• Journal of Teachers' Pension
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• v.2
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• pp.181-206
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• 2017

출산율 하락과 기대여명 증가에 따라 인구구조의 고령화가 급격히 진행되고 있다. 이에 따라 소득보장이나 건강보장과 같은 사회보장제도의 장기 재정 불안정과 관련된 사회적 우려가 높다. 여러 세대를 거쳐 사회보장제도를 안정적으로 유지하기 위해서는 제도의 장기적 재정 상태에 대한 정확한 전망이 요청된다. 재정 상태에 대한 정확한 진단은 장기 재정 안정화를 위한 가장 기본적인 전제 조건이며, 정확한 재정 상태에 대한 평가 없이 재정 안정화를 위한 사회적 합의를 도출하는 것은 가능하지 않다. 본 연구는 사학연금의 장기 재정 전망에 필요한 사망률 전망 방법을 검토함으로써 사학연금 장기 재정 전망 작업의 정확성과 신뢰성을 높이고자 하는 목적을 가지고 있다. 보다 구체적으로, 본 연구는 연앙인구 및 사망 건수 자료가 제한적인 동시에 단기 시계열 자료만이 존재하는 사학연금 데이터베이스의 특성을 반영한 사망률 전망 모형을 구축하고 있다. 사학연금 남성 사망률 전망과 관련하여 본 연구에서 제안하는 모형은 목표 집단의 사망력 패턴과 밀접히 연관된 준거 집단을 통합적으로 모형화하는 정합적 사망률 모형(coherent mortality model)이다. 반면 관측된 사망 건수가 매우 제한적인 관계로 사학연금 데이터베이스에 기초하여 사망률을 전망하기 쉽지 않은 여성 사망률의 경우 통계청 장래인구추계에서 전망된 성별 사망확률 격차가 사학연금에도 적용될 수 있다는 가정하에 사학연금 여성 사망률을 전망하는 방법을 제안하고 있다.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2004.04a
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• pp.265-268
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• 2004

At these days, electronic maritime chart is in positively using various fields. But most of electronic maritime chart and topography information system is hard to using various fields despite that demand because that difficult approach from separately and specific constitution. This thesis apply to image filtering that extract requirement topography data from simplification of the whole system. And as constructing database of simple structure that getting various information from sounding depth and existing database, propose electronic maritime chart system to practicable 3D embodiment. And also propose convenience and commonplace to simple process about frequently renewal.

• Proceedings of the Korea Contents Association Conference
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• 2018.05a
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• pp.347-348
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• 2018

일본 뇌염 바이러스(Japanese encephalitis virus)는 작은빨간집모기(Culex spp.)를 매개로 사람에게 감염될 수 있으며, 인체에 치명적인 질병을 유발한다. 일본 뇌염 바이러스의 혈청형(serotype)은 1종류이지만, 유전형(genotype)은 5종류(GI, GII, GIII, GIV, GV)로 분류되고 있다. 현재 일본 뇌염 바이러스 백신은 아시아 지역에서 감염 빈도가 높은 유전형 3(GIII)에 대한 백신이며, 사백신(inactivated vaccine)과 약독화 백신(attenuated vaccine)이 주로 사용되고 있다. 본 연구에서는 기존 백신의 부작용을 줄이고 한계점을 개선하기 위하여, 생물정보학 데이터베이스를 활용한 접근법을 통해 펩타이드 백신 후보군을 선별하였다. 5가지의 유전형 중에서도 감염 빈도가 가장 높은 유전형 3(GIII) 및 최근 감염빈도가 서서히 늘어나고 있어 주의가 요구되고 있는 유전형 1(GI)을 연구 대상으로 선정하였다. 여러 종류의 생물정보학 데이터베이스를 활용하여 백신으로 활용가치가 높은 것으로 보고되고 있는 외피 단백질(envelope protein)에 대한 아미노산 상동성을 분석하고, 이를 바탕으로 공통 적용이 가능한 동시에 면역원성이 높은 펩타이드 3종을 백신 후보군으로 선별하였다. 더 나아가 이들의 3차원 구조 모델링을 통해 보다 백신으로 활용 가능성이 높은 펩타이드를 최종 도출하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.04a
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• pp.708-711
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• 2014

국민소득 증가와 고령화 사회에 진입하면서 다양한 의료소비가 의료서비스산업에 영향을 미치고 있으며, 이러한 사회 구조 및 환경변화에 따라 새로운 질병에 대한 대응 또한 절실히 요구되고 있다. 질병 예측을 위한 연구는 기후변화와 질병, 건강행태와 질병, 사회적 위치와 질병 등 질병에 영향을 주는 많은 요인들이 있다. 그러나 이러한 요인들을 통합, 분석 활용하는 데는 해결해야 할 문제들이 많이 있다. '정부3.0 공공데이터 개방 정책' 을 통해 질병에 관련된 자료가 공개 되면서 본 연구에서는 2010년부터 2012년까지의 질병에 영향을 주는 공공데이터를 연도별로 통합하여 지리정보시스템(GIS)기반 데이터베이스를 구축하고 활용 할 수 있게 하였다. 향후 기후변화에 민감한 질병을 찾기 위해 해당기관의 자료를 활용하여 월별로 데이터베이스를 구축하고, 이를 기반으로 의료서비스의 활성화 및 효율성에 기여 할 수 있다.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.501-512
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• 2006

