• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.121-129
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• 2021

In this study, we try to minimize the tariff risk by constructing a hazardous cargo screening model by applying Association Rule Mining, one of the data mining techniques. For this, the risk level between supply chains is calculated using the Apriori Algorithm, which is an association analysis algorithm, using the big data of the import declaration form of the Korea Customs Service(KCS). We perform data preprocessing and association rule mining to generate a model to be used in screening the supply chain. In the preprocessing process, we extract the attributes required for rule generation from the import declaration data after the error removing process. Then, we generate the rules by using the extracted attributes as inputs to the Apriori algorithm. The generated association rule model is loaded in the KCS screening system. When the import declaration which should be checked is received, the screening system refers to the model and returns the confidence value based on the supply chain information on the import declaration data. The result will be used to determine whether to check the import case. The 5-fold cross-validation of 16.6% precision and 33.8% recall showed that import declaration data for 2 years and 6 months were divided into learning data and test data. This is a result that is about 3.4 times higher in precision and 1.5 times higher in recall than frequency-based methods. This confirms that the proposed method is an effective way to reduce tariff risks.

• Annual Conference of KIPS
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• 2010.11a
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• pp.1743-1745
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• 2010

클라우드 컴퓨팅 환경을 기반으로 하는 비즈니스 생명주기는 직접적인 어플리케이션이나 서비스의 구현을 포함해서, 기획, H/W나 S/W 프로비저닝, 운용 및 관리, 평가와 같은 과정을 요구한다. 이 모든 과정은 다양한 형태의 비즈니스 자산들을 필요로 하면서, 또 다른 새로운 자산들을 만들어낸다. 반복되는 비즈니스 생명주기에서 생성된 비즈니스 자산의 재사용성을 극대화할 수 있다면, 신속하고 효과적인 클라우드 기반의 비즈니스를 추구할 수 있는데, 이러한 과정의 중심에는 효과적인 레포지토리의 구축이 우선된다. 이에 본 논문은 클라우드기반 비즈니스 시스템의 특징을 살펴보고, 비즈니스 생명주기의 각 단계에서 요구되는 레포지토리의 요구사항을 분석하여 적합한 메타데이터 및 데이터 아키텍처를 설계하고 제안하고자 한다. 또한, 오픈소스 시스템을 통해 제안하는 시스템의 활용가능성을 확인하고자 한다.

• Journal of Science and Technology Studies
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• v.18 no.2
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• pp.7-41
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• 2018

The projects of citizen science which is originated from citizen data collecting action driven by governmental institutes and science associations have been implemented with different form of collaboration with scientists. The themes of citizen science has extended from the ecology to astronomy, distributed computing, and particle physics. Citizen science could contribute to the advancement of science through cost-effective science research based on citizen volunteer data collecting. In addition, citizen science enhance the public understanding of science by increasing knowledge of citizen participants. The community-led citizen science projects could raise public awareness of environmental problems and promote the participation in environmental problem-solving. Citizen science projects based on local tacit knowledge can be of benefit to the local environmental policy decision making and implementation of policy. These social values of citizen science make many countries develop promoting policies of citizen science. The korean government also has introduced some citizen science projects. However there are some obstacles, such as low participation of citizen and scientists in projects which the government has to overcome in order to promote citizen science. It is important that scientists could recognize values of citizen science through the successful government driven citizen science projects and the evaluation tool of scientific career could be modified in order to promote scientist's participation. The project management should be well planned to intensify citizen participation. The government should prepare open data policy which could support a data reliability of the community-led monitoring projects. It is also desirable that a citizen science network could be made with the purpose of sharing best practices of citizen science.

• Journal of Information Management
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• v.33 no.4
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• pp.107-128
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• 2002

The purpose of this study is developing a information support system for R&D decision making to maximize economic results of the R&D. This system is composed of studying the model of work flow for R&D decision making, analyzing a technology information, connecting with the databases from KISTI and others, and valuing R&D technology on line. Especially in the case of technology valuation, this system is combined with the valuation model which supports knowledge information for helping more objective estimation.

• Journal of Information Management
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• v.39 no.2
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• pp.211-234
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• 2008

Recently, the frequency and scale of natural disasters such as typhoons, flood, earthquakes, and tidal waves from earthquakes has been increasing. Several nations have recognized that earth observation is essential for protecting the Earth's environment. However, as the data format from earth observation varies depending on areas, institutes, and countries, sharing and exchange between data is difficult. Thus, we have a metadata standardization scheme suitable for the domestic situation to allow exchange of data between societal benefit areas with reference to principles of data sharing and exchange that are discussed on GEO (Group on Earth Observation). We have also designed metadata schemes required to identify the metadata situation of earth observation data being used for 9 societal benefit areas of GEOSS(Global Earth Observation System of Systems).

