• 인터넷정보학회논문지
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• 제19권1호
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• pp.123-130
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• 2018

With the emergence of a new paradigm called Open Science and Big Data, the need for data sharing and collaboration is also emerging in the computational science field. This paper, we analyzed data-driven research cases for computational science by field; material design, bioinformatics, high energy physics. We also studied the characteristics of the computational science data and the data management issues. To manage computational science data effectively it is required to have data quality management, increased data reliability, flexibility to support a variety of data types, and tools for analysis and linkage to the computing infrastructure. In addition, we analyzed trends of platform technology for efficient sharing and management of computational science data. The main contribution of this paper is to review the various computational science data repositories and related platform technologies to analyze the characteristics of computational science data and the problems of data management, and to present design considerations for building a future computational science data platform.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권8호
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• pp.4133-4145
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• 2017

Advances in science and technology are driving us to the better life but also forcing us to make more investment at the same time. Therefore, the government has provided the investment to carry on the promising futuristic technology successfully. Indeed, a lot of resources from the government have supported into the science and technology R&D projects for several decades. However, the performance of the public investments remains unclear in many ways, so thus it is required that planning and evaluation about the new investment should be on data driven decision with fact based evidence. In this regard, the government wanted to know the trend and issue of the science and technology with evidences, and has accumulated an amount of database about the science and technology such as research papers, patents, project reports, and R&D information. Nowadays, the database is supporting to various activities such as planning policy, budget allocation, and investment evaluation for the science and technology but the information quality is not reached to the expectation because of limitations of text mining to drill out the information from the unstructured data like the reports and papers. To solve the problem, this study proposes a practical text mining methodology for the science and technology trend analysis, in case of aerospace technology, and conduct text mining methods such as ontology development, topic analysis, network analysis and their visualization.

• 문화기술의 융합
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• 제7권1호
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• pp.539-544
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• 2021

전술데이터링크는 전장상황 인식 능력을 향상하기 위한 군용 데이터 네트워크이다. 한국군은 전술데이터링크 성능개량상업을 추진하고 있다. 전술데이터링크는 다양한 플랫폼, 센서 데이터 및 지휘통제 체계와의 연동이 필수적이므로 연관 기술분야에 대한 연구가 필요하다. 그러나 전술데이터링크는 군 운영 특성상 다양한 기술정보가 공개되지 않고 있다. 본 논문에서는 전술데이터링크의 기술동향을 파악하기 위하여 지식재산 정보를 활용한 데이터기반 자동화 분석 방법론을 제안한다. 본 논문에서는 지식재산 관련 데이터를 자동으로 수집하고 전처리하며, 시계열에 따라 분석한다. 추가적으로 특허 기술정보의 기관별 현황을 종합하고 핵심 연구자를 네트워크 분석을 통한 식별 과정을 제시하고 각각의 결과를 제시하였다.

• 풍력에너지저널
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• 제14권3호
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• pp.34-42
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• 2023

The reduction of operation and maintenance (O&M) costs is a critical factor in determining the competitiveness of wind energy. Predictive maintenance based on the estimation of remaining useful life (RUL) is a key technology to reduce logistic costs and increase the availability of wind turbines. Although a mechanical component usually has sudden changes during operation, most RUL estimation methods use the trend of a state index over the whole operation period. Therefore, overestimation of RUL causes confusion in O&M plans and reduces the effect of predictive maintenance. In this paper, two RUL estimation methods (load based and data driven) are proposed for the bearings of a wind turbine with the results of trend classification, which differentiates constant and increasing states of the state index. The proposed estimation method is applied to a bearing degradation test, which shows a conservative estimation of RUL.

• 한국해양공학회지
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• 제36권3호
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• pp.194-210
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• 2022

Recently, an analysis method using machine learning for solving problems in coastal and ocean engineering has been highlighted. Machine learning models are effective modeling tools for predicting specific parameters by learning complex relationships based on a specified dataset. In coastal and ocean engineering, various studies have been conducted to predict dependent variables such as wave parameters, tides, storm surges, design parameters, and shoreline fluctuations. Herein, we introduce and describe the application trend of machine learning models in coastal and ocean engineering. Based on the results of various studies, machine learning models are an effective alternative to approaches involving data requirements, time-consuming fluid dynamics, and numerical models. In addition, machine learning can be successfully applied for solving various problems in coastal and ocean engineering. However, to achieve accurate predictions, model development should be conducted in addition to data preprocessing and cost calculation. Furthermore, applicability to various systems and quantifiable evaluations of uncertainty should be considered.

