Sensor Data Quality in Ships: A Time Series Forecasting Approach to Compensate for Missing Data and Drift in Measurements of Speed through Water Sensors
Author:
Affiliation:
1. Department of Industrial Design and Production Engineering, University of West Attica, 12243 Athens, Greece
2. Naval Architecture and Marine Engineering Section, Hellenic Naval Academy, 18539 Piraeus, Greece
Abstract
Funder
RESEARCH–CREATE–INNOVATE
Publisher
MDPI AG
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering,Engineering (miscellaneous)
Link
https://www.mdpi.com/2411-9660/7/2/46/pdf
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5. Zhao, X., Li, P., Xiao, K., Meng, X., Han, L., and Yu, C. (2019). Sensor Drift Compensation Based on the Improved LSTM and SVM Multi-Class Ensemble Learning Models. Sensors, 19.
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