Veröffentlichungen | Publications

Document date: April 7, 2021

Beiträge in wissenschaftlichen Fachjournalen

  • Constanze Schwan and Wolfram Schenck. A three-step model for the detection of stable grasp points with machine learning. Integrated Computer-Aided Engineering, in press.
  • Zafran Hussain Shah, Marcel Müller, Tung-Cheng Wang, Philip Maurice Scheidig, Axel Schneider, Mark Schüttpelz, Thomas Huser, and Wolfram Schenck. Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images. Photonics Research, in press.
    (DOI: 10.1364/PRJ.416437). 
  • Nico Migenda, Ralf Möller, and Wolfram Schenck. Adaptive dimensionality reduction for neural network-based online principal component analysis. PLOS ONE, 16(3):1–32, 2021.
    (DOI: 10.1371/journal.pone.0248896).
  • Alaa Tharwat and Wolfram Schenck. A conceptual and practical comparison of PSO-style optimization algorithms. Expert Systems with Applications, 167:114430, 2021.
    (DOI: https://doi.org/10.1016/j.eswa.2020.114430).
  • Alaa Tharwat and Thomas Gabel. Parameters optimization of support vector machines for imbalanced data using social ski driver algorithm. Neural Computing and Applications, 32: 6925–6938, 2020.
  • Alaa Tharwat and Wolfram Schenck. Balancing Exploration and Exploitation: A novel active learner for imbalanced data. Knowledge-Based Systems, 210:106500, 2020.
    (DOI: 10.1016/j.knosys.2020.106500).
  • Tim Voigt, Martin Kohlhase, and Armin Peter. Bestandsanlagen in der smarten Produktion, Integrationsstrategien anhand eines Praxisbeispiels. atp magazin, 04(04):62–69, 2020.
  • Alaa Tharwat. Parameter investigation of support vector machine classifier with kernel functions. Knowledge and Information Systems, 61(3):1269–1302, 2019.
  • Alaa Tharwat, Mohamed Elhoseny, Aboul Ella Hassanien, Thomas Gabel, and Arun Kumar. Intelligent bézier curve-based path planning model using chaotic particle swarm optimization algorithm.
    Cluster Computing, 22(2):4745–4766, 2019.
  • Alaa Tharwat and Aboul Ella Hassanien. Quantum-behaved particle swarm optimization for parameter optimization of support vector machine. Journal of Classification, 36(3):576–598, 2019.
  • Susanne Kunkel and Wolfram Schenck. The NEST dry-run mode: Efficient dynamic analysis of neuronal network simulation code. Frontiers in Neuroinformatics, 11:article 40, 2017.
    (DOI: 10.3389/fninf.2017.00040).
  • Wolfram Schenck, Salem El Sayed, Maciej Foszczynski, Wilhelm Homberg, and Dirk Pleiter. Evaluation and performance modeling of a burst buffer solution. ACM SIGOPS Operating Systems Review, 50(1):12–26, 2017a. (DOI: 10.1145/3041710.3041714).
  • Wolfram Schenck, Michael Horst, Tim Tiedemann, Sergius Gaulik, and Ralf Möller. Comparing parallel hardware architectures for visually guided robot navigation. Concurrency and Computation: Practice and Experience, 29(4):article e3833, 2017b. (DOI: 10.1002/cpe.3833).

Zeitschriftenbeiträge

  • Stefan Berlik. Maschinelles Lernen: Ein Blick hinter die Kulissen. KINOTE, 2019(1):6–11,2019.
  • Martin Kohlhase. Instandhaltung und Wartung – Wenn die Maschine Alarm schlägt.
    markt & wirtschaft, pages 21–22, October 2019.

 

Konferenzbeiträge (begutachtet)

