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Edwin Lughofer

 

The presenter will conceive a new paradigm in the calibration and design of chemometric models from (FT-)NIR spectra. Opposed to batch off-line calibration through the usage of classical statistical methods (such as PLSR, PCR and several extensiond) or more general machine learning based methods (such as support vector machines, neural networks, fuzzy systems), evolving chemometric models can serve as core engine for addressing the incremental updating of calibration models fully automatically in on-line or even in-line installations. Such updates may become indispensable whenever a certain system dynamics or non-stationary environmental influences cause significant changes in the process. Typically, models trained in batch off-line mode then become outdated easily, leading to severe deteriorations of their quantification accuracy, which may even badly influence the (supervision of the) whole chemical process. An approach how to update chemometric models quickly and ideally with lowest possible costs in terms of additional target measurements will be presented in this talk. It will be based on PLS-fuzzy models where the latter are trained based on the score space obtained through the latent variables. This leads to a new form of a non-linear PLSR with embedded piece-wise local predictors, having granular characteristics and even offering some interpretability aspects. The update of the models will comprise

  • Recursive parameter adaptation to adapt to permanent process changes and to increase model significance and accuracy (especially when models are off-line calibrated only on a handful of data).
  • Evolution of new model components (rules) on the fly in order to account for variations in the process such as new operations modes, system states, which requires a change in the model’s ‘non-linearity degree’.
  • Incremental adaptation of the PLS space in order to address a shift in the importance of wavelengths (on the target) over time.

In order to reduce target measurements during on-line usage, the model update will only take place upon drift (=change) alarms (induced by incremental drift indicators) and then with actively selected samples; therefore, a single-pass active learning paradigm will be exploited respecting feature space exploration. In order to omit target measurements for model adaptation completely, unsupervised adaptation strategies for fuzzy models and a new variant of PLS, termed as domain invariant PLS (di-PLS), will be demonstrated.

The talk will be concluded with two real-world applications from the chemical industry (melamine resin and viscose production), where the evolving chemometric models have been successfully installed and used; some results will be presented.

 

Mini-CV:

Edwin Lughofer received his PhD-degree from the Johannes Kepler University Linz (JKU) in 2005. He is currently Key Researcher with the Fuzzy Logic Laboratorium Linz / Department of Knowledge-Based Mathematical Systems (JKU) in the Softwarepark Hagenberg, see www.flll.jku.at/staff/edwin/.

He has participated in several basic and applied research projects on European and national level, with a specific focus on topics of Industry 4.0 and FoF (Factories of the Future). He has published around 170 publications in the fields of evolving fuzzy systems, machine learning and vision, data stream mining, chemometrics, active learning, classification and clustering, fault detection and diagnosis, quality control, predictive maintenance, including 60 journals papers in SCI-expanded impact journals, a monograph on ’Evolving Fuzzy Systems’ (Springer) and an edited book on ’Learning in Non-stationary Environments’ (Springer). In sum, his publications received 2900 references achieving an h-index of 33. He is associate editor of the international journals IEEE Transactions on Fuzzy Systems, Evolving Systems, Information Fusion, Soft Computing and Complex and Intelligent Systems, the general chair of the IEEE Conference on EAIS 2014 in Linz, the publication chair of IEEE EAIS 2015, 2016 and 2017, and the Area chair of the FUZZ-IEEE 2015 conference in Istanbul. He co-organized around 20 special issues and special sessions in international journals and conferences. In 2006 he received the best paper award at the International Symposium on Evolving Fuzzy Systems, in 2013 the best paper award at the IFAC conference in Manufacturing Modeling, Management and Control (800 participants) and in 2016 the best paper award at the IEEE Intelligent Systems Conference.

 

 

 

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