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@PHDTHESIS{Kusmenko:835778,
author = {Kusmenko, Evgeny},
othercontributors = {Rumpe, Bernhard and Aßmann, Uwe},
title = {{M}odel-driven development methodology and domain-specific
languages for the design of artificial intelligence in
cyber-physical systems},
volume = {49},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Düren},
publisher = {Shaker Verlag},
reportid = {RWTH-2021-10814},
isbn = {978-3-8440-8286-9},
series = {Aachener Informatik Berichte Software Engineering},
pages = {xiv, 324 Seiten : Illustrationen},
year = {2021},
note = {Zweitveröffentlicht auf dem Publikationsserver der RWTH
Aachen University 2022; Dissertation, RWTH Aachen
University, 2021},
abstract = {The development of cyber-physical systems poses a multitude
of challenges requiring experts from different fields. Such
systems cannot be developed successfully without the support
of appropriate processes, languages, and tools. Model-driven
software engineering is an important approach which helps
development teams to cope with the increasing complexity of
today's cyber-physical systems. The aim of this thesis is to
develop a model-driven engineering methodology with a
particular focus on interconnected intelligent
cyber-physical systems such as cooperative vehicles. The
basis of the proposed methodology is a
component-and-connector architecture description language
focusing on the decomposition and integration of
cyber-physical system software. It features a strong,
math-oriented type system abstracting away from the
technical realization and incorporating physical units. To
facilitate the development of highly-interconnected
self-adaptive systems, the language enables its users to
model component and connector arrays and supports
architectural runtime-reconfiguration. Architectural
elements can be altered, added, and removed dynamically upon
the occurrence of trigger events. In order to fully cover
the development process, the proposed methodology, in
addition to structural modeling, provides means for behavior
specification and its seamless integration into the
components of the architecture. A matrix-oriented scripting
language enables the developer to specify algorithms using a
syntax close to the mathematical domain. What is more, a
dedicated deep learning modeling language is provided for
the development and training of neural networks as directed
acyclic graphs of neuron layers. The framework supports
different learning methods including supervised,
reinforcement, and generative adversarial learning, covering
a broad range of applications from image and natural
language processing to decision making and test data
generation. The presented toolchain enables an automated
generation of fully functional C++ code together with the
corresponding build and training scripts based on the
architectural models and behavior specifications. Finally,
to facilitate the integration and deployment of the modeled
software in distributed environments, we use a tagging
approach to model the middleware and to control a middleware
generation toolchain.},
cin = {121510 / 120000},
ddc = {004},
cid = {$I:(DE-82)121510_20140620$ / $I:(DE-82)120000_20140620$},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2021-10814},
url = {https://publications.rwth-aachen.de/record/835778},
}
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