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The value of human data annotation for machine learning based anomaly detection in environmental systems
Russo, S., Besmer, M. D., Blumensaat, F., Bouffard, D., Disch, A., Hammes, F., … Villez, K. (2021). The value of human data annotation for machine learning based anomaly detection in environmental systems. Water Research, 206, 117695 (10 pp.). https://doi.org/10.1016/j.watres.2021.117695
Active learning for anomaly detection in environmental data
Russo, S., Lürig, M., Hao, W., Matthews, B., & Villez, K. (2020). Active learning for anomaly detection in environmental data. Environmental Modelling and Software, 134, 104869 (11 pp.). https://doi.org/10.1016/j.envsoft.2020.104869
Shape anomaly detection for process monitoring of a sequencing batch reactor
Villez, K., & Habermacher, J. (2016). Shape anomaly detection for process monitoring of a sequencing batch reactor. Computers and Chemical Engineering, 91, 365-379. https://doi.org/10.1016/j.compchemeng.2016.04.012
Shape constrained splines with discontinuities for anomaly detection in a batch process
Villez, K., & Habermacher, J. (2015). Shape constrained splines with discontinuities for anomaly detection in a batch process. In K. V. Gernaey, J. K. Huusom, & R. Gani (Eds.), Computer aided chemical engineering: Vol. 37. 12th international symposium on process systems engineering and 25th European symposium on computer aided process engineering (pp. 1805-1810). https://doi.org/10.1016/B978-0-444-63577-8.50146-7