XML is the most popular platform-independent data expression which is used to communicate between loosely coupled heterogeneous systems such as B2B Applications or Workflow systems. The powerful query language XQuery has been developed to support diverse needs for querying XML documents. XQuery is designed to configure results from diverse data sources into a uniquely structured query result. Therefore, it became the standard for the XML query language. Although the latest XQuery supports heavy search functions including iterations, the grouping mechanism for data is too primitive and makes the query expression difficult and complex. Therefore, this work is focused on supporting the groupby clause in the query expression to process XQuery grouping. We suggest it to be a more efficient way to process grouping for restructuring and aggregation functions on XML data. We propose an XQuery EBNF that includes the groupby clause and implemented an XQuery processing system with grouping functions based on the eXist Native XML Database.

• Journal of the Korean Society for information Management
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• v.24 no.2
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• pp.89-104
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• 2007

This research attempts to give a personalized recommendation framework in large-sized music contents environment. Despite of existing studios and commercial contents for recommendation systems, large online shopping malls are still looking for a recommendation system that can serve personalized recommendation and handle large data in real-time. This research utilizes data mining technologies and new pattern matching algorithm. A clustering technique is used to get dynamic user segmentations using user preference to contents categories. Then a sequential pattern mining technique is used to extract contents access patterns in the user segmentations. And the recommendation is given by our recommendation algorithm using user contents preference history and contents access patterns of the segment. In the framework, preprocessing and data transformation and transition are implemented on DBMS. The proposed system is implemented to show that the framework is feasible. In the experiment using real-world large data, personalized recommendation is given in almost real-time and shows acceptable correctness.

• Journal of IKEEE
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• v.22 no.4
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• pp.933-940
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• 2018

In this paper, we propose a multi-block deep learning structure for improving face recognition rate. The recognition structure of the proposed deep learning consists of three steps: multi-blocking of the input image, multi-block selection by facial feature numerical analysis, and perform deep learning of the selected multi-block. First, the input image is divided into 4 blocks by multi-block. Secondly, in the multi-block selection by feature analysis, the feature values of the quadruple multi-blocks are checked, and only the blocks with many features are selected. The third step is to perform deep learning with the selected multi-block, and the result is obtained as an efficient block with high feature value by performing recognition on the deep learning model in which the selected multi-block part is learned. To evaluate the performance of the proposed deep learning structure, we used CAS-PEAL face database. Experimental results show that the proposed multi-block deep learning structure shows 2.3% higher face recognition rate than the existing deep learning structure.

• Journal of Digital Convergence
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• v.15 no.10
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• pp.243-251
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• 2017

As external data have become important corporate information resources, there are growing needs to combine them with internal data. This paper proposes an ontology-based scheme to combine external data with multi-dimensional databases, which shall be called multi-dimensional event ontology. In the ontology, external data are represented as events. Event characteristics such as actors, places, times, targets are linked to dimensions of a multi-dimensional database. By mapping event characteristics to database dimensions, external event data are shared via multi-dimensional hierarchies. This paper proposes rules to synchronize information sharing in multi-dimensional event ontology such as upward event information sharing, downward event information sharing and complex event information sharing. These rules are implemented using Protege. This study has a value in suggesting Big Data information sharing processes using an event database framework.

• The Transactions of the Korea Information Processing Society
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• v.6 no.8
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• pp.2000-2007
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• 1999

Recently, DDS (Distributed Database System) has been often implemented on VAN (Value Added Network) as we know the amazing expansion of information network. DDS can yield significant cost and response time advantages over centrailzed systems for geographically distributed organizations. However, inappropriate design can result in high cost and poor response time. In a DDS design, the main problem is 1) how to select proper computer, and 2) how to allocate data fragment into proper nodes. This paper addresses DDS design problem of selecting the proper class of computers and the allocating data files on VAN. Also, the formulated model includes tow objectives, the operating and investment cost. GA (Genetic Algorithm) is developed to solve this mathematical formulation. A numerical experiment shows that the proposed method arrives at a good solution.

• The KIPS Transactions:PartD
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• v.9D no.6
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• pp.1091-1096
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• 2002

This paper presents the development of a protein interaction database. The developed system is characterized as follows. First, the proposed system not only maintains interaction data collected by an experiment, but also the genomic information of the protein data. Secondly, the system can extract details on interacting proteins through the developed wrappers. Thirdly, the system is based on wrapper-based system in order to extract the biologically meaningful data from various web sources and integrate them into a relational database. The system inherits a layered-modular architecture by introducing a wrapper-mediator approach in order to solve the syntactic and semantic heterogeneity among multiple data sources. Currently the system has wrapped the relevant data for about 40% of about 11,500 proteins on average from various accessible sources. A wrapper-mediator approach makes a protein interaction data comprehensive and useful with support of data interoperability and integration. The developing database will be useful for mining further knowledge and analysis of human life in proteomics studies.