  • Constanze Schwan and Wolfram Schenck. Design of interpretable machine learning tasks for the application to industrial order picking. In Kommunikation und Bildverarbeitung in der Automation – Ausgewählte Beiträge der Jahreskolloquien KommA und BVAu 2020. Springer, in press.
  • Luca Steinmann, Nico Migenda, Tim Voigt, Martin Kohlhase, and Wolfram Schenck. Variational Autoencoder based Novelty Detection for Real-World Time Series. In 2nd International conference on Industrial Engineering and Artificial Intelligence (IEAI 2021). IEEE, in press.
  • Tim Voigt, Marvin Schöne, Martin Kohlhase, Oliver Nelles, and Martin Kuhn. Space-Filling Designs for Experiments with Assembled Products. In 2nd International conference on Industrial Engineering and Artificial Intelligence (IEAI 2021). IEEE, in press.
  • Jan Cirullies and Christian Schwede. On-demand Shared Digital Twins – An Information Architectural Model to Create Transparency in Collaborative Supply Networks. In Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS 2021). A Virtual AIS Conference, pages 1675–1684, 2021.
  • Stephan Godt and Martin Kohlhase. Identifikation eines nichtlinearen dynamischen Mehrgrößensystems mit rekurrenten neuronalen Netzen im Vergleich zu lokal-affinen Zustandsraummodellen. In Proceedings – 30. Workshop Computational Intelligence, pages 159–179. KIT Scientific Publishing, Karlsruhe, 2020.
  • Nico Migenda and Wolfram Schenck. Adaptive Dimensionality Reduction for Local Principal Component Analysis. In 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2020), pages 1579–1586. IEEE, 2020.
  • David Pelkmann, Alaa Tharwat, and Wolfram Schenck. How to label? Combining experts’ knowledge for German text classification. In 2020 7th Swiss Conference on Data Science (SDS), pages 61–62. IEEE, 2020. (DOI: 10.1109/SDS49233.2020.00023).
  • Marvin Schöne and Martin Kohlhase. Least Squares Approach for Multivariate Split Selection in Regression Trees. In Cesar Analide, Paulo Novais, David Camacho, and Hujun Yin, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2020, pages 41–50, Cham, 2020a. Springer International Publishing. (DOI: 10.1007/978-3-030-62362-3_5).
  • Marvin Schöne and Martin Kohlhase. Least-Squares-Based Construction Algorithm for Oblique and Mixed Regression Trees. In Proceedings – 30. Workshop Computational Intelligence, pages 207 – 227. KIT Scientific Publishing, Karlsruhe, 2020b.(DOI: 10.5445/KSP/1000124139).
  • Constanze Schwan and Wolfram Schenck. Visual Movement Prediction for Stable Grasp Point Detection. In Lazaros Liadis, Plamen Parvanov Angelov, Chrisina Jayne, and Elias Pimenidis, editors, Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference, pages 70–81, Cham, 2020. Springer International Publishing. ISBN 978-3-030-48791-1.
    (DOI: 10.1007/978-3-030-48791-1_5).
  • Tim Voigt, Martin Kohlhase, and Oliver Nelles. Incremental Latin Hypercube Additive Design for LOLIMOT. In 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2020), pages 1602–1609. IEEE, 2020.
  • Stephan Godt and Martin Kohlhase. Data Mining im geschlossenen Regelkreis basierend auf adaptiven Kennfeldern mit integriertem Anti-Windup-Mechanismus. In Proceedings – 29. Workshop Computational Intelligence, pages 51–71. KIT Scientific Publishing, Karlsruhe, 2019.
  • Nico Migenda, Ralf Möller, and Wolfram Schenck. Adaptive dimensionality adjustment for online “Principal Component Analysis”. In Hujun Yin, David Camacho, Peter Tino, Antonio J. Tallón-Ballesteros, Ronaldo Menezes, and Richard Allmendinger, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2019, number 11871 in Lecture Notes in Computer Science, pages 76–84. Springer International Publishing, Cham, 2019. ISBN 978-3-030-33607-3.
    (DOI: 10.1007/978-3-030-33607-3_9).
  • Tim Voigt, Martin Kohlhase, and Oliver Nelles. Inkrementelle Modellbildung von statischen Prozessen auf Basis von Latin Hypercube Designs. In Proceedings – 29. Workshop Computational Intelligence, pages 267–288. KIT Scientific Publishing, Karlsruhe, 2019.
  • Kevin Grünberg andWolfram Schenck. A Case Study on Benchmarking IoT Cloud Services. In Min Luo and Liang-Jie Zhang, editors, Cloud Computing — CLOUD 2018, number 10967 in Lecture Notes in Computer Science, pages 398–406. Springer International Publishing, Cham, 2018.
    ISBN 978-3-319-94295-7. (DOI: 10.1007/978-3-319-94295-7_28).
  • Benjamin Korth, Christian Schwede, and Markus Zajac. Simulation-ready digital twin for realtime management of logistics systems. In 2018 IEEE International Conference on Big Data (Big Data), pages 4194–4201, Seattle, WA, USA, 2018. IEEE.
  • Tim Voigt and Martin Kohlhase. Schätzung von datenbasierten lokal-linearen Modellen auf der Grundlage von LOLIMOT für den systematischen Entwurf von lokal-linearen Zustandsreglern. In Proceedings – 28. Workshop Computational Intelligence, pages 93–111. KIT Scientific Publishing, Karlsruhe, 2018.

Technische Reports und Preprints

  • Zafran Hussain Shah, Marcel Müller, Tung-Cheng Wang, Philip Maurice Scheidig, Axel Schneider, Mark Schüttpelz, Thomas Huser, and Wolfram Schenck. Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images.
    bioRxiv, preprint:2020.10.27.352633, 2020. (DOI: 10.1101/2020.10.27.352633).

 

Projektberichte

  • Stephan Godt and Wolfram Schenck. Studie für Aufbau und Betrieb eines Anwendungszentrums als zukunftsfähige Einrichtung für den Wissens- und Technologietransfer an Unternehmen. Project report, CfADS, Bielefeld University of Applied Sciences, Bielefeld, 2018.
    URL https://www.fh-bielefeld.de/ium/cfads.