• Journal of the Korean earth science society
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• v.21 no.5
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• pp.503-524
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• 2000

Intercomparisons between four kinds of data have been done to estimate the accuracy of satellite observations and model reanalysis for middle and lower tropospheric thermal state over regional oceans. The data include the Microwave Sounding Units (MSU) Channel 2 (Ch2) brightness temperatures of NOAA satellites and the vertically weighted corresponding temperature of ECMWF GCM (1980-93). The satellite data for midtropospheric temperatures are MSU2 (1980-98) in nadir direction and SC2 (1980-97) in multiple scans, and for lower tropospheric temperature SC2R (1980-97). MSU2 was derived in this study while SC2 and SC2R were described in Spencer and Christy (1992a, 1992b). Temporal correlations between the above data were high (r${\ge}$0.90) in the middle and high latitudes, but low(r${\sim}$0.65) over the low latitude and more convective regions. Their values with SC2R which included the noises due to hydrometeors and surface emission were conspicuously low. The reanalysis shows higher correlation with SC2 than with MSU2 partially because of the hydrometeors screening. SC2R in monthly climatological anomalies was more sensitive to surface thermal condition in northern hemisphere than MSU2 or SC2. The first EOF mode for the monthly mean data of MSU and ECMWF shows annual cycle over most regions except the tropics. The mode in MSU2 over the Pacific suggests the east-west dipole due to the Walker circulation, but this tendency is not clear in other data. In the first and second modes for the Ch2 anomalies over most regions, the MSU and ECMWF data commonly indicate interannual variability due to El Ni${\tilde{n}$o and La Ni${\tilde{n}$a. The substantial disagreement between observations and model reanalysis occurs over the equatorial upwelling region of the western Pacific, suggesting uncertainties in the model parameterization of atmosphere-ocean interaction.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.47-56
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• 2014

Recently, the velocity of sea-level rising has increased due to the global warming and the natural disasters have been occurred many times. Therefore, there are various demands for the integration of vertical reference datums for the ocean and land areas in order to develop a coastal area and prevent a natural disaster. Currently, the vertical datum for the ocean area refers to Local Mean Sea Level(LMSL) and the vertical datum for the land area is based on Incheon Mean Sea Level(IMSL). This study uses 31 points of Tidal Gauge Bench Mark (TGBM) in order to compares and analyzes the geometric heights referring LMSL, IMSL, and the nationally determined geoid surface. 11 points of comparable data are biased more than 10 cm when the geometric heights are compared. It seems to be caused by the inflow of river, the relocation of Tidal Gauge Station, and the topographic change by harbor construction. Also, this study analyze the inclination of sea surface which is the difference between IMSL and LMSL, and it shows the inclination of sea surface increases from the western to southern, and eastern seas. In this study, it is shown that TGBM can be used to integrate vertical datums for the ocean and land areas. In order to integrate the vertical datums, there need more surveying data connecting the ocean to the land area, also cooperation between Korea Hydrographic and Oceanographic Administration and National Geographic Information Institute. It is expected that the integrated vertical datum can be applied to the development of coastal area and the preventative of natural disaster.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.57-66
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• 2015

Considerable research efforts are being directed towards analyzing unstructured data such as text files and log files using commercial and noncommercial analytical tools. In particular, researchers are trying to extract meaningful knowledge through text mining in not only business but also many other areas such as politics, economics, and cultural studies. For instance, several studies have examined national pending issues by analyzing large volumes of text on various social issues. However, it is difficult to provide successful information services that can identify R&D documents on specific national pending issues. While users may specify certain keywords relating to national pending issues, they usually fail to retrieve appropriate R&D information primarily due to discrepancies between these terms and the corresponding terms actually used in the R&D documents. Thus, we need an intermediate logic to overcome these discrepancies, also to identify and package appropriate R&D information on specific national pending issues. To address this requirement, three methodologies are proposed in this study-a hybrid methodology for extracting and integrating keywords pertaining to national pending issues, a methodology for packaging R&D information that corresponds to national pending issues, and a methodology for constructing an associative issue network based on relevant R&D information. Data analysis techniques such as text mining, social network analysis, and association rules mining are utilized for establishing these methodologies. As the experiment result, the keyword enhancement rate by the proposed integration methodology reveals to be about 42.8%. For the second objective, three key analyses were conducted and a number of association rules between national pending issue keywords and R&D keywords were derived. The experiment regarding to the third objective, which is issue clustering based on R&D keywords is still in progress and expected to give tangible results in the future.