• 한국지하수토양환경학회지:지하수토양환경
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• 제21권5호
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• pp.8-15
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• 2016

Bioremediation, which uses microbes to degrade most organic pollutants in soil and groundwater, can be used in solving environmental issues in various polluted sites. In this research, a wind-driven bioventing system is built to degrade about 20,000 mg/kg of high concentration diesel pollutants in soil-pollution mode. The wind-driven bioventing test was proceeded by the bioaugmentation method, and the indigenous microbes used were Bacillus cereus, Achromobacter xylosoxidans, and Pseudomonas putida. The phenomenon of two-stage diesel degradation of different rates was noted in the test. In order to interpret the results of the mode test, three microbes were used to degrade diesel pollutants of same high concentration in separated aerated batch-mixing vessels. The data derived thereof was input into the Haldane equation and calculated by non-linear regression analysis and trial-and-error methods to establish the kinetic parameters of these three microbes in bioventing diesel degradation. The results show that in the derivation of μ_m (maximum specific growth rate) in biodegradation kinetics parameters, K_s (half-saturation constant) for diesel substance affinity, and K_i (inhibition coefficient) for the adaptability of high concentration diesel degradation. The K_s is the lowest in the trend of the first stage degradation of Bacillus cereus in a high diesel concentration, whereas K_i is the highest, denoting that Bacillus cereus has the best adaptability in a high diesel concentration and is the most efficient in diesel substance affinity. All three microbes have a degradation rate of over 50% with regards to Pristane and Phytane, which are branched alkanes and the most important biological markers.

• 한국융합학회논문지
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• 제10권12호
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• pp.271-280
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• 2019

제조산업 분야의 디지털트랜스포메이션의 일환인 스마트공장은 데이터 기반으로 모니터링 및 분석 그리고 예측을 통해서 의사결정 방식을 획기적으로 변화시키고 있다. 특히 설비에 대한 예지보전은 스마트공장의 핵심적인 요소로서 필요성이 증대되고 있다. 본 연구의 목적은 철강 도금공정의 예지보전을 위해 도금로 설비의 열화 특성을 고려한 잔존수명 분석과 예측모델을 산출하는 것이다. 상관성 분석, 다중회귀 분석, 주성분회귀 분석 그리고 시간의 경과에 따른 열화의 추이 파악을 위하여 이동회귀 방식을 제안하여 진행하였다. 그 결과 도금로 열화는 생산성 인자들과 주된 의존적 관계가 있으며, 특히 환경 온도 인자들의 영향성이 열화의 추이 변화에 관계가 있음을 추론할 수 있었다. 예측된 잔존수명을 활용하여 도금로 교체가 필요한 시점을 사전에 알려주는 예지보전을 구현하였다. 향후 설비의 열화 추이 파악에 본 연구에서 수행한 방안이 적절한 사례가 되어 보다 정합성 있는 예지보전 구현이 가능해지기를 기대한다.

• Asia Pacific Journal of Business Review
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• 제6권2호
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• pp.27-60
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• 2022

Previous studies stated that low carbon innovation performance could be influenced by government regulations and the green market, which is the new trend of consumer consumption in the present time, mainly focusing on external factors. Before study augured that low carbon innovation performance could be driven by internal and external factors of cooperation such as institutional pressure, stakeholder pressure, and innovation resources. However, the study of green transformational leadership and organizational environmental culture on low carbon innovation performance is rare, especially in Chinese manufacturing, as well as the effect of influencing factors of TPB model: environmental attitude, subjective norm, and perceived behavior capability on low carbon innovation performance. Previous studies mostly used the TPB model for predicting individual behavior. This study established a theoretical model combining the TPB model with green transformational leadership and organizational environmental culture of Chinese automobile manufacturing on low carbon innovation performance. This study consists of two sections of research methodology: section 1 related to questionnaire design and data collection. We established a questionnaire and distributed it online, targeting responses from the managerial level working in Chinese automobile manufacturing. Eventually, 155 valid questionnaires were used for analysis. Section 2 involved data analysis using statistical software. Reliability and data validity was examined by reliability analysis and factor analysis. Correlations and convergent validity analyses were applied, and structural equation modeling was conducted to test the proposed hypotheses. The findings indicated that green transformational leadership, organizational environmental culture, and essential factors of TPB model; environmental attitude, subjective norm and perceived behavior capability positively affect low carbon innovation performance. In addition, the indirect effect of green transformational leadership was tested and found that organizational environmental culture and TPB factors mediated the relationship between transformational leadership and low carbon innovation performance.

