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Cernuda C.,Johannes Kepler University | Lughofer E.,Johannes Kepler University | Hintenaus P.,University of Salzburg | Marzinger W.,I RED Infrarot Systeme GmbH | And 3 more authors.
8th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2013 - Advances in Intelligent Systems Research | Year: 2013

In this paper we investigate the usage of non-linear chemometric models, which are calibrated based on near infrared (FTNIR) spectra, in order to increase efficiency and to improve quantification quality in melamine resin production. They rely on fuzzy systems model architecture and are able to incrementally adapt themselves during the on-line process, resolving dynamic process changes, which may cause severe error drifts of static models. The most informative wavebands in NIR spectra are extracted by a new variant of forward selection, termed as forward selection with bands (FSB) and used as inputs for the fuzzy models. A specific ensemble strategy is developed which is able to properly compensate noise in repeated spectra measurements. Results on high-dimensional data from four independent types of melamine resin show that 1.) our fuzzy modeling methodology can outperform state-of-the-art chemometric modeling methods in terms of validation error, 2.) the ensemble strategy is able to improve the performance of models without ensembling and 3.) incremental model updates are necessary in order to preventdrifting residuals. © 2013. The authors -Published by Atlantis Press.


Cernuda C.,Johannes Kepler University | Lughofer E.,Johannes Kepler University | Hintenaus P.,University of Salzburg | Marzinger W.,I RED Infrarot Systeme GmbH | And 3 more authors.
Chemometrics and Intelligent Laboratory Systems | Year: 2013

In melamine resin production process, it is essential to supervise the condensation process. Monitoring the value of the cloud point indicates the best point of time to stop the condensation. Currently, the supervision is conducted manually by operators, which from time to time need to draw and analyze samples from the production process. In order to increase efficiency and to improve quantification quality, in this paper we investigate the usage of non-linear chemometric models, which are calibrated based on near infrared (FTNIR) process spectrum measurements. They rely on fuzzy systems model architecture and are able to incrementally adapt themselves during the on-line process, resolving dynamic process changes which may appear on-line over time due to long-term fluctuations (e.g., caused by dirt) and changes in the composition of the educt, often leading to severe error drifts of static models. Extracting the most informative wavebands prior to model training is essential to avoid a curse of dimensionality; this is achieved by a new extended variant of forward selection, termed as forward selection with bands (. FSB). Furthermore, variants of how to integrate auxiliary sensor information (temperature, pH value, pressure) together with the FTNIR spectra are presented (. hybridity). A specific ensemble strategy is developed which is able to properly compensate noise in repeated spectrum measurements. Results on high-dimensional data from four independent types of melamine resin show that 1) our non-linear modeling methodology can outperform state-of-the-art linear and non-linear chemometric modeling methods in terms of validation error, 2) the ensemble strategy is able to improve the performance of models without ensembling significantly and 3) incremental model updates are necessary in order to keep the predictive quality of the models high by preventing drifting residuals. © 2013 Elsevier B.V.


Cernuda C.,Johannes Kepler University | Lughofer E.,Johannes Kepler University | Hintenaus P.,University of Salzburg | Marzinger W.,I RED Infrarot Systeme GmbH
Journal of Chemometrics | Year: 2014

Nowadays, the techniques employed in data acquisition provide huge amounts of data. Some parts of the information are related to the others, making dimensionality reduction desirable, and losing less information as much as possible, in order to decrease computational times and complexity when applying any ensuing data mining technique. Genetic algorithms offer the possibility of selecting which variables contain the most relevant information to represent all the original ones. The traditional genetic operators seem to be too general, leading to results that could be improved by means of designed genetic operators that employ some available problem-specific information. Especially, when dealing with calibration by means of near-infrared spectral data, which use to contain thousands of variables, it is known that not isolated wavelengths but wavebands allow a more robust model design. This aspect should be taken into account when crossing individuals. We propose three crossover operators specifically designed for calibration with near-infrared spectral data, based on a pseudo-random two-point crossover, where the first point is chosen randomly, and the selection of the second point is guided by problem-specific information. We compare their performance with that of state-of-the-art operators. We combine these new genetic algorithm-based variable selection designs with partial least squares regression and fuzzy systems based calibration.Our benchmark consists of two real-world high-dimensional data sets, corresponding to polyetheracrylat, where hydroxyl number, viscosity, and acidity are on-line monitored; and melamine resin production, where the chilling point (CP) is considered in order to regulate the condensation. We show that designed operators promote wavebands selection, achieve better-quality solutions, and converge faster and smoother than state-of-the-art operators. © 2014 John Wiley & Sons, Ltd.


