산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제46권3호
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  • Pages.186-197
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  • 2023
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  • 2005-0461(pISSN)
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  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
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해양기상부표의 센서 데이터 품질 향상을 위한 프레임워크 개발

Development of a Framework for Improvement of Sensor Data Quality from Weather Buoys

  • 이주용 (강원대학교 경영회계학부) ;
  • 이재영 (부산대학교 산업공학과) ;
  • 이지우 (부산대학교 산업공학과) ;
  • 신상문 (동아대학교 산업경영공학과) ;
  • 장준혁 (선박해양플랜트연구소 해사디지털서비스연구센터) ;
  • 한준희 (부산대학교 산업공학과)
  • Ju-Yong Lee (Division of Business Administration & Accounting, Kangwon National University) ;
  • Jae-Young Lee (Department of Industrial Engineering, Pusan National University) ;
  • Jiwoo Lee (Department of Industrial Engineering, Pusan National University) ;
  • Sangmun Shin (Department of Industrial & Management Engineering, Dong-A University) ;
  • Jun-hyuk Jang (Maritime Digital Transformation Research Center, Korea Research Institute of Ships & Ocean Engineering) ;
  • Jun-Hee Han (Department of Industrial Engineering, Pusan National University)
  • 투고 : 2023.09.15
  • 심사 : 2023.09.18
  • 발행 : 2023.09.30
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초록

In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy's status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of 'AIR_TEMPERATURE' data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real-world scenarios.

키워드

과제정보

This research was supported by Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries (20210650). This work was also supported by "Human Resources Program in Energy Technology" of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 202106540003)

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