• 한국시뮬레이션학회논문지
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• 제31권3호
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• pp.35-44
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• 2022

본 연구에서는 양자 컴퓨터 관련 기술 트렌드 분석과 예측을 수행한다. 기존 양자 컴퓨터 기술 분석 관련 연구는 주로 기술 특징을 중심으로 응용 가능 분야에 집중되었다. 본 논문은 시장 중심의 기술 분석과 예측을 위하여 양자 컴퓨터 관련 국내 뉴스 기사를 기반으로 중요하게 다뤄지는 양자 컴퓨터 기술들을 분석하고 미래신호 감지와 예측을 수행한다. 뉴스 기사에서 사용된 단어들을 분석하여 빠르게 변화하는 시장의 변화와 대중의 관심사를 파악한다. 본 논문은 Cha & Chang (2022) 컨퍼런스 발표 자료를 확장했다. 연구는 2019년부터 2021년까지의 국내 뉴스 기사를 수집하여 진행된다. 먼저, 텍스트 마이닝을 통해 주요 키워드를 정리한다. 다음으로, Term Frequency - Inverse Document Frequency(TF-IDF), Key Issue Map(KIM), Key Emergence Map(KEM) 등의 분석을 통해 양자컴퓨터관련 기술을 탐색한다. 마지막으로, 랜덤포레스트, 의사결정나무, 연관분석 등을 통해 미래기술들과 수요 및 공급의 연관성을 파악한다. 연구결과 빈도분석, 키워드 확산도 및 가시성 분석에서 모두 AI의 관심도가 가장 높게 나타났다. 사이버보안의 경우 시간이 지날수록 뉴스기사에서 언급되는 비율이 다른 기술에 비해 압도적으로 높게 나타났다. 또한 양자통신, 내성암호, 증강현실 역시 관심도의 증가율이 높게 나타났다. 따라서 이를 트렌드 기술의 적용에 대한 시장의 기대가 높음을 알 수 있다. 본 연구의 결과는 양자컴퓨터 시장의 관심 분야 파악과 기술 투자 관련 대응체계 구축에 응용될 수 있다.

• Ocean and Polar Research
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• 제38권2호
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• pp.149-159
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• 2016

The statistical measures of the coastal environmental data are used in a variety of statistical inferences, hypothesis tests, and data-driven modeling. If the measures are biased, then the statistical estimations and models may also be biased and this potential for bias is great when data contain some outliers defined as extraordinary large or small data values. This study aims to suggest more robust statistical measures as alternatives to more commonly used measures and to assess the performance these robust measures through a quantitative evaluation of more typical measures, such as in terms of locations, spreads, and shapes, with regard to environmental monitoring data in the Busan coastal sea. The detection of outliers within the data was carried out on the basis of Rosner's test. About 5-10% of the nutrient data were found to contain outliers based on Rosner's test. After removal (zero-weighting) of the outliers in the data sets, the relative change ratios of the mean and standard deviation between before and after outlier-removal conditions revealed the figures 13 and 33%, respectively. The variation magnitudes of skewness and kurtosis are 1.36 and 8.11 in a decreasing trend, respectively. On the other hand, the change ratios for more robust measures regarding the mean and standard deviation are 3.7-10.5%, and the variation magnitudes of robust skewness and kurtosis are about only 2-4% of the magnitude of the non-robust measures. The robust measures can be regarded as outlier-resistant statistical measures based on the relatively small changes in the scenarios before and after outlier removal conditions.