• The Journal of the Korea Contents Association
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• v.12 no.8
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• pp.411-419
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• 2012

In case that R&D results from R&D projects are well managed and archived, the research institutes can transfer the valuable technologies related to R&D results with some costs to corporations. However, it is still difficult to maintain and reuse R&D results because they are managed by each person or each department and not integrated between R&D results. Therefore, the government should undertake to manage R&D results overall by collecting meta data and distribute analyzed information from meta data. Each researching institute also makes an efforts to manage R&D results focusing on their reusing. For this purpose, in this paper, we present a process to manage R&D results; insert meta data of R&D results to the system, upload files of R&D results to the database of the system, inquire, and use meta data of R&D results. Based on the process, we design a system architecture for managing R&D results. In addition, it should be mainly considered to design a global schema for integrating R&D results into one database. The system shows detailed information on R&D results and provides R&D results conveniently to users. We expect that we may reduce the cost of reusing R&D results and improve the quality of R&D results with designing efficiently a process and a global schema of R&D result management system.

• Management & Information Systems Review
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• v.29 no.4
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• pp.169-186
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• 2010

This study aims to reveal the ways to sharpen the edges of Korean companies through the relativity analysis between knowledge management and innovational cluster in environmental changes in resent Busan. That is, according to the knowledge management approach, the methods and directions of strengthening industrial competition were established, while the strategy of innovational clusters was suggested as a way of expanding and encouraging knowledge management. The key words of innovational cluster are in this research are the framework of Cluster theory, the importance of innovational cluster, and the change of managerial strategy paradigm. This study provide the several implication for the practice of knowledge management and the researchers. Based on these theories of knowledge management and industrial clusters, their close relationships were analyzed. As a result, industrial clusters were found to be effectively utilized to enlarge and deepen knowledge management. In addition, this suggests the efficient operation guideline of knowledge management. this study indicates both knowledge and innovational cluster should be operated and handled together in the managerial strategy. but this research has limitations in generaling the study result because it collects data from local firms only in Busan.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.2
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• pp.149-160
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• 2015

This study aims to estimate the forest volumes of the upper region of Nam-Han River in ecosystem zoning by forest types and age classes, and to suggest the optimal estimation method through the comparison of the standard errors according to the spatial unit. In the estimation of forest volumes, we used both of direct estimation, which uses sample plots of the target area only, and synthetic estimation, which includes sample plots of the expanded areas as well as those of the target area. As for the spatial expansion, we applied four standards for synthetic estimator: Mountainous zone, Neighbor ecosystem region, Gangwon province, and Buffer zone. The results show that average forest volume per ha, calculated by direct estimation, was $143.5m^3/ha$, while that by synthetic estimation with each standard, was estimated at $146.9m^3/ha$ by Gangwon province, $144.8m^3/ha$ by Buffer zone, $139.8m^3/ha$ by Neighbor ecosystem region, and $138.6m^3/ha$ by Mountainous zone, respectively. The standard errors of direct estimation was $1.79m^3/ha$, while those of synthetic estimation showed not a great difference among the errors. Meanwhile, considering the standard errors by forest type, the lowest was ${\pm}2.3m^3/ha$ of broad-leaved forest, followed by ${\pm}3.3m^3/ha$ of mixed forest, and ${\pm}4.8m^3/ha$ of coniferous forest.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.324-334
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• 2019

This study investigates the characteristics of patients hospitalized at a convalescent hospital, by considering patient groups based on their medical record information. It further analyzes and correlates the factors, thus providing basic data required to improve the patient classification system at convalescent hospitals. The data includes total information of 213 medical records of patients discharged from a convalescent hospital in the Jeonbuk region during a period of one year, from January to December 2016. The study examines and correlates the days of hospitalization and the number of diseases, revealing a positive correlation having a correlation coefficient, thereby indicating that infliction with a greater number of diseases results in longer hospitalization. Based on these findings, the study raises the need to revise and supplement items on the patient assessment report to help determine the patient groups and identify medical efforts to be actually provided to patients. In addition, a proper care service system for each patient group based on their respective problems that are intensively managed according to the patient groups will be an essential element in the efficient management of convalescent hospitals. Furthermore, an important task addressed will be in managing the health of the elderly population at the national level.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.11a
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• pp.154-155
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• 2011

