Dr. rer. nat.

Rüping, Stefan

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  • Publication
    A review of machine learning for the optimization of production processes
    ( 2019)
    Weichert, Dorina
    ;
    Link, Patrick
    ;
    Stoll, Anke
    ;
    Rüping, Stefan
    ;
    Ihlenfeldt, Steffen
    ;
    Wrobel, Stefan
    Due to the advances in the digitalization process of the manufacturing industry and the resulting available data, there is tremendous progress and large interest in integrating machine learning and optimization methods on the shop floor in order to improve production processes. Additionally, a shortage of resources leads to increasing acceptance of new approaches, such as machine learning to save energy, time, and resources, and avoid waste. After describing possible occurring data types in the manufacturing world, this study covers the majority of relevant literature from 2008 to 2018 dealing with machine learning and optimization approaches for product quality or process improvement in the manufacturing industry. The review shows that there is hardly any correlation between the used data, the amount of data, the machine learning algorithms, the used optimizers, and the respective problem from the production. The detailed correlations between these criteria and the recent progress made in this area as well as the issues that are still unsolved are discussed in this paper.
  • Publication
    E2mC: Improving Emergency Management Service Practice through Social Media and Crowdsourcing Analysis in Near Real Time
    ( 2017)
    Havas, C.
    ;
    Resch, B.
    ;
    Francalanci, C.
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    Pernici, B.
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    Scalia, G.
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    Fernandez-Marquez, J.L.
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    Achte, T. Van
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    Zeug, G.
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    Mondardini, M.R.R.
    ;
    Grandoni, D.
    ;
    Kirsch, Birgit
    ;
    Kalas, M.
    ;
    Lorini, V.
    ;
    Rüping, Stefan
    In the first hours of a disaster, up-to-date information about the area of interest is crucial for effective disaster management. However, due to the delay induced by collecting and analysing satellite imagery, disaster management systems like the Copernicus Emergency Management Service (EMS) are currently not able to provide information products until up to 48-72 h after a disaster event has occurred. While satellite imagery is still a valuable source for disaster management, information products can be improved through complementing them with user-generated data like social media posts or crowdsourced data. The advantage of these new kinds of data is that they are continuously produced in a timely fashion because users actively participate throughout an event and share related information. The research project Evolution of Emergency Copernicus services (E2mC) aims to integrate these novel data into a new EMS service component called Witness, which is presented in this paper. Like this, the timeliness and accuracy of geospatial information products provided to civil protection authorities can be improved through leveraging user-generated data. This paper sketches the developed system architecture, describes applicable scenarios and presents several preliminary case studies, providing evidence that the scientific and operational goals have been achieved.