Published since June 20, 1964

at - Automatisierungstechnik

Methoden und Anwendungen der Steuerungs-, Regelungs- und Informationstechnik

ISSN: 2196-677X

Editor-in-chief: Ulrich Jumar

Impact Factor: 0.823

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About this journal

Objective
Automatisierungstechnik (AUTO) publishes articles covering the entire range of automation technology: development and application of methods, the operating principles, characteristics, and applications of tools and the interrelationships between automation technology and societal developments. The journal includes a tutorial series on "Theory for Users," and a forum for the exchange of viewpoints concerning past, present, and future developments. Automatisierungstechnik is the official organ of GMA (The VDI/VDE Society for Measurement and Automatic Control) and NAMUR (The Process-Industry Interest Group for Automation Technology).

Topics

control engineering
digital measurement systems
cybernetics
robotics
process automation / process engineering
control design
modelling
information processing
man-machine interfaces
networked control systems
complexity management
machine learning
ambient assisted living
automated driving
bio-analysis technology
building automation
factory automation / smart factories
flexible manufacturing systems
functional safety
mechatronic systems

Article formats
Overview articles (reviews), methodological papers, applications papers, tools papers

Your Benefits

Your benefits:

Practical relevance of published articles
Important guide on matters related to automation technology
New theoretical methods and their potential areas of application
Close ties to research institutes and universities engaged in automation research
Annual Best Paper Awards sponsored by industry partners
Special Issues on hot topics by renowned Guest Editors

History

The journal at – Automatisierungstechnik was founded in 1953 in West Germany, at the time entitled rt – Regelungstechnik, nine years after the first German chair for automation control was established at the Technical University of Berlin-Charlottenburg. The first German institute entirely dedicated to automation control was founded in 1955 in Dresden. The division of Germany into East and West as of 1949 affected the German automation control community in terms of knowledge exchange, personal contacts and publication habits, with West German results usually being published in at, while researchers in East Germany published in Messen, Steuern, Regeln (msr), founded in 1957. In 1991, following the German reunification, economic considerations dictated a merger of msr with the practical journal Automatisierungstechnische Praxis (atp), while the more theoretical content previously published by msr found itself drawn to at.

at – Automatisierungstechnik has been closely affiliated with the prime annual automation control conference in Germany, Regelungstechnisches Kolloquium, which is held every February in the town of Boppard near Koblenz. The 50th anniversary of this colloquium was celebrated in 2016 in at with a number of articles including historical accounts and rhymed wit.

Other publications in the field

Previous Titles

at - Automatiserungstechnik was originally founded as rt – Regelungstechnik. Its current name was introduced in 1985.

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Supplementary Materials

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Volume 70 Issue 9

September 2022

Publicly Available September 3, 2022

Frontmatter

Page range: I-IV

Download PDF

Editorial

Open Access September 3, 2022

AI Systems Engineering – systematic development and operation of AI-based systems

Jürgen Beyerer, Julius Pfrommer, Thomas Usländer

Page range: 753-755

Download PDF

Survey

Open Access September 3, 2022

KI-Engineering – AI Systems Engineering

Systematic development of AI as part of systems that master complex tasks

Julius Pfrommer, Thomas Usländer, Jürgen Beyerer

Page range: 756-766

More Download PDF

Abstract

KI-Engineering – translated as AI Systems Engineering – aims at the development of a new engineering practice in the intersection of Systems Engineering and Artificial Intelligence. Its goal is to professionalize the use of AI methods in a systems engineering context. The article defines KI-Engineering and compares it with historical examples of research disciplines that founded engineering disciplines. It furthermore discusses the long-term challenges where further development is needed and which results were already achieved in the context of the Competence Center for KI-Engineering (CC-KING).

September 3, 2022

Tools and methods for Edge-AI-systems

Nils Schwabe, Yexu Zhou, Leon Hielscher, Tobias Röddiger, Till Riedel, Sebastian Reiter

Page range: 767-776

Abstract

The enormous potential of artificial intelligence, especially artificial neural networks, when used for edge computing applications in cars, traffic lights or smart watches, has not yet been fully exploited today. The reasons for this are the computing, energy and memory requirements of modern neural networks, which typically cannot be met by embedded devices. This article provides a detailed summary of today’s challenges and gives a deeper insight into existing solutions that enable neural network performance with modern HW/SW co-design techniques.

Open Access September 3, 2022

PAISE^® – process model for AI systems engineering

Constanze Hasterok, Janina Stompe

Page range: 777-786

More Download PDF

Abstract

The application of artificial-intelligence-(AI)-based methods within the context of complex systems poses new challenges within the product life cycle. The process model for AI systems engineering, PAISE ® , addresses these challenges by combining approaches from the disciplines of systems engineering, software development and data science. The general approach builds on a component-wise development of the overall system including an AI component. This allows domain specific development processes to be parallelized. At the same time, component dependencies are tested within interdisciplinary checkpoints, thus resulting in a refinement of component specifications.