2010년 천리안위성의 성공적인 발사에 따라 인공위성의 활용에 대한 기대가 커지고 있다. 천리안 해양관측위성(GOCI)이외에 아리랑 2호가 현재 운용중인 우리나라 위성들이다. 가까운 시기에 아리랑 5호(2011년 말), 아리랑 3호(2012년), 아리랑 3A호(2013년)가 발사될 예정이다. 즉, 해양적용을 위한 위성환경은 이제부터 준비되고 있다고 볼 수 있다. 대외적으로 보면, 인공위성 자원은 아주 많다. 문제는 이와 같은 자원을 어떻게 활용할 것인가 인데 이의 활용 기술 개발적 측면에서는 많이 소홀한 것이 사실이다. 전세계적으로 이 시스템 개발을 위한 치열한 경쟁이 진행 중에 있다. 이미 소말리아 주변 감시체계는 많은 부분을 위성에 의지하고 있다. 우리나라에서 최초로 위성활용 가능성을 보여준 사건이 허베이스피리트호 원유유출 사고이다. 이 사고는 2007년 12월7일 아침 7시6분경 서해안 만리포 북서쪽 10km 해상에서 크레인을 적재한 1만1800t급 바지선이 정박 중인 홍콩 선적 유조선 허베이 스피리트호(14만6000t급)와 부딪치면서 발생했다. 이와 같은 기름 유출 사고의 경우, 유출 범위를 정확하게 이해하는 것이 중요하다. 거의 준비된 상태가 아님에도 불구하고 12월 8일 아침 최초로 유출된 기름을 모습을 보여주는 위성이미지(광학위성)가 얻어졌다. 하지만 이와 같은 자료가 관련 전문가가 이용할 수 있기까지 많은 시간이 소용되었고, 이 정보를 전달할 수 있는 방법도 없었다. 사실 단순한 이미지가 아니라 지리정보체계를 가진 오염정보를 제공할 방법도 준비도 되어 있지 못한 상황이었다. 본 발표를 통하여, 허베이스피리트호 사고뿐만 아니라, 2011년 6월부터 수개월간 지속된 발해만 오염사고 적용 등 다양한 사례 소개를 하고, 이를 기반으로 해양경찰청에서 업무활용을 위한 방안을 제시한다. 먼저, 해경청의 주요 임무인, 경비, 수색구조, 오염대응 분야별로 현황 분석을 수행하였다. 또한 국외사례에 대한 조사를 한 후, 최종 인공위성 원격탐사기술의 해경청 도입방안에 대한 설계를 실시하였다. 국제적으로 인공위성을 이용한 해양 경비, 수색구조, 오염 모니터링기술 개발이 이루어지고 있으며, 유럽 국가는 시범도입을 진행 중에 있다. 유럽해사안전국(EMSA)은 해양경비 및 수색구조를 위한 선박통항 및 보고 서비스와 오염대비대응(Pollution Preparedness and Response, PPR) 위성 서비스를 회원국에 제공하고 있다. 해양경찰청 임무 수행뿐만 아니라, 해양영토 관리적 측면에서 첨단 위성장비 활용, 선진국형 해상경비 패러다임의 전환 필요성이 크다고 할 수 있다.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.3
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• pp.34-42
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• 2008

The control points are important assets of countries which express the most accurate location information that is used in surveying land and other measurements. The location information has played an important role in our daily lives with the development of ubiquitous technology. While many researchers have recently applied new technology like RFID(Radio-Frequency Identification) to the effective management of control points, the research into data retrieval and the interoperability of control point data is still primitive step. Therefore, we construct a data modeling to effectively manage control points using ontology data structure and focus on semantic retrieval method. Our retrieval system can provide the inferred and associated information among data using Prot$\acute{e}$g$\acute{e}$-OWL tool. Our system has advantages in reducing the number of repeated queries by hierarchy searching and improving the searching time by association searching. Also, we propose an effective method to construct retrieval systems being able to edit items of categories and properties without editing the related codes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.294-297
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• 2015

Recently, big data analysis which is based on machine learning has been gained a lot of attentions in various fields. Especially, agriculture is considered as one promising field that machine learning algorithm can be efficiently utilized and accordingly, lots of works have been done so far. However, most of the researches are focusing on the forecast of weather or analysis of genome, and machine learning algorithm for livestock management, especially which uses individual data of livestocks, e.g., temperature and movement, are not properly investigated yet. In this work, we propose fast abnormal livestock detection algorithm based on machine learning, more specifically expectation maximization, such that livestock which has problem can be efficiently and promptly found. In our proposed scheme, livestocks are divided into two clusters using expectation maximization based on their bionic data and the abnormal livestock can be detected by comparing the size of two clusters. Especially, we divide the case in which single livestock has problem and the case in which livestocks have epidemic such that fast response is enabled when epidemic case. Moreover, our algorithm does not need statistical information.