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Autor

  • Kowarik, Stefan (18)
  • Pithan, L. (5)
  • Liehr, Sascha (4)
  • Beyer, P. (2)
  • Bornemann-Pfeiffer, Martin (2)
  • Chruscicki, Sebastian (2)
  • Duva, G. (2)
  • Gerlach, A. (2)
  • Hinderhofer, A. (2)
  • Kern, Simon (2)
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Erscheinungsjahr

  • 2020 (4)
  • 2019 (7)
  • 2018 (2)
  • 2017 (5)

Dokumenttyp

  • Zeitschriftenartikel (9)
  • Vortrag (5)
  • Forschungsdatensatz (2)
  • Beitrag zu einem Tagungsband (1)
  • Posterpräsentation (1)

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  • Englisch (16)
  • Deutsch (2)

Referierte Publikation

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  • ja (9)

Schlagworte

  • Artificial neural networks (4)
  • Distributed acoustic sensing (3)
  • X-ray reflectivity (3)
  • Artificial Neural Networks (2)
  • Automation (2)
  • Distributed fiber optic sensing (2)
  • Faseroptische Sensorik (2)
  • Fiber optic sensing (2)
  • Online NMR Spectroscopy (2)
  • Process Industry (2)
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Organisationseinheit der BAM

  • 8 Zerstörungsfreie Prüfung (18)
  • 8.6 Faseroptische Sensorik (18)
  • 1 Analytische Chemie; Referenzmaterialien (2)
  • 1.4 Prozessanalytik (2)
  • 8.0 Abteilungsleitung und andere (1)

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Artificial neural networks for quantitative online NMR spectroscopy (2020)
Kern, Simon ; Liehr, Sascha ; Wander, Lukas ; Bornemann-Pfeiffer, Martin ; Müller, S. ; Maiwald, Michael ; Kowarik, Stefan
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.
Training Data of Quantitative Online NMR Spectroscopy for Artificial Neural Networks (2020)
Kern, Simon ; Liehr, Sascha ; Wander, Lukas ; Bornemann-Pfeiffer, Martin ; Müller, S. ; Maiwald, Michael ; Kowarik, Stefan
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.
kowarik-labs/AI-reflectivity: v0.1 (2019)
Kowarik, Stefan ; Pithan, L.
AI-reflectivity is a code based on artificial neural networks trained with simulated reflectivity data that quickly predicts film parameters from experimental X-ray reflectivity curves. This project has a common root with (ML-reflectivity)[https://github.com/schreiber-lab/ML-reflectivity] and evolved in parallel. Both are linked to the following publication: Fast Fitting of Reflectivity Data of Growing Thin Films Using Neural Networks A. Greco, V. Starostin, C. Karapanagiotis, A. Hinderhofer, A. Gerlach, L. Pithan, S. Liehr, F. Schreiber, S. Kowarik (2019). J. Appl. Cryst. For an online live demonstration using a pre-trained network have a look at github.
Fiber Optic Train Monitoring with Distributed Acoustic Sensing: Conventional and Neural Network Data Analysis (2020)
Kowarik, Stefan ; Hussels, Maria-Teresa ; Chruscicki, Sebastian ; Münzenberger, Sven ; Lämmerhirt, A. ; Pohl, P. ; Schubert, M.
Distributed acoustic sensing (DAS) over tens of kilometers of fiber optic cables is well-suited for monitoring extended railway infrastructures. As DAS produces large, noisy datasets, it is important to optimize algorithms for precise tracking of train position, speed, and the number of train cars, The purpose of this study is to compare different data analysis strategies and the resulting parameter uncertainties. We present data of an ICE 4 train of the Deutsche Bahn AG, which was recorded with a commercial DAS system. We localize the train signal in the data either along the temporal or spatial direction, and a similar velocity standard deviation of less than 5 km/h for a train moving at 160 km/h is found for both analysis methods, The data can be further enhanced by peak finding as well as faster and more flexible neural network algorithms. Then, individual noise peaks due to bogie clusters become visible and individual train cars can be counted. From the time between bogie signals, the velocity can also be determined with a lower standard deviation of 0.8 km/h, The analysis methods presented here will help to establish routines for near real-time Train tracking and train integrity analysis.
Fast fitting of reflectivity data of growing thin films using neural networks (2019)
Greco, A. ; Starostin, V. ; Karapanagiotis, C. ; Hinderhofer, A. ; Gerlach, A. ; Pithan, L. ; Liehr, Sascha ; Schreiber, Frank ; Kowarik, Stefan
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.
Smart materials and structures based on fiber optic sensing (2019)
Kowarik, Stefan
This talk gives an overview of basic techniques. Towards the end some applications of neural networks are discussed.
Train monitoring using distributed fiber optic acoustic sensing (2020)
Kowarik, Stefan ; Hicke, Konstantin ; Chruscicki, Sebastian ; Schukar, Marcus ; Breithaupt, Mathias ; Lämmerhirt, A. ; Pohl, P. ; Schubert, M.
We use distributed acoustic sensing to determine the velocity of trains from train vibration patterns using artificial neural network and conventional algorithms. The velocity uncertainty depends on track conditions, train type and velocity.
Artificial intelligence, the end of the world, and surface science (2017)
Kowarik, Stefan
Artificial intelligence (AI), machine learning, and neural networks have revolutionized fields such as self-driving cars or machine translation. Indeed, AI has progressed so far that scientists such as Stephen Hawking now fear that it may destroy humankind altogether. So, let’s get started and use AI in surface science. Here we train neural networks to use raw measurement data as input and immediately return the desired output parameters. We discuss the example of X-ray reflectivity measurements of ultrathin films, which contain reciprocal space information and must traditionally be fitted with dynamic scattering theory. Instead, we train the neural network with simulated X-ray data of multilayer structures and then apply it to measurement data. The neural network yields high accuracy results, is robust against noise, and performs significantly faster than fitting algorithms in real-time experiments. The presented neural network data analysis is becoming increasingly attractive, because free software has become very accessible and specialized computer chips (NPU) are currently being rolled out.
A novel 3D printed radial collimator for x-ray diffraction (2019)
Kowarik, Stefan ; Bogula, ; Boitano, ; Carla, ; Pithan, ; Schafer, ; Wilming, ; Zykov, ; Pithan,
We demonstrate the use of a 3D printed radial collimator in X-ray powder diffraction and surface sensitive grazing incidence X-ray diffraction. We find a significant improvement in the overall Signal to background ratio of up to 100 and a suppression of more than a factor 3⋅10⁵ for undesirable Bragg reflections generated by the X-ray “transparent” windows of the sample environment. The background reduction and the removal of the high intensity signals from the windows, which limit the detector’s dynamic range, enable significantly higher sensitivity in experiments within sample environments such as vacuum chambers and gas- or liquid-cells. Details of the additively manufactured steel collimator geometry, alignment strategies using X-ray fluorescence, and data analysis are also briefly discussed. The flexibility and affordability of 3D prints enable designs optimized for specific detectors and sample environments, without compromising the degrees of freedom of the diffractometer.
Fiber Optic sensing @BAM (2017)
Kowarik, Stefan
I will discuss fiber optic sensing principles at BAM. Overlapping areas of interes between our group and the Institut für Angewandte Photonik will be discussed.
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