Methods

September 3, 2022

Explainable AI: introducing trust and comprehensibility to AI engineering

Nadia Burkart, Danilo Brajovic, Marco F. Huber

Page range: 787-792

Abstract

Machine learning (ML) rapidly gains increasing interest due to the continuous improvements in performance. ML is used in many different applications to support human users. The representational power of ML models allows solving difficult tasks, while making them impossible to be understood by humans. This provides room for possible errors and limits the full potential of ML, as it cannot be applied in critical environments. In this paper, we propose employing Explainable AI (xAI) for both model and data set refinement, in order to introduce trust and comprehensibility. Model refinement utilizes xAI for providing insights to inner workings of an ML model, for identifying limitations and for deriving potential improvements. Similarly, xAI is used in data set refinement to detect and resolve problems of the training data.

Open Access September 3, 2022

The why and how of trustworthy AI

An approach for systematic quality assurance when working with ML components

Anna Schmitz, Maram Akila, Dirk Hecker, Maximilian Poretschkin, Stefan Wrobel

Page range: 793-804

More Download PDF

Abstract

Artificial intelligence is increasingly penetrating industrial applications as well as areas that affect our daily lives. As a consequence, there is a need for criteria to validate whether the quality of AI applications is sufficient for their intended use. Both in the academic community and societal debate, an agreement has emerged under the term “trustworthiness” as the set of essential quality requirements that should be placed on an AI application. At the same time, the question of how these quality requirements can be operationalized is to a large extent still open. In this paper, we consider trustworthy AI from two perspectives: the product and organizational perspective. For the former, we present an AI-specific risk analysis and outline how verifiable arguments for the trustworthiness of an AI application can be developed. For the second perspective, we explore how an AI management system can be employed to assure the trustworthiness of an organization with respect to its handling of AI. Finally, we argue that in order to achieve AI trustworthiness, coordinated measures from both product and organizational perspectives are required.

Applications

September 3, 2022

Industrial challenges for AI systems engineering

Towards autonomous industrial systems

Ingo Sawilla, Christian Weber, Benedikt Schmidt, Marco Ulrich

Page range: 805-814

Abstract

Integration of Artificial Intelligence (AI) methods into industrial systems engineering processes is challenging. Despite an increasing body of knowledge on AI techniques and impressive state-of-the-art reports, the application of AI in industrial contexts is only at an early stage. This paper summarizes challenges for AI Systems Engineering. Two examples of AI systems engineering are provided: the TRUMPF Sorting Guide and ABB BatchInsight. Summaries of the projects give insights into the project executions and related challenges. The learnings from these projects also show that increased maturity of AI systems engineering can be expected from increased method competence and adjusted project setups. Here guidelines and best practices for AI systems engineering can support.

Forum

September 3, 2022

Better insights into wood-based panel production

What it takes to develop a digital platform for wood-based panel manufacturers to help them produce smarter with the use of AI

Jan Bär, Santhosh Balaji Ramesh, Florian Schleissinger, Jürgen Woll

Page range: 815-822

Abstract

Dieffenbacher has developed a completely new software platform for the digitalization of wood-based panel production plants. The name of the Platform is EVORIS. This article explains how smart data processing in production, with the help of artificial intelligence and machine learning, delivers transparency to production processes and helps operators and technologists to produce smarter. Furthermore, experiences in working methods, the development process, AI engineering and, above all, lessons learnt from theory vs. practice are illustrated.

Volume 70 (2022)

Volume 69 (2021)

Volume 68 (2020)

Volume 67 (2019)

Volume 66 (2018)

Volume 65 (2017)

Volume 64 (2016)

Volume 63 (2015)

Volume 62 (2014)

Volume 61 (2013)

Volume 60 (2012)

Volume 59 (2011)

Volume 58 (2010)

Volume 57 (2009)

Volume 56 (2008)

Volume 55 (2007)

Volume 54 (2006)

Volume 53 (2005)

Volume 52 (2004)

Volume 51 (2003)

Volume 50 (2002)

Volume 49 (2001)

Volume 48 (2000)

Volume 47 (1999)

Volume 46 (1998)

Volume 45 (1997)

Volume 44 (1996)

Volume 43 (1995)

Volume 42 (1994)

Issue 1-12

Volume 41 (1993)

Volume 40 (1992)

Volume 22 (1974)

Volume 21 (1973)

Issue 1-12

Volume 20 (1972)

Issue 1-12

Volume 19 (1971)

Issue JG

Volume 18 (1970)

Issue JG

Volume 17 (1969)

Volume 16 (1968)

Issue JG

Volume 15 (1967)

Issue 1-12

Volume 14 (1966)

Issue 1-12

Volume 13 (1965)

Issue 1-12

Volume 12 (1964)

Issue 1-12

Volume 11 (1963)

Issue 1-12

Volume 10 (1962)

Issue 1-12

Volume 9 (1961)

Issue 1-12

Volume 8 (1960)

Issue 1-12

Volume 7 (1959)

Issue 1-12

Volume 6 (1958)

Issue 1-12

Volume 5 (1957)

Issue 1-12

Volume 4 (1956)

Issue 1-12

Volume 3 (1955)

Issue 1-12

Volume 2 (1954)

Issue 1-12

Volume 1 (1953)

Issue 1-12

5-year Impact Factor	0.654
Cite Score	1.9
Impact Factor	0.823
Journal Citation Indicator	0.17
SCImago Journal Rank	0.342
Source Normalized Impact per Paper	0.712

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Submission
You can submit your manuscript online using the Editorial Manager system. Simply visit http://www.editorialmanager.com/aut/default.aspx directly or click on the link Online Submission of Manuscripts on the journal homepage. For a guide how to use the Editorial Manager please refer to the Author Tutorial for Editorial Manager, also linked on the journal homepage.

