References
Abedjan, Z. et al. (2016). Detecting Data Errors: Where are we and what needs to be done. [Web]. Cited 01.07.2020. Available from: http://www.vldb.org/pvldb/vol9/p993-abedjan.pdf
Berrebi, J. et al (2004). Ultrasonic Flow Metering Errors Due to Pulsating Flow. EISLAB, Dept. of Computer Science and Electrical Engineering, Lulea University of Technology, Sweden. [Web]. Cited 06.05.2020. Available from: https://www.diva-portal.org/smash/get/diva2:989127/FULLTEXT01.pdf
Birgersson, M. et al. (2009), Bentonite erosion, Final Report, SKB Technical report TR-09-34 [Web]. Cited 12.04.2020. Available from: https://www.skb.se/publication/1975568/TR-09-34.pdf
Brownlee, J. (2016). Naive Bayes for Machine Learning. [Web]. Cited 25.06.2020. Available from: https://machinelearningmastery.com/naive-bayes-for-machine-learning/#
Catak, M. and Ergan, C. (2019). Self-Calibration of Ultrasonic Water Flow Meter. International Journal of Recent Technology and Engineering (IJRTE). Vol. 9, Issue 4., November 2019. [Web]. Cited 05.05.2020. Available from: https://www.researchgate.net/publication/338886029_Self-Calibration_of_Ultrasonic_Water_Flow_Meter
College Physics Labs (2020). Measurement and Error Analysis. The University of North Carolina at Chapel Hill. Retrieved from webassign.net. [Web]. Cited 18.05.2020. Available from: https://www.webassign.net/question_assets/unccolphysmechl1/measurements/manual.html
Eren, H. (1998). Accuracy in Real Time Ultrasonic Applications and Transit-time Flow Meters. IEEE Instrumentation and Measurement Technology Conference, Ottawa, Canada, May 18-21.
Flovik, V. (2019). How to use machine learning for anomaly detection and condition monitoring. [Web]. Cited 29.06.2020. Available from: https://towardsdatascience.com/how-to-use-machine-learning-for-anomaly-detection-and-condition-monitoring-6742f82900d7
Hamouda, A. et al (2016). An Enhanced Technique for Ultrasonic Flow Metering Featuring Very Low Jitter and Offset. Sensors 16. 1008.
International Organization for Standardization - ISO 5168. Measurement of fluid flow: Procedures for the evaluation of uncertainties. Geneva, Switzerland, 65 p., 2005.
Kalla, S. (2009). Random Error. Retrieved from Explorable.com. [Web]. Cited 18.05.2020. Available from: https://explorable.com/random-error
Lansing, J. (2003). Principles of Operation for Ultrasonic Gas Flow Meters. Daniel Measurement and Control, Inc. [Web]. Cited 08.05.2020. Available from: https://asgmt.com/wp-content/uploads/pdf-docs/2003/1/11.pdf
Metzen, J.H. (2015). Probability Calibration. [Web]. Cited 25.06.2020. Available from: https://jmetzen.github.io/2015-04-14/calibration.html
Othman, A. (2007). Drilling Engineering - SKM3413. [Web]. Cited 20.02.2020. Available from: https://www.scribd.com/doc/89388268/Drilling-Mud-Calculations
Simurda, M. et al. (2016). Modelling of Transit-Time Ultrasonic Flow Meters Under Multiphase Flow Conditions. 2016 IEEE International Ultrasonics Symposium (IUS). DOI: 10.1109/ULTSYM.2016.7728844
Weissenbrunner, A. et al (2016). Simulation-Based Determination of Systematic Errors of Flow Meters Due to Uncertain Inflow Conditions. Flow Measurement and Instrumentation. Vol. 52, 25-39. [Web]. Cited 06.05.2020. Available from: https://www.sciencedirect.com/science/article/pii/S0955598616300875
Yazdanshenashad, B. et al. (2018). Neural-network-based Error Reduction in Calibrating Utility Ultrasonic Flow Meters. Flow Measurement and Instrumentation 64, 54-63.
Zhang, H. (2004). The optimality of Naive Bayes. Proc. FLAIRS. Cited 18.05.2020. Available from: https://www.cs.unb.ca/~hzhang/publications/FLAIRS04ZhangH.pdf