Home

Research Interests

  • Outlier Detection
  • Predictive Maintenance
  • Data Visualization

 

Projects

pdmInsight

DBISfactory

Frankfurt Developer Day 2017:
Apache Spark algorithms in a Raspberry Cluster

IoT Conference, Munich 2017
An MQTT based Architecture for Neural Networks

| 2020 | 2019 | 2018 | 2017 | 2016 |

2020

Buck, Timo (2020) Deep Learning in the Context of Inventory Valuation in the Pharmaceutical Industry. Master thesis, Ulm University. file
Henkel, Fabian (2020) Generische UI-Konzepte im Web-Frontend. Master thesis, Institute of Databases and Informations Systems. file

2019

Allgaier, Johannes (2019) Machine learning under concept drift for industrial data using Python. Master thesis, Institute of Databases and Informations Systems. file
Berroth, Kai-Uwe (2019) Evaluation von Vorhersagemodellen auf Basis von UN Millenniumszielen. Master thesis, Institute of Databases and Informations Systems. file
Wochele, Joel (2019) Evaluation von Angular Elements im Kontext von Produktionsleitsystemen mittels einer Microservice-Architektur. Master thesis, Ulm University. file

2018

Hesse, Lukas (2018) Modellgetriebene Softwareentwicklung im Umfeld von Manufacturing Execution Systems. Master thesis, Ulm University. file
Hunt, Alexander (2018) Vision Enhancement for Autonomous Driving under Adverse Weather Conditions using Generative Adversarial Nets. Master thesis, Ulm University. file
Tong, Yu (2018) Path Recognition with DTW in a Distributed Environment. Master thesis, Ulm University. file
Väth, Thomas (2018) Exploring Temporal Data in a Mixed-Reality Application. Bachelor thesis, Ulm University. file

2017

Grabiec, Sebastian (2017) Developing a client-specific workflow for Predictive Maintenance. Master thesis, Ulm University. file
Grausz, Krisztián (2017) Log Analyzer for IoT Applications. Master thesis, Ulm University. file
Kuhaupt, Nicolas (2017) Conception And Analysis Of A Raspberry Pi Cluster With Apache Spark. Master thesis, Ulm University. file
Salonikidis, Georgios (2017) Minimization of Redundant Make-To-Stock Production in a Dental Factory: An Integer Linear Programming Approach. Bachelor thesis, Ulm University. file
Schwarz, Holger (2017) Evaluierung neuronaler Netze auf Maschinendatenbasis. Master thesis, Ulm University. file

2016

Ipekbayrak, Gözde (2016) Using NoSQL Databases in the Context of Manufacturing Execution Systems. Master thesis, Ulm University. file
Reutlinger, Jürgen (2016) Einsatz von Prozess Management Technologie in Manufacturing Execution Systems. Master thesis, Ulm University. file
Wagner, Eugen (2016) Microservices as a Manufacturing Execution System Architecture. Master thesis, Ulm University. file

Dr. Burkhard Hoppenstedt

Burkhard Hoppenstedt studied Computer Science (i.e., Media Informatics) at Ulm University and at the NTNU in Trondheim (Norway). He graduated with a master's degree in 2016. For his master thesis, his focus was on possibilities of production control in the industry with the so-called OEE index and methods of Predictive Maintenance.

During his external dissertation, he will deal with models and approaches in the context of Predictive Analytics (Industry 4.0).

In his leisure time, he travels a lot, does improvisation theatre and enjoys going to the cinema.

| 2020 | 2019 | 2018 | 2017 |

2020

Hoppenstedt, Burkhard and Reichert, Manfred and El-Khawaga, Ghada and Winter, Karl-Michael and Pryss, Rüdiger (2020) Detecting Production Phases Based on Sensor Values using 1D-CNNs. arXiv. file
Hoppenstedt, Burkhard and Probst, Thomas and Reichert, Manfred and Schlee, Winfried and Kammerer, Klaus and Spiliopoulou, Myra and Schobel, Johannes and Winter, Michael and Felnhofer, Anna and Kothgassner, Oswald and Pryss, Rüdiger (2020) Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios. Journal of Visualized Experiments (JoVE), 164(e61349), J. Vis. Exp., 10.3791/61349.
Kammerer, Klaus and Pryss, Rüdiger and Hoppenstedt, Burkhard and Sommer, Kevin and Reichert, Manfred (2020) Process-Driven and Flow-Based Processing of Industrial Sensor Data. Sensors, 20(18), MDPI, 10.3390/s20185245. file
Pryss, Rüdiger and Schlee, Winfried and Hoppenstedt, Burkhard and Reichert, Manfred and Spiliopoulou, Myra and Langguth, Berthold and Breitmayer, Marius and Probst, Thomas (2020) Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study. J Med Internet Res, 22(6), JMIR. file

