• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.189-198
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• 2024

AI is accepted not only in the private sector but also in the defense sector as a cutting-edge technology that must be introduced for the development of national defense. In particular, artificial intelligence has been selected as a key task in defense science and technology innovation, and the importance of data is increasing. As the national defense department shifts from a closed data policy to data sharing and activation, efforts are being made to secure high-quality data necessary for the development of national defense. In particular, we are promoting a review of the business budget system to secure data so that related procedures can be improved to reflect the unique characteristics of AI and big data, and research and development can begin with sufficient large quantities and high-quality data. However, there is a need to establish standardization and quality standards for structured data and unstructured data at the national defense level, but the defense department is still proposing standardization and quality standards for structured data, so this needs to be supplemented. In this paper, we propose an unstructured data set standard format for defense unstructured data sets, which are most needed in defense artificial intelligence, and based on this, we propose a standardization method for defense unstructured data sets.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.9 no.1
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• pp.221-240
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• 1998

This study concerns information quality of the database which has been produced in the ASKERIC database. The measures used in this study were accuracy of the records. consistency, completeness and currency. Accuracy was measurred in terms of the keywords used in different ways in the US and Britain and the spelling errors in the records. Consistency was measured in terms of 'see also' and 'see reference' mechanism and character capitalization. Completeness was measured as follows： completeness of the search fields in the record and relevance of search fields. Currency was measured using the publication date. The experimental result showed that ERIC databases had some errors in terms of accuracy. consistency. completeness and currency. However, continuous striving for the automatic error checking functions and the policy of feedback from users have contributed to the improvement of the quality in ERIC databases.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.3
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• pp.109-134
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• 2023

The purpose of this study is to identify the current status of institutional research data repositories in the field of science and technology and to suggest ways to activate them. The study conducted literature research, case analysis, and interviews with repository managers both domestically and internationally. The study suggested strategies with a focus on establishing repository regulations and policies, improving awareness of research data sharing, and enhancing research data quality management. First, in terms of repository regulations and policy establishment, it was considered necessary to promote the status of the National R&D Information Processing Standards, a regulation related to research data, and clarify repository basis regulations. Second, to enhance awareness of research data sharing, the need for comprehensive research data education and the identification of exemplary cases were suggested. Third, in terms of strengthening research data quality management, the need for preparation for interaction between researchers-persons in charge-committees, standardization work, and long-term preservation was suggested.

• Journal of Digital Contents Society
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• v.12 no.3
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• pp.339-345
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• 2011

The k-means has been one of the popular, simple and faster clustering algorithms, but the right value of k is unknown. The value of k (the number of clusters) is a very important element because the result of clustering is different depending on it. In this paper, we present a novel algorithm based on an external cluster relationship criterion which is an evaluation metric of clustering result to determine the number of clusters dynamically. Experimental results show that our algorithm is superior to other methods in terms of the accuracy of the number of clusters.

• Annual Conference of KIPS
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• 2008.05a
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• pp.261-264
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• 2008

객체 중심의 개발방법은 오브젝트(Object)를 중심으로 프로세스를 표현하고 있으며, 이에 반해 관계형 DBMS 로 표현 되는 데이터 영역에서는 테이블(Table)을 기반으로 데이터를 집합체로 제시하고 있다. 이러한 차이로 인해 상호 영역이 맺어지는 영구 저장 영역(Persistence Layer)의 구현과 유지보수에 어려움을 겪고 있다. 최근 대두된 Object-Relation Mapping(ORM) 기술은 이러한 영구 저장 영역의 구현을 상대적으로 쉽고 빠르게 해 준다고 하나 이 또한 기술 적용의 어려움과 테이블의 조합으로 만들어지는 조회와 같은 주요 아키텍처 품질 요소를 만족시키기가 어렵다. 이에 본 논문에서는 영구 저장 영역을 ORM 으로 설계하는 데 있어 고려해야 할 요소들을 정리하여 시스템 별로 적절한 아키텍처 선정에 참조할 수 있는 기준을 제시하였다. 또한 이동통신사의 실제 비즈니스 사례를 들어 ORM 이 적용되지 않은 시스템을 대상으로 제안한 기준에 의해 해당 아키텍처를 선정하고 구현하였으며, 적용 전과 적용 후를 비교하여 주요 요구사항을 만족시키는지 평가하였다.