Cernuda C.,Johannes Kepler University | Lughofer E.,Johannes Kepler University | Mayr G.,Kompetenzzentrum Holz GmbH | Roder T.,Lenzing AG | And 3 more authors.
Chemometrics and Intelligent Laboratory Systems | Year: 2014

In viscose production, it is important to monitor three process parameters as part of the spin bath in order to assure a high quality of the final product: the concentrations of H2SO4, Na2SO4 and ZnSO4. NIR-spectroscopy is a fast analytical method applicable to conditions of industrial production and is capable of determining those concentrations. The collective composition of the spin bath varies in the industrial process, which implies changes in the matrix of the aforementioned analytes. Thus, conventional static chemometric models, which are trained based on collected calibration spectra from Fourier transform near infrared (FT-NIR) measurements, show a quite imprecise behavior when predicting the concentrations of new on-line data. In this paper, we are presenting a methodology which is able to cope with on-line self-calibration and -adaptation demands in order to compensate high system dynamics, reflected in conceptual changes in the mappings between NIR spectra and target concentrations. The methodology includes intelligent strategies for actively selecting those samples which should be accumulated into and excluded from the current data window in order to optimize the generalization performance of calibration models (thus termed as incremental and decremental active learning stages) while keeping the number of update cycles (and thus required target measurements) as low as possible. This follows the company requirements in terms of necessary cost reduction. Experiments on real-world data streams from viscose production process show that the new self-calibration methods are able to significantly reduce the number of update cycles while still keeping the predictive quality of the calibration models high (below 5% errors) for H2SO4 and Na2SO4. Incremental active learning is able to smoothen and improve the overall quality of the predictions, while decremental active learning achieves a lower number of medium to large prediction errors. © 2014 Elsevier B.V.


Cernuda C.,Johannes Kepler University | Lughofer E.,Johannes Kepler University | Marzinger Wolfgang W.,I RED Infrarot Systeme GmbH | Kasberger J.,Recendt GmbH
Chemometrics and Intelligent Laboratory Systems | Year: 2011

In polyetheracrylat (PEA) production, it is important to monitor three process parameters in order to assure a high quality of the final product: hydroxyl (OH) number, viscosity and acidity (acid number). Due to the high resolution and high sensitivity, it has been shown in the past that the Fourier transform near infrared (FTNIR) process spectrum measurements can be used to obtain spectra with precise content information about these process parameters. In order to perform an automatic supervision and to reduce the (off-line, laboratory) analysis effort of experts and operators of these substances, chemometric quantification models have to be used. In this paper, we investigate the usage of a specific type of fuzzy systems, so-called Takagi-Sugeno fuzzy systems, for calibrating the chemometric models. This type of model architecture supports the usage of piecewise local linear predictors, being able to model flexibly different degrees of non-linearities implicitly contained in the mapping between NIR spectra and reference values. The training of these models is conducted by an evolving clustering method (adding new local linear models on demand) and a local (weighted) least squares estimation of the consequent parameters, and connected with a wavelength (dimensionality) reduction mechanism. Results on a concrete data set show that it can outperform state-of-the-art calibration methods as well as support vector regression as alternative non-linear model. © 2011 Elsevier B.V.


Cernuda C.,Johannes Kepler University | Lughofer E.,Johannes Kepler University | Suppan L.,Kompetenzzentrum Holz GmbH | Roder T.,Lenzing AG | And 4 more authors.
Analytica Chimica Acta | Year: 2012

