• Proceedings of the Korean Society of Computer Information Conference
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• 2013.07a
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• pp.395-397
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• 2013

MODIS는 기상, 대기, 해양, 그리고 육상 등의 지구전체에 대한 정보를 산출하기 위한 센서로서, 인공위성에 탑재되어 지구관측 데이터를 생산한다. 최초의 MODIS 위성 데이터는 많은 왜곡을 포함하고 있으므로 지형 및 광휘 보정작업은 분석 작업을 하기 위한 필수적인 전처리 작업이다. 위성 데이터 처리를 위해 개발된 SeaDAS는 단일노드/단일코어상에서 수행되기 적합하게 개발되었기 때문에, 대용량의 위성데이터를 전처리하기 위해 많은 시간을 소비해야 한다. 본 논문은 Sun Grid Engine 기반의 다중노드/다중코어를 이용하는 위성 데이터 분산 처리 방법을 제안하고 성능 및 확장성에 대한 평가를 수행한다.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.215-228
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• 2019

HDF5 data format is used to effectively store and distribute large volume of Geostationary Ocean Color Imager(GOCI) satellite data. The Korea Ocean Satellite Center has developed and provided a GOCI Data Processing System(GDPS) for general users who are not familiar with HDF5 format. Nevertheless, it is not easy to merge and process Hierarchical Data Format version5(HDF5) data that requires an understanding of satellite data characteristics, needs to learn how to use GDPS, and stores location and attribute information separately. Therefore, the open source R and rhdf5, data.table, and matrixStats packages were used to develop algorithm that could easily utilize satellite data in HDF5 format without the need for the process of using GDPS.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2012.06a
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• pp.85-86
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• 2012

e-Navigation strategy IMO promotes is defined as it is necessary to network to provide various maritime safety information to in land and on board users, and it is expected to provide a large amount and diverse kinds of maritime spatial information services to them frequently. However, as there are some limits to transmit that by current mobile maritime communication technologies, it is required to simplify and optimize the information. In this study, tree node and convex hull method is applied to S-100 SD spatial information to generalize and we arranged the efficiency and effect of generalization by storing in XML form which can be used in general.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.1
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• pp.112-125
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• 2021

Marine spatial planning(MSP) designates the marine as nine kinds of use zones for the systematic and rational management of marine spaces. One of them is the fishery protection zone, which is necessary for the sustainable production of fishery products, including the protection and fosterage of fishing activities. This study intends to quantitatively identify the fishing activity space, one of the elements necessary for the designation of fisheries protection zones, by mapping of fishery activities using V-Pass data and deriving the fishery activity concentrated zone. To this end, pre-processing of V-Pass data was performed, such as constructing a dataset that combines static and dynamic information, calculating the speed of fishing vessels, extracting fishing activity points, and removing data in non-fishing activity zone. Finally, using the selected V-Pass point data, a fishery activity map was made by kernel density estimation, and the concentrated space of fishery activity was analyzed. In addition, it was confirmed that there is a difference in the spatial distribution of fishing activities according to the type of fishing vessel and the season. The pre-processing technique of large volume V-Pass data and the mapping method of fishing activities performed through this study are expected to contribute to the study of spatial characteristics evaluation of fishing activities in the future.

• Journal of KIISE
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• v.42 no.7
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• pp.840-845
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• 2015

In recent years, Big Data has been a topic of great interest for the production and operation work of offshore plants as well as for enterprise resource planning. The ability to predict future equipment performance based on historical results can be useful to shuttling assets to more productive areas. Specifically, a centrifugal compressor is one of the major piece of equipment in offshore plants. This machinery is very dangerous because it can explode due to failure, so it is necessary to monitor its performance in real time. In this paper, we present stream data processing architecture that can be used to compute the performance of the centrifugal compressor. Our system consists of two major components: a virtual tag stream generator and a real-time data stream manager. In order to provide scalability for our system, we exploit a parallel programming approach to use multi-core CPUs to process the massive amount of stream data. In addition, we provide experimental evidence that demonstrates improvements in the stream data processing for the centrifugal compressor.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.4
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• pp.129-141
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• 2006

