Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Christopher Haubeck"'
Autor:
Abhishek Chakraborty, Alexander Fay, Christopher Haubeck, Udo Kelter, Winfried Lamersdorf, Christopher Pietsch
Publikováno v:
at - Automatisierungstechnik. 66:795-805
Understanding changes in a manufacturing system is of utmost importance to effectively manage its evolution. This article proposes a pattern-based approach for capturing and describing behavioral changes by integrating recent advantages in the fields
Publikováno v:
IFAC-PapersOnLine. 51:276-283
Industry 4.0 connects different machines and their modules to each other. Integrating already existing non-modular machines and establishing the required modularization in such a scenario requires a lot of time-consuming analysis. But Industry 4.0 al
Publikováno v:
Computer Science and Information Systems. 15:705-731
Anomaly detection is the process of identifying nonconforming behaviour. Many approaches from machine learning to statistical methods exist to detect behaviour that deviate from its norm. These non-conformances of specifications can stem from failure
Autor:
Kiana Busch, Robert Heinrich, Safa Bougouffa, Birgit Vogel-Heuser, Ralf Reussner, Suhyun Cha, Christopher Haubeck
Publikováno v:
Managed Software Evolution ISBN: 9783030134983
Managed Software Evolution
Managed Software Evolution
In this chapter we provide an overview on the demonstrators of the SPP1593’s case studies. To study evolution, it is important to collaborate by a joint research that supports sharing of knowledge and resources. In order to support joint research,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0af04ab6cc238a45ab7390f73e422f0
https://doi.org/10.1007/978-3-030-13499-0_4
https://doi.org/10.1007/978-3-030-13499-0_4
Autor:
Kurt Schneider, Daniel Strüber, Emre Taspolatoglu, Robert Heinrich, Michael Goedicke, Marco Konersmann, Christopher Haubeck, Jan Jürjens, Jens Bürger, Fabien Patrick Viertel, Winfried Lamersdorf, Alexander Fay, Ralf Reussner, Jan Ladiges
Publikováno v:
Managed Software Evolution ISBN: 9783030134983
Managed Software Evolution
Springer 207-253 (2019). doi:10.1007/978-3-030-13499-0_9
Managed Software Evolution
Springer 207-253 (2019). doi:10.1007/978-3-030-13499-0_9
Managed Software Evolution; Springer 207-253 (2019). doi:10.1007/978-3-030-13499-0_9
Published by Springer
Published by Springer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99edc7595c9685cd2a92f2e206de819b
Autor:
Lars Grunske, Mattias Ulbrich, Matthias Tichy, Gabriele Taentzer, Christopher Haubeck, Winfried Lamersdorf, Birgit Vogel-Heuser, Kiana Busch, Sandro Koch, Sinem Getir, Robert Heinrich, Abhishek Chakraborty, Stefan Kögel, Timo Kehrer, Safa Bougouffa, Alexander Fay, Udo Kelter, Jan Ladiges, Suhyun Cha
Publikováno v:
Managed Software Evolution ISBN: 9783030134983
Managed Software Evolution
Managed Software Evolution
Successful system evolution is dependent on knowledge about the system itself, its past and its present, as well as the environment of the system. This chapter presents several approaches to automate the acquisition of knowledge about the system’s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4057ee8f9dfdb90727bc888d915d18b6
https://doi.org/10.1007/978-3-030-13499-0_10
https://doi.org/10.1007/978-3-030-13499-0_10
Publikováno v:
Service-Oriented Computing – ICSOC 2017 Workshops ISBN: 9783319917634
Cyber Physical Systems (CPSs) are both software and hardware intense systems which are integrated into the digital world. An increasingly relevant application area for CPSs are software-driven industrial Production Automation Systems. A major driver
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e1eee2c4f45a91041be45b2c5859cf53
https://doi.org/10.1007/978-3-319-91764-1_22
https://doi.org/10.1007/978-3-319-91764-1_22
Publikováno v:
Intelligent Distributed Computing XI ISBN: 9783319663784
IDC
IDC
Anomaly Detection (AD) in distributed cloud systems is the process of identifying unexpected (i.e. anomalous) behaviour. Many approaches from machine learning to statistical methods exist to detect anomalous data instances. However, no generic soluti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d4a167311a0ef24c4407e3ed8fbcc65
https://doi.org/10.1007/978-3-319-66379-1_22
https://doi.org/10.1007/978-3-319-66379-1_22
Autor:
Jan Ladiges, Christopher Haubeck, Winfried Lamersdorf, Abhishek Chakraborty, Alexander Fay, Alexander Pokahr
Publikováno v:
INDIN
Due to rapid changes in customer requirements, production systems constantly need to evolve. Together with the increasing penetration of internet, this everlasting evolution process is one of the main drivers to implement Cyber-Physical Production Sy
Autor:
Mattias Ulbrich, Timo Kehrer, Birgit Vogel-Heuser, Sascha Lity, Bernhard Beckert, Vladimir Klebanov, Christopher Haubeck, Ina Schaefer, Sinem Getir, Matthias Kowal, Winfried Lamersdorf, Jens Folmer, Alexander Fay, Stefan Feldmann, Matthias Tichy, Jan Ladiges
Publikováno v:
INDIN
Automated machines and plants are operated for some decades and undergo an everlasting evolution during this time. In this paper, we present three related open evolution challenges focusing on software evolution in the domain of automated production