@phdthesis{28374, author = {{Bretz, Lukas}}, isbn = {{978-3-947647-20-0}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Rahmenwerk zur Planung und Einführung von Systems Engineering und Model-Based Systems Engineering}}}, volume = {{401}}, year = {{2021}}, } @inproceedings{27087, author = {{Gabriel, Stefan and Niewöhner, Nadine and Asmar, Laban and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Procedia CIRP}}, pages = {{427--432}}, publisher = {{Elsevier}}, title = {{{Integration of agile practices in the product development process of intelligent technical systems}}}, volume = {{100}}, year = {{2021}}, } @inproceedings{27094, author = {{Wiecher, Carsten and Greenyer, Joel and Wolff, Carsten and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{Requirements Engineering: Foundation for Software Quality, REFSQ 2021}}, editor = {{Dalpiaz, Fabiano and Spoletini, Paola}}, location = {{Essen}}, publisher = {{Springer Nature Switzerland AG}}, title = {{{Iterative and Scenario-Based Requirements Specification in a System of Systems Context}}}, year = {{2021}}, } @article{21004, abstract = {{Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.}}, author = {{Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}}, issn = {{0162-8828}}, journal = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}}, keywords = {{Automated Machine Learning, Multi Label Classification, Hierarchical Planning, Bayesian Optimization}}, pages = {{1--1}}, title = {{{AutoML for Multi-Label Classification: Overview and Empirical Evaluation}}}, doi = {{10.1109/tpami.2021.3051276}}, year = {{2021}}, } @article{21092, abstract = {{Automated Machine Learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoML is an effective use of computational resources: An AutoML process involves the evaluation of many candidate pipelines, which are costly but often ineffective because they are canceled due to a timeout. In this paper, we present an approach to predict the runtime of two-step machine learning pipelines with up to one pre-processor, which can be used to anticipate whether or not a pipeline will time out. Separate runtime models are trained offline for each algorithm that may be used in a pipeline, and an overall prediction is derived from these models. We empirically show that the approach increases successful evaluations made by an AutoML tool while preserving or even improving on the previously best solutions.}}, author = {{Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke}}, journal = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}}, publisher = {{IEEE}}, title = {{{Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning}}}, year = {{2021}}, } @inproceedings{21570, author = {{Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, title = {{{Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance}}}, year = {{2021}}, } @book{23458, author = {{Dumitrescu, Roman and Albers, Albert and Gausemeier, Jürgen and Riedel, Oliver and Stark, Rainer}}, publisher = {{Fraunhofer-Institut für Entwurfstechnik Mechatronik IEM, Paderborn}}, title = {{{Engineering in Deutschland – Status quo in Wirtschaft und Wissenschaft. Ein Beitrag zum Advanced Systems Engineering}}}, year = {{2021}}, } @inproceedings{22913, author = {{Hüllermeier, Eyke and Mohr, Felix and Tornede, Alexander and Wever, Marcel Dominik}}, location = {{Bilbao (Virtual)}}, title = {{{Automated Machine Learning, Bounded Rationality, and Rational Metareasoning}}}, year = {{2021}}, } @inproceedings{22914, author = {{Mohr, Felix and Wever, Marcel Dominik}}, location = {{Virtual}}, title = {{{Replacing the Ex-Def Baseline in AutoML by Naive AutoML}}}, year = {{2021}}, } @unpublished{30866, abstract = {{Automated machine learning (AutoML) strives for the automatic configuration of machine learning algorithms and their composition into an overall (software) solution - a machine learning pipeline - tailored to the learning task (dataset) at hand. Over the last decade, AutoML has developed into an independent research field with hundreds of contributions. While AutoML offers many prospects, it is also known to be quite resource-intensive, which is one of its major points of criticism. The primary cause for a high resource consumption is that many approaches rely on the (costly) evaluation of many machine learning pipelines while searching for good candidates. This problem is amplified in the context of research on AutoML methods, due to large scale experiments conducted with many datasets and approaches, each of them being run with several repetitions to rule out random effects. In the spirit of recent work on Green AI, this paper is written in an attempt to raise the awareness of AutoML researchers for the problem and to elaborate on possible remedies. To this end, we identify four categories of actions the community may take towards more sustainable research on AutoML, i.e. Green AutoML: design of AutoML systems, benchmarking, transparency and research incentives.