Abstract
In this work, inspired by the interpretability and usefulness of the statistical process control, we propose a novel procedure for simultaneous monitoring of multiple processes that is based on a neural network with learnable activation functions. The proposed procedure for learning control limits with neural network (CONNF) is aimed at scenarios where labeled data are available and makes use of these labels. CONNF can be particularly useful in monitoring processes when the amount of run-in data is insufficient, or the cost of obtaining such data is high. We illustrate the performance of CONNF method with a simulation study and preliminary results for real-life data collected from smartphones of patients with diagnosed bipolar disorder. These results show the potential of CONNF and indicate further research directions.
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Notes
- 1.
The study was conducted in the Department of Affective Disorders, Institute of Psychiatry and Neurology in Warsaw, Poland within the project entitled “Smartphone-based diagnostics of phase changes in the course of bipolar disorder”.
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Acknowledgements
This work is supported by the Small Grants Scheme (NOR/SGS/BIPOLAR/ 0239/2020-00) within the research project: Bipolar disorder prediction with sensor-based semi-supervised learning (BIPOLAR). The authors thank the researcher Gennaro Vessio for inspiring discussion and advice.
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Kmita, K., Kaczmarek-Majer, K., Hryniewicz, O. (2023). Learning Control Limits for Monitoring of Multiple Processes with Neural Network. In: García-Escudero, L.A., et al. Building Bridges between Soft and Statistical Methodologies for Data Science . SMPS 2022. Advances in Intelligent Systems and Computing, vol 1433. Springer, Cham. https://doi.org/10.1007/978-3-031-15509-3_32
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