9 papers
A multi-level preprocessing and modelling framework for spectral imaging of microplastics
Zina-Sabrina Duma, Tenzin Tsering, Sara Heikkinen +4
Spectral imaging provides chemically specific and spatially resolved analysis of microplastics, but its routine application is hindered by large data volumes, acquisition artefacts…
An unsupervised kernel norm monitoring for fault detection in a time series photovoltaic system
Victoria Jorry, Zina-Sabrina Duma, Satu-Pia Reinikainen +2
Grid-connected photovoltaic systems (GCPVS) are generally robust but remain susceptible to faults that can compromise energy conversion efficiency or raise safety concerns. Promptl…
Probabilistic multivariate statistical process control via kernel parameter uncertainty propagation
Zina-Sabrina Duma, Victoria Jorry, Ayesha Safraz +4
Kernel-based multivariate statistical process control (K-MSPC) extends classical monitoring to nonlinear industrial processes. Its performance depends critically on kernel paramete…
Uncertainty calibration for latent-variable regression models
Zina-Sabrina Duma, Otto Lamminpää, Jouni Susiluoto +6
Uncertainty quantification is essential for scientific analysis, as it allows for the evaluation and interpretation of variability and reliability in complex systems and datasets.…
Optimising Kernel-based Multivariate Statistical Process Control
Zina-Sabrina Duma, Victoria Jorry, Tuomas Sihvonen +2
Multivariate Statistical Process Control (MSPC) is a framework for monitoring and diagnosing complex processes by analysing the relationships between multiple process variables sim…
Kernel-based retrieval models for hyperspectral image data optimized with Kernel Flows
Zina-Sabrina Duma, Tuomas Sihvonen, Jouni Susiluoto +3
Kernel-based statistical methods are efficient, but their performance depends heavily on the selection of kernel parameters. In literature, the optimization studies on kernel-based…