TY - GEN A1 - Liehr, Sascha T1 - ANNforPAT - Artificial Neural Networks for Process Analytical Technology N2 - This code accompanies the paper "Artificial neural networks for quantitative online NMR spectroscopy" published in Analytical and Bioanalytical Chemistry (2020). KW - Artificial neural networks KW - Automation KW - Online NMR spectroscopy KW - Process industry KW - Real-time process monitoring PY - 2020 UR - https://github.com/BAMresearch/ANNforPAT PB - GitHub CY - San Francisco AN - OPUS4-54481 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Liehr, Sascha A1 - Münzenberger, Sven A1 - Krebber, Katerina T1 - Wavelength-scanning distributed acoustic sensing for structural monitoring and seismic applications N2 - We introduce wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) for dynamic vibration sensing along optical fibers. The method is based on spectral shift computation from Rayleigh backscatter spectra. Artificial neural networks (ANNs) are used for fast and high-resolution strain computation from raw measurement data. The applicability of the method is demonstrated for vibration monitoring of a reinforced concrete bridge. We demonstrate another application example for quasi-static and dynamic measurement of ground deformation and surface wave propagation along a dark fiber in a telecommunication cable. T2 - 7th International Symposium on Sensor Science CY - Napoli, Italy DA - 09.05.2019 KW - Optical fiber sensor KW - Distributed acoustic sensor (DAS) KW - Optical time domain reflectometry KW - Rayleigh scattering KW - Artificial neural networks KW - Structural health monitoring KW - Seismic measurement PY - 2019 AN - OPUS4-48774 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Liehr, Sascha A1 - Münzenberger, Sven A1 - Krebber, Katerina T1 - Wavelength-scanning distributed acoustic sensing for structural monitoring and seismic applications T2 - Proceedings of International Symposium on Sensor Science N2 - We introduce wavelength-scanning coherent optical time domain reflectometry (WS-COTDR) for dynamic vibration sensing along optical fibers. The method is based on spectral shift computation from Rayleigh backscatter spectra. Artificial neural networks (ANNs) are used for fast and high-resolution strain computation from raw measurement data. The applicability of the method is demonstrated for vibration monitoring of a reinforced concrete bridge. We demonstrate another application example for quasi-static and dynamic measurement of ground deformation and surface wave propagation along a dark fiber in a telecommunication cable. T2 - 7th International Symposium on Sensor Science CY - Napoli, Italy DA - 09.05.2019 KW - Optical fiber sensor KW - Distributed acoustic sensor (DAS) KW - Optical time domain reflectometry KW - Rayleigh scattering KW - Artificial neural networks KW - Structural health monitoring KW - Seismic measurement PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-487733 DO - https://doi.org/10.3390/proceedings2019015030 SN - 2504-3900 VL - 15 SP - Paper 30, 1 EP - 5 PB - MDPI CY - Basel AN - OPUS4-48773 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Greco, A. A1 - Starostin, V. A1 - Karapanagiotis, C. A1 - Hinderhofer, A. A1 - Gerlach, A. A1 - Pithan, L. A1 - Liehr, Sascha A1 - Schreiber, Frank A1 - Kowarik, Stefan T1 - Fast fitting of reflectivity data of growing thin films using neural networks JF - Journal of Applied Crystallography N2 - X-ray reflectivity (XRR) is a powerful and popular scattering technique that can give valuable insight into the growth behavior of thin films. This study Shows how a simple artificial neural network model can be used to determine the thickness, roughness and density of thin films of different organic semiconductors [diindenoperylene, copper(II) phthalocyanine and alpha-sexithiophene] on silica from their XRR data with millisecond computation time and with minimal user input or a priori knowledge. For a large experimental data set of 372 XRR curves, it is shown that a simple fully connected model can provide good results with a mean absolute percentage error of 8–18% when compared with the results obtained by a genetic least mean squares fit using the classical Parratt formalism. Furthermore, current drawbacks and prospects for improvement are discussed. KW - Artificial neural networks KW - X-ray reflectivity PY - 2019 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-498997 DO - https://doi.org/10.1107/S1600576719013311 SN - 1600-5767 VL - 52 SP - 1342 EP - 1347 PB - Wiley AN - OPUS4-49899 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Liehr, Sascha T1 - Real-time dynamic strain sensing in optical fibers using artificial neural networks N2 - The use of artificial neural networks (ANNs) is demonstrated for efficient real-time data processing in optical fiber sensing applications. Using ANN-based algorithms, two orders of magnitude improved computation time and improved measurement resolution is achieved for distributed strain sensing using the wavelength-scanning coherent optical time domain reflectometry technique. T2 - WORKSHOP - Machine learning in optical analytics CY - Berlin, Germany DA - 13.11.2019 KW - Artificial neural networks KW - Distributed acoustic sensing (DAS) KW - Distributed vibration sensing KW - Signal processing PY - 2019 AN - OPUS4-49869 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kern, Simon A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Müller, S. A1 - Maiwald, Michael A1 - Kowarik, Stefan T1 - Artificial neural networks for quantitative online NMR spectroscopy JF - Analytical and bioanalytical chemistry N2 - Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data. KW - Online NMR spectroscopy KW - Real-time process monitoring KW - Artificial neural networks KW - Automation KW - Process industry PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-507508 DO - https://doi.org/10.1007/s00216-020-02687-5 SN - 1618-2642 VL - 412 IS - 18 SP - 4447 EP - 4459 PB - Springer CY - Berlin AN - OPUS4-50750 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Kern, Simon A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Müller, S. A1 - Maiwald, Michael A1 - Kowarik, Stefan T1 - Training data of quantitative online NMR spectroscopy for artificial neural networks N2 - Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). 1H spectra (43 MHz) were recorded as single scans. Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (i) Training data based on combinations of measured pure component spectra and (ii) Training data based on a spectral model. Synthetic low-field NMR spectra First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum. Xi (“pure component spectra dataset”) Xii (“spectral model dataset”) Experimental low-field NMR spectra from MNDPA-Synthesis This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included. KW - NMR spectroscopy KW - Real-time process monitoring KW - Artificial neural networks KW - Online NMR spectroscopy KW - Automation KW - Process industry PY - 2020 DO - https://doi.org/10.5281/zenodo.3677139 PB - Zenodo CY - Geneva AN - OPUS4-50456 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -