해양기상부표의 센서 데이터 품질 향상을 위한 프레임워크 개발

• 영문명: Development of a Framework for Improvement of Sensor Data Quality from Weather Buoys
• 발행기관: 한국산업경영시스템학회
• 저자명: 이주용 이재영 이지우 신상문 장준혁 한준희
• 간행물 정보: 『산업경영시스템학회지』제46권 제3호, 186~197쪽, 전체 12쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2023.09.30

영문 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 re- al-world scenarios.

목차

1. Introduction

2. Related Studies and limitations

3. Models for Data Quality Improvement

4. Computational Experiments

5. Conclusion

References

키워드

Weather Buoy Data Quality Management Machine Learning Data Fault Detection Data Interpolation