provide a preemptive response to Data Center failures and an appropriate tuning time.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.667-672
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• 2009

Gathering information and systemization of infrastructure disaster management is to reduce uncertainties in making decisions and maximize the number of alternations. The key objects of a sensor-based progress report and propagation automation systems are to provide objective data, realize and support decision making and deliver them to a certain area, department, manager and other people rapidly. The major findings and results of this study are as follows. 1) Application of international standard-based alerting protocol(CAP; Common Alerting Protocol). 2) Development of database of existing progress report and propagation manual in order to achieve networking of safety management on major social infrastructure of the nation. 3) Development middleware application programs to progress report and propagation data using SMS, FAX, EMS, VMS, MMS.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.83-90
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• 2009

Natural disasters, such as the recent floods in the Midwest, Hurricane Ike in the Gulf coast region (U.S.), and the earthquake in Sichuan (China), cause severe damage to the infrastructure as well as the associated industries and communities that rely on the infrastructure. The estimated damages due to Hurricane Ike in 2008 were a staggering $27 billion, the third worst in U.S. history. In addition, the worst earthquake in three decades in Sichuan resulted in about 90,000 people dead or missing and $20 billion of the estimated loss. A common observation in the analyses of these natural disaster events is the inadequacy of critical infrastructure to withstand the forces of natural calamities and the lack of mitigation strategies when they occur on the part of emergency-related organizations, industries, and communities. If the emergency-related agencies could identify and fortify the vulnerable critical infrastructure in the preparedness stage, the damage and impacts can be significantly reduced. Therefore, it is important to develop a decision support system (DSS) for identifying region-specific mitigation strategies based on the inter-relationships between the infrastructure and associated industries and communities in the affected region. To establish effective mitigation strategies, relevant data were collected from the affected areas with respect to the technical, social, and economic impact levels. The data analysis facilitated identifying the major factors, such as vulnerability, criticality, and severity, for developing a DSS. Customized mitigation strategies that will help agencies prepare, respond, and recover according to the disaster response were suggested.

• Asia pacific journal of information systems
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• v.14 no.4
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• pp.23-47
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• 2004

This study was tried for the improvement of software process ability of the software firms, and analyzed empirically the impact that a software process infrastructure level influences on the software process level and process performance. The questionnaire were developed and data were collected from the process improvement correspondences or project quality managers of the 78 software firms. The result was shown that management-organization infrastructure was composed of software process improvement organization's role and activity, process standard and education, management system and supporting, management guides and procedures. And organization's standard development procedure or criteria, process asset, process support tools were included in technical infrastructure. This study provides that some components of software process infrastructure had an significant influence on the process level, process infrastructure management level, and software process performance.

• Smart Structures and Systems
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• v.17 no.4
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• pp.669-690
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• 2016

Advances in sensor technologies have led to the instrumentation of sensor networks for bridge monitoring and management. For a dense sensor network, enormous amount of sensor data are collected. The data need to be managed, processed, and interpreted. Data management issues are of prime importance for a bridge management system. This paper describes a data management infrastructure for bridge monitoring applications. Specifically, NoSQL database systems such as MongoDB and Apache Cassandra are employed to handle time-series data as well the unstructured bridge information model data. Standard XML-based modeling languages such as OpenBrIM and SensorML are adopted to manage semantically meaningful data and to support interoperability. Data interoperability and integration among different components of a bridge monitoring system that includes on-site computers, a central server, local computing platforms, and mobile devices are illustrated. The data management framework is demonstrated using the data collected from the wireless sensor network installed on the Telegraph Road Bridge, Monroe, MI.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.820-824
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• 2003

One of the most important research areas on remote sensing is spectral unmixing of hyper-spectral data. For spectral unmixing of hyper spectral data, accurate land cover information is necessary. But obtaining accurate land cover information is difficult process. Obtaining land cover information from high-resolution data may be a useful solution. In this study spectral signature of endmembers on ASTER acquired in October was calculated from land cover information on IKONOS acquired in September. Then the spectral signature of endmembers applied to ASTER images acquired on January and March. Then the result of spectral unmxing of them evauateted. The spectral signatures of endmembers could be applied to different seasonal images. When it applied to an ASTER image which have similar zenith angle to the image of the spectral signatures of endmembers, spectral unmixing result was reliable. Although test data has different zenith angle from the image of spectral signatures of endmembers, the spectral unmixing results of urban and vegetation were reliable.

• Journal of Korean Society of Rural Planning
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• v.20 no.4
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• pp.277-284
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• 2014

Recently the use of smart phones and mobile devices is increasing rapidly, data search and retrieval in the mobile environments are generalized. There are only few mobile applications available in the area of agriculture while huge amount of new applications are developed and uploaded. The purpose of this study was to develop the android based mobile application for providing agricultural infrastructure and disaster information. The mobile application was designed through the database establishment and management system, server management system, and mobile application development. The database is composed of weather data, agricultural infrastructure data, and agricultural disaster data. By developing the mobile application which provides agricultural infrastructure information, it is expected to improve the accessibility to agricultural information and mitigate the agricultural disaster damages.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.655-665
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• 2018

In efforts to increase its agricultural productivity, the Indonesian Center for Agricultural Biotechnology and Genetic Resources Research and Development has conducted a variety of genomic studies using high-throughput DNA genotyping and sequencing. The large quantity of data (big data) produced by these biotechnologies require high performance data management system to store, backup, and secure data. Additionally, these genetic studies are computationally demanding, requiring high performance processors and memory for data processing and analysis. Reliable network connectivity with large bandwidth to transfer data is essential as well as database applications and statistical tools that include cleaning, quality control, querying based on specific criteria, and exporting to various formats that are important for generating high yield varieties of crops and improving future agricultural strategies. This manuscript presents a reliable, secure, and scalable information technology infrastructure tailored to Indonesian agriculture genotyping studies.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.134-137
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• 2021

This study aims to develop a digital twin and big data-based port infrastructure control system that reflects smart maintenance technology. It is a technology that can evaluate aging and disaster risk by converting heterogeneous data such as sensing data and image data acquired from port infrastructure into big data, visualized in a digital twin-based control system, and comprehensively analyzed. The meaning of big data to express the physical world and processes by combining data, which are the core components of the virtual world, and the matters to be reflected in each stage of securing, processing, storing, analyzing and utilizing necessary big data, and we would like to define methods for linking with IT resources.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.37-45
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• 2024

This study aims to collect and systematize indicators for each stage of digital transformation at the infrastructure level to accurately diagnose the current status of digital transformation in Korea and to serve as a reference for establishing a balanced digital strategy. In order to establish a framework for digital transformation of infrastructure, 19 indicators in three categories(tangible/intangible, data) were identified across three stages of digital transformation: computerization, digitization, and digital transformation, and 19 indicators in three categories were identified to study the changes in digital infrastructure. The main findings are: First, the digital transformation of infrastructure is at a high level, moving from digitization to digital transformation. Second, the scope of digital transformation policies is expanding as digital transformation is triggered, and additional policies on inclusion and social disparities should be prepared. It is also important to improve the regulatory environment, which is relatively undervalued. Third, as data becomes more important, it is important to develop indicators and measurements to strengthen digital competitiveness in terms of data infrastructure. This study is an exploratory study of the existing indicators, which can be used to conduct specialized research on the differences in the level of digital transformation by industry, sector, company size, age, gender, region, and group, and to study indicators for the expansion of digital transformation to social and industrial sectors. The expected effect is to deepen the process of understanding the interaction between each indicator, so that future digital transformation policies can be organized and promoted, and policy outcomes can be predicted and responded to in advance.