• The Journal of the Korea Contents Association
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• v.17 no.12
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• pp.1-10
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• 2017

Open data has received a lot of attention from around the world. The Korean government is also making efforts to open government data. However, despite the quantitative increase in public data, the lack of data is still pointed out. This paper proposes a method to improve data sharing and utilization by semantically linking public data. First, we propose a knowledge model for expressing administrative districts and their semantic relationships in Korea. An administrative district is an administrative unit that divides the territory of a nation, which is a unit of politics, according to the purpose of the state administration. The knowledge model of the administrative district defines the structure of the administrative district system and the relationship between administrative units based on the Local Autonomy Act. Second, a knowledge graph of the administrative districts is introduced. As a reference information to link public open data at a semantic level, some characteristics of a knowledge graph of administrative districts and methods for linking heterogeneous public open data and improving data quality are addressed. Finally, some use cases are addressed for interlinking between the knowledge graph of the administrative districts and public open data. In particular, national administrative organisations are interlinked with the knowledge graph, and it demonstrates how the knowledge graph can be utilised for improving data identification and data quality.

• Proceedings of the Safety Management and Science Conference
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• 2013.11a
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• pp.539-546
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• 2013

본 연구는 승마용 안전모의 신뢰성 확보를 위해 안전기준을 정립하기 위한 연구이다. 국내 외 레저 스포츠 안전모 관련 안전기준을 비교 분석하여 도출한 주요 시험평가방법을 현재 국내에 유통 중인 중저가 승마용 안전모를 대상으로 성능시험을 하였다. 충격흡수성 시험 시 1축과 3축 가속도 센서를 이용한 시험방법에 대해서 성능평가 데이터를 통해 1축 가속도 센서를 이용한 충격흡수성 시험으로 선정하였다. 유지시스템의 강도시험에서 헤드 폼 서포트 방식과 후크 서포트 방식으로 구분되던 것을 시료에 손상 없이 시험 가능한 헤드 폼 서포트 방식으로 결정하였다. 이와 같이 승마용 안전모의 안전기준을 정립하여 제품의 안전성 및 신뢰성을 확보하고 나아가 "품질경영 및 공산품 안전 관리법"에 규정함으로써 자국민의 안전을 확보하고 중국산 저가 제품에 대한 시장진입장벽을 구축하여 경쟁력 있는 안전모 시장을 구축하기를 기대한다.

• Proceedings of The KACE
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• 2018.01a
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• pp.49-52
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• 2018

스카이라인 질의는 데이터들 사이의 비교 연산을 통해 지배되지 않은 데이터들의 최소 집합을 스카이라인으로 탐색하며 이때 지배되지 않고 스카이라인으로 선택된 데이터들은 지배된 데이터들을 대표하게 된다. 이러한 특징은 금융, 네트워크, 웹서비스 등 다양한 분야에서 스카이라인의 활용을 이끌어냈다. 하지만 스카이라인 질의는 데이터의 양이나 차원의 수가 증가하는 경우 전체적인 성능이 크게 감소하는 문제를 야기하기 때문에 이를 해결하기 위한 다양한 기법들이 연구 및 제안되고 있다. 하지만 실제 스카이라인 질의를 활용하기 위해서는 객관적 성능 평가를 통해 주어진 상황에서 최적의 성능을 보일 수 있는 기법을 선택해야할 필요가 있지만 기존의 연구들은 성능 평가에 있어 각 기법이 목표한 문제들에 대한 단편적인 실험을 진행하고 있기 때문에 이들을 객관적으로 평가하기 위해서는 새로운 스카이라인 성능 평가 방법이 필요한 실정이다. 본 논문에서는 이러한 문제를 해결하기에 앞서 스카이라인 질의 기법의 객관적 성능 평가를 위한 품질 요소 선택 기준을 선택하기 위해 기존 연구들에 대한 조사와 분석을 진행한다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.11
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• pp.21-28
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• 2023

In manufacturing companies that focus on small-scale production of multiple product varieties, defective products are manually selected by workers rather than relying on automated inspection. Consequently, there is a higher risk of incorrect sorting due to variations in selection criteria based on the workers' experience and expertise, without consistent standards. Moreover, for non-standardized flexible objects with varying sizes and shapes, there can be even greater deviations in the selection criteria. To address these issues, this paper designs a quality inspection system using artificial intelligence-based unsupervised learning methods and conducts research by experimenting with accuracy using a dataset obtained from real manufacturing environments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.531-538
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• 2017

In the case of hydraulic pressure type fuse, we accept or reject certain product lots by considering the number of defective products in the operating pressure test. Generally, this procedure, known as 'The inspection by attributes', has been most commonly used in the field of quality assurance of products. However, the method of inspection by attributes suffers because it tests more samples than inspection by variables. Even though the quality of the products has remained stable in the process condition, the same number of samples is required for every lot, which wastes time and money. This paper suggests that the lot acceptance procedure is changed from inspection by attributes to inspection by variables. We can calculate the statistical tolerance percent of defectives and compare this to the Acceptable Quality Level (AQL) in order to save money and time. It is also easier to monitor and control the quality of products by using the process capability index and x-bar charts. In conclusion, the procedure delivers mutual benefit to both the customer and the producer by securing high quality products and reference data.