Sung-Woo Park, "Data Mining Method for Offshore Structures Based on Big Data Technology", M.Sc. Thesis, Seoul National University, 2019.02.26
박성우, "빅데이터 기술을 이용한 해양 구조물의 데이터 마이닝 방법", 석사학위논문, 서울대학교, 2019.02.26
M.Sc. Thesis
2019.02.15 16:30
Sung-Woo Park, "Data Mining Method for Offshore Structures based on Big Data Technology", M.Sc. Thesis, Seoul National University, 2019.02.26
조회 수 1110
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| Abstract | As many products as ships and offshore structures are constructed in the shipyard, and various data are generated and stored in the design or construction stage. Big data technology needs to be applied to process data of large size quickly, obtain meaningful results and use it for decision making. In this paper, we propose a solution to two of the problems that may occur in the shipyard. One of the two problems which can arise in the shipyard has mainly happened in the design stage. Engineers can make the mistake of choosing the wrong material in the design process, and the wrong material selection in the design process can directly lead to a design error. Another problem may arise during the procurement and purchase process. In the absence of additional information such as lead time of material or inventory at the time of procurement, additional time is required to retrieve the data. Both problems arise predominantly from the unskilled. Therefore, the purpose of this study is to establish a system that can inform the engineers about the relationships between materials which can be obtained by association analysis and material requirements which can be obtained by regression analysis. This kind of system can help the engineers to reduce design errors and time consuming due to the procurement process. The information of piping materials used in an offshore structure can be regarded as ‘big data’ because of their various types and size, and the data mining algorithms based on the big data technology are applied to data related to the offshore structures. To analyze the relationship between materials for design, ‘frequent pattern growth algorithm’ was used. For material requirement analysis, big data technology-based regression analysis was used to generate a regression model, respectively. Finally, the proposed method was used to check the relationship between materials, and to predict material requirement, and verified the effectiveness of the proposed method by comparing each result with actual cases. |
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| Publication Date | 2019-02-26 |
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