In viscose production, it is important to monitor three process parameters in order to assure a high quality of the final product: the concentrations of H 2SO 4, Na 2SO 4 and Z nSO 4. During on-line production these process parameters usually show a quite high dynamics depending on the fiber type that is produced. Thus, conventional chemometric models, which are trained based on collected calibration spectra from Fourier transform near infrared (FT-NIR) measurements and kept fixed during the whole life-time of the on-line process, show a quite imprecise and unreliable behavior when predicting the concentrations of new on-line data. In this paper, we are demonstrating evolving chemometric models which are able to adapt automatically to varying process dynamics by updating their inner structures and parameters in a single-pass incremental manner. These models exploit the Takagi-Sugeno fuzzy model architecture, being able to model flexibly different degrees of non-linearities implicitly contained in the mapping between near infrared spectra (NIR) and reference values. Updating the inner structures is achieved by moving the position of already existing local regions and by evolving (increasing non-linearity) or merging (decreasing non-linearity) new local linear predictors on demand, which are guided by distance-based and similarity criteria. Gradual forgetting mechanisms may be integrated in order to out-date older learned relations and to account for more flexibility of the models. The results show that our approach is able to overcome the huge prediction errors produced by various state-of-the-art chemometric models. It achieves a high correlation between observed and predicted target values in the range of [0.95,0.98] over a 3 months period while keeping the relative error below the reference error value of 3%. In contrast, the off-line techniques achieved correlations below 0.5, ten times higher error rates and the more deteriorate, the more time passes by. © 2012 Elsevier B.V.


Cernuda C.,Johannes Kepler University | Lughofer E.,Johannes Kepler University | Suppan L.,Kompetenzzentrum Holz GmbH | Roder T.,Lenzing AG | And 4 more authors.
Communications in Computer and Information Science | Year: 2012

In viscose production, it is important to monitor three process parameters as part of the spin-bath in order to assure a high quality of the final product: the concentrations of H 2 SO 4, Na 2 SO 4 and ZnSO 4. During on-line production these process parameters usually show a quite high dynamics depending on the fibre type that is produced. Thus, conventional chemometric models, kept fixed during the whole life-time of the on-line process, show a quite imprecise and unreliable behavior when predicting the concentrations of new on-line data. In this paper, we are demonstrating evolving chemometric models based on TS fuzzy systems architecture, which are able to adapt automatically to varying process dynamics by updating their inner structures and parameters in a single-pass incremental manner. Gradual forgetting mechanisms are necessary in order to out-date older learned relations and to account for more flexibility and spontaneity of the models. The results show that our dynamic approach is able to overcome the huge prediction errors produced by various state-of-the-art static chemometric models, which could be verified on data recorded on-line over a three months period. © 2012 Springer-Verlag Berlin Heidelberg.


Berer T.,Research Center for Non Destructive Testing Gmb | Berer T.,Christian Doppler Laboratory | Brandstetter M.,Research Center for Non Destructive Testing Gmb | Hochreiner A.,Research Center for Non Destructive Testing Gmb | And 5 more authors.
Optics Letters | Year: 2015

We demonstrate non-contact remote photoacoustic spectroscopy in the mid-infrared region. A room-temperatureoperated pulsed external-cavity quantum cascade laser is used to excite photoacoustic waves within a semitransparent sample. The ultrasonic waves are detected remotely on the opposite side of the sample using a fiber-optic Mach- Zehnder interferometer, thereby avoiding problems associated with acoustic attenuation in air. We present the theoretical background of the proposed technique and demonstrate measurements on a thin polystyrene film. The obtained absorption spectrum in the region of 1030-1230 cm-1 is compared to a spectrum obtained by attenuated total reflection, showing reasonable agreement. © 2015 Optical Society of America.


Witschnigg A.,University of Leoben | Laske S.,University of Leoben | Kracalik M.,University of Leoben | Feuchter M.,University of Leoben | And 6 more authors.
Journal of Applied Polymer Science | Year: 2010

The morphology of polymer nanocomposites is usually characterized by various methods like X-ray diffraction (XRD) or transmission electron microscopy (TEM). In this work, a new approach for characterizing nanocomposites is developed: the results of small angle x-ray scattering, on-line extensional rheometry (level of melt strength) and Young's modulus out of tensile test are correlated with those of near infrared (NIR) spectroscopy. The disadvantages of the common characterization methods are high costs and very time consuming sample preparation and testing. In contrast, NIR spectroscopy has the advantage to be measured inline and in real time directly in the melt. The results were obtained for different aggregate states (NIR spectroscopy and on-line rheotens test in melt state, tensile test, and XRD in solid state). Therefore, important factors like crystallization could not be considered. Nevertheless, this work demonstrates that the NIR-technology is perfectly suitable for quantitative in-line characterization. The results show that, by the installation of a NIR spectrometer on a nanocomposite-processing compounder, a powerful instrument for quality control and optimization of compounding process, in terms of increased and constant quality, is available. © 2010 Wiley Periodicals, Inc.

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