}}, author = {{Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{arXiv:2111.05850}}, title = {{{Towards Green Automated Machine Learning: Status Quo and Future Directions}}}, year = {{2021}}, } @inproceedings{21198, author = {{Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}}, location = {{Delhi, India}}, title = {{{Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data}}}, year = {{2021}}, } @unpublished{27778, abstract = {{Consider a set of jobs connected to a directed acyclic task graph with a fixed source and sink. The edges of this graph model precedence constraints and the jobs have to be scheduled with respect to those. We introduce the Server Cloud Scheduling problem, in which the jobs have to be processed either on a single local machine or on one of many cloud machines. Both the source and the sink have to be scheduled on the local machine. For each job, processing times both on the server and in the cloud are given. Furthermore, for each edge in the task graph, a communication delay is included in the input and has to be taken into account if one of the two jobs is scheduled on the server, the other in the cloud. The server can process jobs sequentially, whereas the cloud can serve as many as needed in parallel, but induces costs. We consider both makespan and cost minimization. The main results are an FPTAS with respect for the makespan objective for a fairly general case and strong hardness for the case with unit processing times and delays.}}, author = {{Maack, Marten and Meyer auf der Heide, Friedhelm and Pukrop, Simon}}, booktitle = {{arXiv:2108.02109}}, title = {{{Full Version -- Server Cloud Scheduling}}}, year = {{2021}}, } @phdthesis{52665, author = {{Hillebrand, Michael}}, isbn = {{978-3-947647-22-4}}, title = {{{Entwicklungssystematik zur Integration von Eigenschaften der Selbstheilung in Intelligente Technische Systeme }}}, volume = {{Band 403}}, year = {{2021}}, } @phdthesis{52664, author = {{Wu, Liang}}, isbn = {{978-3-947647-21-7}}, title = {{{Ultrabreitbandige Sampler in SiGe-BiCMOS-Technologie für Analog-Digital-Wandler mit zeitversetzter Abtastung}}}, volume = {{402}}, year = {{2021}}, } @phdthesis{23379, abstract = {{Mit der zunehmenden Bedeutung von digitalen Lösungen und innovativen Dienstleistungen geht eine signifikante Transformation des produzierenden Gewerbes einher. Die Digitalisierung führt zu intelligenten Produkten, die Daten generieren und über das Internet austauschen. Auf Basis dieser Daten können Produkthersteller gänzlich neue digitale Dienstleistungen anbieten, sogenannte Smart Services. Ihre erfolgreiche Umsetzung ist essentiell, um in der Wettbewerbsarena der Zukunft bestehen zu können. Die Gestaltung eines Smart Service-Geschäfts ist jedoch nicht trivial. Ziel der vorliegenden Arbeit ist eine Systematik zur Entwicklung von Smart Service-Strategien im produzierenden Gewerbe. Die Systematik besteht aus drei Bestandteilen: der Erste ist die Konzeption von Smart Service-Strategien im Sinne eines Referenzmodells. Sie definiert die auszugestaltenden Aspekte. Der Zweite ist das Gestaltungswissen. Es werden Normstrategien und Funktionalitäten im Kontext von Smart Services für die Strategieentwicklung bereitgestellt. Die Strategieentwicklung wird im dritten Bestandteil adressiert, einer Methode bestehend aus einem Vorgehensmodell und unterstützenden Hilfsmitteln. Das Vorgehensmodell orchestriert den Einsatz der Hilfsmittel und des Gestaltungswissens. Resultat ist eine Smart Service-Strategie, die die Vision für das Smart Service-Geschäft sowie den Weg zu deren Realisierung darstellt. Die Systematik wurde anhand eines Unternehmens des Sondermaschinenbaus erfolgreich validiert.}}, author = {{Koldewey, Christian}}, isbn = {{978-3-947647-18-7}}, keywords = {{Smart Service, Strategie}}, pages = {{4, 217, A--41}}, title = {{{Systematik zur Entwicklung von Smart Service-Strategien im produzierenden Gewerbe}}}, doi = {{10.17619/UNIPB/1-1167}}, volume = {{399}}, year = {{2021}}, } @proceedings{27519, editor = {{Gausemeier, Jürgen and Bauer, Wilhelm and Dumitrescu, Roman}}, publisher = {{Heinz Nixdorf Institut, Universität Paderborn}}, title = {{{Vorausschau und Technologieplanung - 16. Symposium für Vorausschau und Technologieplanung}}}, volume = {{Band 400}}, year = {{2021}}, } @phdthesis{42070, author = {{Olma, Simon}}, isbn = {{9783947647231}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts}}, title = {{{Systemtheorie von Hardware-in-the-Loop-Simulationen mit Anwendung auf einem Fahrzeugachsprüfstand mit parallelkinematischem Lastsimulator}}}, volume = {{404}}, year = {{2021}}, } @phdthesis{28370, author = {{Kohlstedt, Andreas}}, isbn = {{978-3-947647-15-6}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Modellbasierte Synthese einer hybriden Kraft-/Positionsregelung für einen Fahrzeugachsprüfstand mit hydraulischem Hexapod}}}, volume = {{396}}, year = {{2021}}, } @article{23991, author = {{Kruse, Stephan and Gudyriev, Sergiy and Kneuper, Pascal and Schwabe, Tobias and Kurz, Heiko G. and Scheytt, Christoph}}, journal = {{IEEE Microwave and Wireless Components Letters}}, number = {{6}}, pages = {{783--786}}, title = {{{Silicon Photonic Radar Transmitter IC for mm-Wave Large Aperture MIMO Radar Using Optical Clock Distribution}}}, doi = {{10.1109/LMWC.2021.3062112}}, volume = {{31}}, year = {{2021}}, } @phdthesis{28367, author = {{Echterfeld, Julian}}, isbn = {{978-3-947647-12-5}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Systematik zur Digitalisierung von Produktprogrammen}}}, volume = {{393}}, year = {{2020}}, }