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.172-172
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• 2022

분산계수는 하천에서 오염물질의 혼합능을 파악할 수 있는 대표적인 인자이다. 특히 하수처리장 방류수 혼합예측과 같이 횡 방향 혼합에 대한 예측이 중요한 경우, 하천의 지형적, 수리학적 특성을 고려한 2차원 횡 분산계수의 결정이 필요하다. 2차원 횡 분산계수의 결정을 위해 기존 연구에서는 추적자실험결과로부터 경험식을 만들어 횡 분산계수 산정에 사용해왔다. 회귀분석을 통한 경험식 산정을 위해서는 충분한 데이터가 필요하지만, 2차원 추적자 실험 건수가 충분치 않아 신뢰성 높은 경험식 산정이 어려운 상황이다. 따라서 본 연구에서는 SMOTE기법을 이용하여 횡분산계수 실험데이터를 증폭시켜 이로부터 횡 분산계수 경험식을 산정하고자 한다. 또한 다중선형회귀분석을 통해 도출된 경험식의 한계를 보완하기 위해 다양한 머신러닝 기법을 적용하고, 횡 분산계수 산정에 적합한 머신러닝 기법을 제안하고자 한다. 기존 추적자실험 데이터로부터 하폭 대 수심비, 유속 대 마찰유속비, 횡 분산계수 데이터 셋을 수집하였으며, SMOTE 알고리즘의 적용을 통해 회귀분석과 머신러닝 기법 적용에 필요한 데이터그룹을 생성했다. 새롭게 생성된 데이터 셋을 포함하여 다중선형회귀분석을 통해 횡 분산계수 경험식을 결정하였으며, 새로 제안한 경험식과 기존 경험식에 대한 정확도를 비교했다. 또한 다중선형회귀분석을 통해 결정된 경험식은 횡 분산계수 예측범위에 한계를 보였기 때문에 머신러닝기법을 적용하여 다중선형회귀분석에 대한 예측성능을 평가했다. 이를 위해 머신러닝 기법으로서 서포트 벡터 머신 회귀(SVR), K근접이웃 회귀(KNN-R), 랜덤 포레스트 회귀(RFR)를 활용했다. 세 가지 머신러닝 기법을 통해 도출된 횡 분산계수와 경험식으로부터 결정된 횡 분산계수를 비교하여 예측 성능을 비교했다. 이를 통해 제한된 실험데이터 셋으로부터 2차원 횡 분산계수 산정을 위한 데이터 전처리 기법 및 횡 분산계수 산정에 적합한 머신러닝 절차와 최적 학습기법을 도출했다.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.887-892
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• 2023

Vessel traffic service(VTS) centers are equipped with RADAR, AIS(Automatic Identification System), weather sensors, and VHF(Very High Frequency). VTS operators use this equipment to observe the movement of ships operating in the VTS area and provide information. The VTS data generated by these various devices is highly valuable for analyzing maritime traffic situation. However, owing to a lack of compatibility between system manufacturers or policy issues, they are often not systematically managed. Therefore, we developed the VTS Bigdata Platform that could efficiently collect, store, and manage control data collected by the VTS, and this paper describes its design and implementation. A microservice architecture was applied to secure operational stability that was one of the important issues in the development of the platform. In addition, the performance of the platform could be improved by dualizing the storage for real-time navigation information. The implemented system was tested using real maritime data to check its performance, identify additional improvements, and consider its feasibility in a real VTS environment.

• The Korean Journal of Applied Statistics
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• v.36 no.2
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• pp.141-166
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• 2023

This paper investigates synthetic data generation methods and their evaluation measures. There have been increasing demands for releasing various types of data to the public for different purposes. At the same time, there are also unavoidable concerns about leaking critical or sensitive information. Many synthetic data generation methods have been proposed over the years in order to address these concerns and implemented in some countries, including Korea. The current study aims to introduce and compare three representative synthetic data generation approaches: Sequential regression, nonparametric Bayesian multiple imputations, and deep generative models. Several evaluation metrics that measure the utility and disclosure risk of synthetic data are also reviewed. We provide empirical comparisons of the three synthetic data generation approaches with respect to various evaluation measures. The findings of this work will help practitioners to have a better understanding of the advantages and disadvantages of those synthetic data methods.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.123-134
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• 2018

Recently, due to the rising incidence of disasters in the nation, there has been a growing interest in the relevance and role of science and technology in solving disaster and safety related issues. In addition, the necessities of securing the human rights of all citizens in disaster risk reduction, identifying fields of technology development for effective disaster response, and improving the efficiency of R&D investment for disaster and safety are becoming more important as the different types of disasters and stages of disaster and safety management process have been considered. In this study, we analyzed bipartite or two-mode networks constructed from an expert survey dataset of technology development for disaster and safety management. The results reveal that earthquake and fire are the two disasters affecting an individual and society at large and demonstrate that AI and big data analytics are effective supports in managing disaster and safety. We believe that such a network analytic approach can be used to explore some important implications exist for the national science and technology effort and successful disaster and safety management practices in Korea.

• Journal of Technology Innovation
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• v.28 no.3
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• pp.55-76
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• 2020

The traditional occupational boundaries of human resources in science and technology (S&T) have quickly blurred in Korea. On the one hand, the knowledge-based economy has emerged and S&T proliferated beyond conventional areas, leading scientists and engineers to advance into various convergence fields. On the other hand, Korea's labor market is characterized by a higher percentage of highly-educated human resources and a relatively smaller number of high-quality jobs. As a result, the highly educated in S&T have flowed over the traditional careers into non-S&T careers. Focusing on doctorates in S&T, this paper analyzes changes in their career patterns and identifies main determinants. Specifically, jobs are categorized into traditional STEM occupations and the others in order to identify fluctuations in their share and to analyze factors affecting such changes. The analyses are based on data from the 'Survey on Careers and Mobility of Doctorate Holders 2012' conducted by the Science and Technology Policy Institute. The results exhibit marked changes in the occupational composition of doctorates in S&T. Occupational diversification has been proceeded faster in natural sciences, the private sector, and the younger generation than in engineering, the public sector, and the older generation.