Your benefits of publishing with us

Rapid online publication with short turnaround times
High impact
High quality blind peer-review
Free publication of color figures in print and online
No page charges
Optional open access publication
Accepted papers will be published online first as DOI-citable, forward-linked articles for quickest possible visibility for the scientific community
Every article easily discoverable because of Search Engine Optimization (SEO) and comprehensive abstracting and indexing services
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Submission process

Check that your topic fits the scope of the journal
Use our LaTeX or Word templates for drafting a manuscript Submission of your paper via our submission management tool
The editorial office will forward your manuscript to the Editor-in-Chief for evaluation and peer-review. We aim to provide decisions after peer-review within 3 months of receipt (including any author revisions); papers deemed unsuitable for the journal will usually be returned to authors within two weeks of submission
You will be guided through every step of peer review process
Decision on your paper
You will be informed by e-mail if your manuscript has been accepted or rejected or if it requires further attention
Hybrid Open Access is available as an option post-acceptance
Publication online and in print

Please note

Before submitting your article please refer to our at Ethical Guidelines and our Copyright Transfer Agreement
Manuscripts must be written in clear and concise German or English
For German articles we recommend that authors additionally include abstract and keywords in English
Our Repository Policy allows you to distribute 30 PDF copies of your published article to colleagues (the PDF has to include the information that it is an author's copy). Please also feel free to distribute the link to the online abstract
If you have any general questions please visit our FAQ page for authors or contact us directly at AUTO.Editorial@degruyter.com

We look forward to receiving your manuscript!

Hybrid Open Access

In this journal, authors have the option to publish their article under an open access license. Open Access allows you as an author to retain copyright and share your findings with colleagues and interested parties worldwide without any restraints.
Please note that authors from institutions with which we have a transformative agreement can publish open access without paying an article processing charge (APC). More information on the eligible institutions and articles can be found under the "Funding and Support" tab here.

Editorial

Editor-in-Chief
Prof. Dr.-Ing. Ulrich Jumar, ifak, Magdeburg

Advisory Board
Prof. Dr.-Ing. Jürgen Adamy, TU Darmstadt
Prof. Dr.-Ing. habil. Jürgen Beyerer, Fraunhofer-IOSB, Karlsruhe
Prof. Dr.-Ing. habil. Georg Bretthauer, Karlsruher Institut für Technologie
Prof. Dr.-Ing. Alexander Fay, Helmut-Schmidt-Universität, Hamburg
Prof. Dr.-Ing. Sören Hohmann, Karlsruher Institut für Technologie
Prof. Dr.-Ing. Tobias Kleinert, RWTH Aachen
Dr. Kim Listmann, Bender GmbH & Co KG, Grünberg
Prof. Dr. Ralf Mikut, Karlsruher Institut für Technologie
Dr.-Ing. Jörg Neidig, Siemens AG, Nürnberg
Prof. Dr.-Ing. Georg Rauter, University of Basel
Prof. Dr.-Ing. habil. Dipl.-Math. Klaus Röbenack, TU Dresden
Prof. Dr.-Ing. habil. Joachim Rudolph, Universität des Saarlands, Saarbrücken
Prof. Dr.-Ing. habil. Martin Wolter, Otto-von-Guericke-Universität Magdeburg
Prof. Dr. Ningbo Yu, Nankai University, Tianjin, P.R. China

Journal Coordinator
Birgit Zoglmeier
De Gruyter, Munich

AUTO.Editorial@degruyter.com

(Deutsch)

Online ISSN: 2196-677X
Print ISSN: 0178-2312
Type: Journal
Languages: German, English
Publisher: De Gruyter (O)
First published: June 20, 1964
Publication Frequency: 12 Issues per Year
Audience: researchers in the field of automation technology in academia and industry

at - Automatisierungstechnik

Methoden und Anwendungen der Steuerungs-, Regelungs- und Informationstechnik

Frontmatter

AI Systems Engineering – systematic development and operation of AI-based systems

KI-Engineering – AI Systems Engineering

Systematic development of AI as part of systems that master complex tasks

Abstract

Tools and methods for Edge-AI-systems

Abstract

PAISE® – process model for AI systems engineering

Abstract

Explainable AI: introducing trust and comprehensibility to AI engineering

Abstract

The why and how of trustworthy AI

An approach for systematic quality assurance when working with ML components

Abstract

Industrial challenges for AI systems engineering

Towards autonomous industrial systems

Abstract

Better insights into wood-based panel production

What it takes to develop a digital platform for wood-based panel manufacturers to help them produce smarter with the use of AI

Abstract

PAISE^® – process model for AI systems engineering