The image data amount is increasing rapidly that used geography, sea information etc. with great development of a remote sensing technology using artificial satellite. Therefore, people need automatic method that use image processing description than macrography for analysis remote sensing image. In this paper, we propose that acquire texture information to use GLCM(Gray Level Co-occurrence Matrix) in red tide area of artificial satellite remote sensing image, and detects red tide area by PCA(principal component analysis) automatically from this data. Method by sea color that one feature of remote sensing image of existent red tide area detection was most. but in this paper, we changed into 2 principal component accumulation images using GLCM's texture feature information 8. Experiment result, 2 principal component accumulation image's variance percentage is 90.4%. We compared with red tide area that use only sea color and It is better result.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1120-1128
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• 2022

In the event of a marine accident, the longer the exposure time to the sea increases, the faster the chance of survival decreases. However, because the search area of the sea is extremely wide compared to that of land, marine object detection technology based on the sensor mounted on a satellite or an aircraft must be applied rather than ship for an efficient search. The purpose of this study was to rapidly detect an object in the ocean using a hyperspectral image sensor mounted on an aircraft. The image captured by this sensor has a spatial resolution of 8,241 × 1,024, and is a large-capacity data comprising 127 spectra and a resolution of 0.7 m per pixel. In this study, a marine object detection model was developed that combines a seawater identification algorithm using DBSCAN and a density-based land removal algorithm to rapidly analyze large data. When the developed detection model was applied to the hyperspectral image, the performance of analyzing a sea area of about 5 km² within 100 s was confirmed. In addition, to evaluate the detection accuracy of the developed model, hyperspectral images of the Mokpo, Gunsan, and Yeosu regions were taken using an aircraft. As a result, ships in the experimental image could be detected with an accuracy of 90 %. The technology developed in this study is expected to be utilized as important information to support the search and rescue activities of small ships and human life.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.892-903
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• 2020

The rapid and accurate prediction of storm-surge height during typhoon attacks is essential in responding to coastal disasters. Most methods used for predicting typhoon data are based on numerical modeling, but numerical modeling takes significant computing resources and time. Recently, various studies on the expeditious production of predictive data based on artificial intelligence have been conducted, and in this study, artificial intelligence-based storm-surge height prediction was performed. Several learning data were needed for artificial intelligence training. Because the number of previous typhoons was limited, many synthesized typhoons were created using the tropical cyclone risk model, and the storm-surge height was also generated using the storm surge model. The comparison of the storm-surge height predicted using artificial intelligence with the actual typhoon, showed that the root-mean-square error was 0.09 ~ 0.30 m, the correlation coefficient was 0.65 ~ 0.94, and the absolute relative error of the maximum height was 1.0 ~ 52.5%. Although errors appeared to be somewhat large at certain typhoons and points, future studies are expected to improve accuracy through learning-data optimization.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.3
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• pp.246-252
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• 2018

Recent studies have been received much attention on underwater laser communication, which is capable of high data rate. However, in underwater laser communication, distortions caused by absorption and scattering induced performance degradation. A typical way to improve performance is to apply channel coding technique. In the beginning of studies, simple methods such as RS and BCH coding techniques were applied. However, due to distance expansion and performance improvement, channel coding methods with low error probability such as LDPC coded method were applied. In this paper, we analyzed the performance according to the size of the code word N, the distance between the transceivers and the size of the M of the M-ary PPM modulation scheme. Simulation results show that parameter M of M-ary PPM is most effect on performance.

• KIISE Transactions on Computing Practices
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• v.21 no.9
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• pp.587-595
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• 2015

Several fields of science have traditionally demanded large-scale workflow support, which requires thousands of central processing unit (CPU) cores. In order to support such large-scale scientific workflows, large-capacity cluster systems such as supercomputers are widely used. However, as users require a diversity of software packages and configurations, a system administrator has some trouble in making a service environment in real time. In this paper, we present a container-based cluster management platform and introduce an implementation case to minimize performance reduction and dynamically provide a distributed computing environment desired by users. This paper offers the following contributions. First, a container-based virtualization technology is assimilated with a resource and job management system to expand applicability to support large-scale scientific workflows. Second, an implementation case in which docker and HTCondor are interlocked is introduced. Lastly, docker and native performance comparison results using two widely known benchmark tools and Monte-Carlo simulation implemented using various programming languages are presented.