2019

Hoppenstedt, Burkhard and Reichert, Manfred and Kammerer, Klaus and Spiliopoulou, Myra and Pryss, Rüdiger (2019) Towards a Hierarchical Approach for Outlier Detection in Industrial Production Settings. In: EDBT/ICDT 2019 Workshops, Lisbon, 26 March 2019, CEUR Workshop Proceedings 2322, CEUR-WS.org. file
Hoppenstedt, Burkhard and Kammerer, Klaus and Reichert, Manfred and Spiliopoulou, Myra and Pryss, Rüdiger (2019) Convolutional Neural Networks for Image Recognition in Mixed Reality Using Voice Command Labeling. In: 6th International Conference on Augmented Reality, Virtual Reality and Computer Graphics (SALENTO AVR 2019), Santa Maria al Bagno, Italy, June 24-27, 2019, Lecture Notes in Computer Science 11614, Springer, pp. 63-70. file
Hoppenstedt, Burkhard and Schmid, Michael and Kammerer, Klaus and Scholta, Joachim and Reichert, Manfred and Pryss, Rüdiger (2019) Analysis of Fuel Cells Utilizing Mixed Reality and IoT Achievements. In: 6th International Conference on Augmented Reality, Virtual Reality and Computer Graphics (SALENTO AVR 2019), Santa Maria al Bagno, Italy, June 24-27, 2019, Lecture Notes in Computer Science 11614, Springer, pp. 371-378. file
Hoppenstedt, Burkhard and Witte, Thomas and Ruof, Jona and Kammerer, Klaus and Tichy, Matthias and Reichert, Manfred and Pryss, Rüdiger (2019) Debugging Quadrocopter Trajectories in Mixed Reality. In: 6th International Conference on Augmented Reality, Virtual Reality and Computer Graphics (SALENTO AVR 2019), Santa Maria al Bagno, Italy, June 24-27, 2019, Lecture Notes in Computer Science 11614, Springer, pp. 43-50. file
Hoppenstedt, Burkhard and Probst, Thomas and Reichert, Manfred and Schlee, Winfried and Kammerer, Klaus and Spiliopoulou, Myra and Schobel, Johannes and Winter, Michael and Felnhofer, Anna and Kothgassner, Oswald and Pryss, Rüdiger (2019) Applicability of Immersive Analytics in Mixed Reality: Usability Study . IEEE Access, Vol. 7, pp. 71921-71932, 10.1109/ACCESS.2019.2919162. file
Hoppenstedt, Burkhard and Reichert, Manfred and Kammerer, Klaus and Probst, Thomas and Schlee, Winfried and Spiliopoulou, Myra and Pryss, Rüdiger (2019) Dimensionality Reduction and Subspace Clustering in Mixed Reality for Condition Monitoring of High-Dimensional Production Data. Sensors, MDPI, Vol. 19, pp. 3303, 10.3390/s19183903. file
Kammerer, Klaus and Hoppenstedt, Burkhard and Pryss, Rüdiger and Stökler, Steffen and Allgaier, Johannes and Reichert, Manfred (2019) Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings. Sensors, 19(24), MDPI, 10.3390/s19245370. file
Pryss, Rüdiger and Schlee, Winfried and Reichert, Manfred and Kurthen, Ira and Giroud, Nathalie and Jagoda, Laura and Neuschwander, Pia and Meyer, Martin and Neff, Patrick and Schobel, Johannes and Hoppenstedt, Burkhard and Spiliopoulou, Myra and Langguth, Berthold and Probst, Thomas (2019) Ecological Momentary Assessment based Differences between Android and iOS Users of the TrackYourHearing mHealth Crowdsensing Platform. In: 41st International Engineering in Medicine and Biology Conference, Berlin, Germany, July 23–27, 2019, IEEE Computer Society Press, pp. 3951-3955. file
Pryss, Rüdiger and John, Dennis and Reichert, Manfred and Hoppenstedt, Burkhard and Schmid, Lukas and Schlee, Winfried and Spiliopolou, Myra and Schobel, Johannes and Kraft, Robin and Schickler, Marc and Langguth, Berthold and Probst, Thomas (2019) Machine Learning Findings on Geospatial Data of Users from the TrackYourStress mHealth Crowdsensing Platform. In: IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI 2019), Los Angeles, California, USA, July 30 - August 1, 2019, IEEE Computer Society Press, pp. 350-355. file

2018

Hoppenstedt, Burkhard and Schneider, Christian and Pryss, Rüdiger and Schlee, Winfried and Probst, Thomas and Neff, Patrick and Simoes, Jorge and Treß, Alexander and Reichert, Manfred (2018) HOLOVIEW: Exploring Patient Data in Mixed Reality. In: TRI / TINNET Conference 2018 , Regensburg. file
Hoppenstedt, Burkhard and Pryss, Rüdiger and Stelzer, Birgit and Meyer-Brötz, Fabian and Kammerer, Klaus and Treß, Alexander and Reichert, Manfred (2018) Techniques and Emerging Trends for State of the Art Equipment Maintenance Systems - A Bibliometric Analysis . Applied Sciences, MDPI, Vol. 8, pp. 1-29, 10.3390/app8060916. file
Hoppenstedt, Burkhard and Reichert, Manfred and Schneider, Christian and Kammerer, Klaus and Schlee, Winfried and Probst, Thomas and Langguth, Berthold and Pryss, Rüdiger (2018) Exploring Dimensionality Reduction Effects in Mixed Reality for Analyzing Tinnitus Patient Data. In: 4th Int'l Conference of the Virtual and Augmented Reality in Education (VARE 2018), Budapest, Hungary, 17 - 19 September 2018, pp. 163-170. file
Hoppenstedt, Burkhard and Pryss, Rüdiger and Kammerer, Klaus and Reichert, Manfred (2018) CONSENSORS: A Neural Network Framework for Sensor Data Analysis. In: OTM 2018 Workshops, Valetta, Malta, October 22-26, LNCS 11231, Springer, pp. 196-200. file

2017

Hoppenstedt, Burkhard and Pryss, Rüdiger and Treß, Alexander and Biechele, Bernd and Reichert, Manfred (2017) Datengetriebene Module für Predictive Maintenance. ProductivITy, Vol. 22, pp. 21-23. file

Contact details

Burkhard Hoppenstedt

Research Assistant

Office: Building O27 - Room 5203
Consultation hours on appointment.

burkhard.hoppenstedt(at)uni-ulm.de

Phone:+49 731 50 24 136
Fax:+49 731 50 24 134