• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.488-490
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• 2003

Over the past few years, a number of studies have been made on web map services, which enable the GIS user to access spatial data through the web. They, however, have focused on only implementation techniques separately, such as map client implementation, map server implementation, communication between client and server, and the map data representation. Therefore, it is hard to share and practical use the spatial data, because they does not ensure interoperability in heterogeneous map servers. In this paper, we have designed and implemented the web map server with open architecture, which complies with the standard interfaces proposed by OpenGIS Consortium (OGC). In particular, we have extended the OGC’s interfaces for a map server to support one or more data sources. This paper has contributed to construction and practical use of web map services by newly proposing the method of implementation of a map server, which could be reused regardless of the types of data sources.

• Annual Conference of KIPS
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• 2020.05a
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• pp.231-234
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• 2020

In the past few years, the industrial internet of things (IIoT) has received great attention in various industrial sectors which have potentially increased a high level of integrity, availability, and scalability. The increasing of IIoT is expected to create new smart industrial enterprises and build the next generation smart system. However existing IIoT systems rely on centralized servers that are vulnerable to a single point of failure and malicious attack, which exposes the data to security risks and storage. To address the above issues, blockchain is widely considered as a promising solution, which can build a secure and efficient environment for data storing, processing and sharing in IIoT. In this paper, we propose a decentralized, peer-to-peer platform for secure data storing in industrial IoT base on the ethereum blockchain. We exploit ethereum to ensure data security and reliability when smart devices store the data.

• Journal of Soil and Groundwater Environment
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• v.21 no.4
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• pp.30-41
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• 2016

The amount of groundwater related data is drastically increasing domestically from various sources since 2000. To justify the more expansive continuation of the data acquisition and to derive valuable implications from the data, continued employments of sophisticated and state-of-the-arts statistical tools in the analyses and predictions are important issue. In the present study, we employed a well established machine learning technique of Gaussian Process Regression (GPR) model in the trend analyses of groundwater level for the long-term change. The major benefit of GPR model is that the model provide not only the future predictions but also the associated uncertainty. In the study, the long-term predictions of groundwater level from the stations of National Groundwater Monitoring Network located within Han River Basin were exemplified as prediction cases based on the GPR model. In addition, a few types of groundwater change patterns were delineated (i.e., increasing, decreasing, and no trend) on the basis of the statistics acquired from GPR analyses. From the study, it was found that the majority of the monitoring stations has decreasing trend while small portion shows increasing or no trend. To further analyze the causes of the trend, the corresponding precipitation data were jointly analyzed by the same method (i.e., GPR). Based on the analyses, the major cause of decreasing trend of groundwater level is attributed to reduction of precipitation rate whereas a few of the stations show weak relationship between the pattern of groundwater level changes and precipitation.

• The Journal of the Korea Contents Association
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• v.12 no.1
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• pp.17-29
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• 2012

In recent years, Linked Data that is published under an open license shows increased growth rate and comes into the spotlight due to its interoperability and openness especially in government of developed countries. However there are relatively few out-links compared with its entire number of links and most of links refer a few hub dataset. These occur because of absence of technology that identifies entities in Linked data. In this paper, we present an improved author entity resolution method that using representative properties. To solve problems of previous methods that utilizes relation with other entities(owl:sameAs, owl:differentFrom and so on) or depends on Curation, we design and evaluate an automated realtime resolution process based on multi-ontologies that respects entity's type and its logical characteristics so as to verify entities consistency. The evaluation of author entity resolution shows positive results (The average of K measuring result is 0.8533.) with 29 author information that has obtained confirmation.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1286-1295
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• 2020

It has been increasingly difficult to predict the amounts of Acer mono sap to be collected due to droughts and cold waves caused by recent climate changes with few studies conducted on the prediction of its collection volume. This study thus set out to propose a Big Data prediction system based on meteorological information for the collection of Acer mono sap. The proposed system would analyze collected data and provide managers with a statistical chart of prediction values regarding climate factors to affect the amounts of Acer mono sap to be collected, thus enabling efficient work. It was designed based on Hadoop for data collection, treatment and analysis. The study also analyzed and proposed an optimal prediction model for climate conditions to influence the volume of Acer mono sap to be collected by applying a multiple regression analysis model based on Hadoop and Mahout.

• Publications of The Korean Astronomical Society
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• v.32 no.1
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• pp.41-43
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• 2017

AKARI performed about 10,000 spectroscopic observations with the Infrared Camera (IRC) during its mission phase. These IRC observations provide unique spectroscopic data at near- and mid-infrared wavelengths for studies of the next few decades because of its high sensitivity and unique wavelength coverage. In this paper, we present the current status of the activity for improving the IRC spectroscopic data reduction process, including the toolkit and related data packages, and also discuss the goal of this project.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.612-615
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• 2005

Korea Aerospace Research Institute(KARl) has been receiving Terra and Aqua MODIS data at ground station of Daejeon since July 2002. MODIS data can cover whole East Asia including the Korean Peninsula, Japan and The East China each almost scene and is monitoring ocean, atmosphere and land. By this time, over two thousand scenes have been archived including Terra and Aqua in the storage system and they occupied about over 10TB of disk space. In this study, Web-Downloading system of MODIS data developed on WWW is including following main functions: spectral subset (250m, 500m, 1000m chnnels) Level 1B data of HDF format, result display, ftp download and statistic viewer etc. Users using this system can directly download MODIS data on WWW with a few input parameters. This system is available via the Internet URL after October 2005 on the following, 'http://webmodis.kari.re.kr/'

• Advances in environmental research
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• v.3 no.1
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• pp.87-106
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• 2014

The concern for the global environment ensues researchers from various disciplines to work in collaboration to tackle with the issues of sustainability and environmental conservation for well-being of the people. In this study, we have selected and focused on few basic environment-effecting factors such as temperature, humidity, carbon dioxide and oxygen concentration level and referred them as meteorological data. In this paper, we present the development of our own customized hardware setup, environmental monitoring device (EMD) to obtain the data. Utilizing the relationship among these basic parameters, represented in the form of formulas and equations, we tried to encode them using Matlab programming. Data visualization is achieved by plotting the graphs of basic parameters obtained from EMD as well for the derivatives using Matlab programs.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.677-682
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• 1997

Vibration diagnosis of steam turbine generator is essential for safe operation. For a fast few decades, several data base systems for diagnosis of steam turbine generators have been developed and proved useful. However, there still remains a problem in using data base systems such that they require an expert engineer who has a deep insight or knowledg into the system. Moreover,such data base systems can not give any information if the input is not completely fit with data base. This paper presents an effective method for vibration diagnosis of steam turbine generators using fuzzy inference. The proposed method includes also a strategy to overcome the drawback of data base system such that one cannot obtain any information when the input is insufficient or not exact. A computer program is written to realize the entire procedure for the diagnosis. Three realistic problems are dealt with to show the effectiveness of the proposed method.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.2
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• pp.253-259
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• 2006

There is few study which automatically diagnoses the fault from ship's monitored data. The bigger control and monitoring system is. the more important fault diagnosis and maintenance is to reduce damage caused by system fault. This paper proposes fault diagnosis system using a correlation analysis algorithm which is able to diagnose and forecast the fault from monitored data and is composed of fault detection knowledge base and fault diagnosis knowledge base. For all kinds of ship's engine room monitored data are classified with combustion subsystem, heat exchange subsystem and electric motor and pump subsystem, To verify capability of fault detection, diagnosis and prediction, FMS(Fault Management System) is developed by C++. Simulation by FMS is carried out with population data set made by the log book data of 2 months duration from a large full container ship of H shipping company.