AI Assisted Interference Classification to Improve EMC Troubleshooting in Electronic System Development - Maalouly, Jad; Hemker, Dennis; Hedayat, Christian; Rückert, Christian; Kaufmann, Ivan; Olbrich, Marcel; Lange, Sven; Mathis, Harald (2022 Kleinheubach Conference)
In this paper, machine learning techniques will be used to classify different PCB layouts given their electromagnetic frequency spectra. These spectra result from a simulated near-field measurement of electric field strengths at different locations. Measured values consist of real and imaginary parts (amplitude and phase) in X, Y and Z directions. Training data was obtained in the time domain by varying transmission line geometries (size, distance and signaling). It was then transformed into the frequency domain and used as deep neural network input. Principal component analysis was applied to reduce the sample dimension. The results show that classifying different designs is possible with high accuracy based on synthetic data. Future work comprises measurements of real, custom-made PCB with varying parameters to adapt the simulation model and also test the neural network. Finally, the trained model could be used to give hints about the error’s cause when overshooting EMC limits.
Keywords: emc; pcb; electronic system development; machine learning; neural network

Die „dritte Wissensdimension“. Eine Epistemologie für eine neue Wissenswelt - Hagengruber, Ruth Edith (Sitzungsberichte der Leibniz-Sozietät der Wissenschaften 150/151, Jahrgang 2022: „Cyberscience – Wissenschaftsforschung und Informatik. Digitale Medien und die Zukunft der Kultur wissenschaftlicher Tätigkeit. Arbeitskreis „Emergente Systeme/Informatik und Gesellschaft“ der Leibniz-Sozietät der Wissenschaften zu Berlin in Kooperation mit der Gesellschaft für Wissenschaftsforschung“)

Chatbot-Enhanced Requirements Resolution for Automated Service Compositions - Kersting, Joschka; Ahmed, Mobeen; Geierhos, Michaela (HCI International 2022 Posters)
This work addresses the automatic resolution of software requirements. In the vision of On-The-Fly Computing, software services should be composed on demand, based solely on natural language input from human users. To enable this, we build a chatbot solution that works with human-in-the-loop support to receive, analyze, correct, and complete their software requirements. The chatbot is equipped with a natural language processing pipeline and a large knowledge base, as well as sophisticated dialogue management skills to enhance the user experience. Previous solutions have focused on analyzing software requirements to point out errors such as vagueness, ambiguity, or incompleteness. Our work shows how apps can collaborate with users to efficiently produce correct requirements. We developed and compared three different chatbot apps that can work with built-in knowledge. We rely on ChatterBot, DialoGPT and Rasa for this purpose. While DialoGPT provides its own knowledge base, Rasa is the best system to combine the text mining and knowledge solutions at our disposal. The evaluation shows that users accept 73% of the suggested answers from Rasa, while they accept only 63% from DialoGPT or even 36% from ChatterBot.
Keywords: On-The-Fly Computing; Chatbot; Knowledge Base

Methodenentwicklung zur Verbesserung der Schädigungsmodellierung in der numerischen 3D-Belastungssimulation mechanischer Fügeverfahren unter Berücksichtigung der fügeinduzierten Vorbeanspruchung - Otroshi, Mortaza; Meschut, Gerson

Microinverter PV Systems: New Efficiency Rankings and Formula for Energy Yield Assessment for any PV Panel Size at different Microinverter types - Krauter, Stefan; Bendfeld, Jörg (Proceedings of the 8th World Conference on Photovoltaik Energy Conversion)

Optimal Design of a Semi Grid-Connected PV System for a Site in Lwak, Kenya Using HOMER - Kakande, Josephine Nakato; Philipo, Godiana Hagile; Krauter, Stefan (Proceedings of the 8th World Conference on Photovoltaik Energy Conversion)

Schädigungsmodellierung von Hilfsfügeelementen beim mechanischen Fügen von Stahlwerkstoffen - Otroshi, Mortaza; Meschut, Gerson

Hardware Trojans in Reconfigurable Computing - Ahmed, Qazi Arbab
Wettstreit zwischen der Entwicklung neuer Hardwaretrojaner und entsprechender Gegenmaßnahmen beschreiten Widersacher immer raffiniertere Wege um Schaltungsentwürfe zu infizieren und dabei selbst fortgeschrittene Test- und Verifikationsmethoden zu überlisten. Abgesehen von den konventionellen Methoden um einen Trojaner in eine Schaltung für ein Field-programmable Gate Array (FPGA) einzuschleusen, können auch die Entwurfswerkzeuge heimlich kompromittiert werden um einen Angreifer dabei zu unterstützen einen erfolgreichen Angriff durchzuführen, der zum Beispiel Fehlfunktionen oder ungewollte Informationsabflüsse bewirken kann. Diese Dissertation beschäftigt sich hauptsächlich mit den beiden Blickwinkeln auf Hardwaretrojaner in rekonfigurierbaren Systemen, einerseits der Perspektive des Verteidigers mit einer Methode zur Erkennung von Trojanern auf der Bitstromebene, und andererseits derjenigen des Angreifers mit einer neuartigen Angriffsmethode für FPGA Trojaner. Für die Verteidigung gegen den Trojaner ``Heimtückische LUT'' stellen wir die allererste erfolgreiche Gegenmaßnahme vor, die durch Verifikation mittels Proof-carrying Hardware (PCH) auf der Bitstromebene direkt vor der Konfiguration der Hardware angewendet werden kann, und präsentieren ein vollständiges Schema für den Entwurf und die Verifikation von Schaltungen für iCE40 FPGAs. Für die Gegenseite führen wir einen neuen Angriff ein, welcher bösartiges Routing im eingefügten Trojaner ausnutzt um selbst im fertigen Bitstrom in einem inaktiven Zustand zu verbleiben: Hierdurch kann dieser neuartige Angriff zur Zeit weder von herkömmlichen Test- und Verifikationsmethoden, noch von unserer vorher vorgestellten Verifikation auf der Bitstromebene entdeckt werden.
Keywords: FPGA Security; Hardware Trojans; Bitstream-level Trojans; Bitstream Verification

Evaluation Criteria in Product Idea Selection Decisions - Gräßler, Iris; Koch, Anna-Sophie (XXXIII Proceedings of the ISPIM Innovation Conference)
Within innovation management, choosing the best fitting product idea is the most important decision point. The future existence and success of the organization is depending on selected product ideas. To find the best fitting idea for an organization, it needs evaluation criteria representing the organizations mission Therefore, this study focuses on a holistic overview about evaluation criteria and on supporting organizations in the process to select its individual evaluation criteria. Based on a literature study, existing approaches regarding evaluation criteria in product idea selection are identified and a list of evaluation criteria is reworked. Using the list of evaluation criteria, a prioritization method is created. Within an interview with experts in innovation management, the method is discussed regarding usability and the level of assistance in product idea selection. The developed criteria can be used directly by innovation manager and industrial practitioners to evaluate potential product ideas.
Keywords: innovation management; evaluation criteria; idea selection; idea evaluation; meta-study

Implicit Statements in Healthcare Reviews: A Challenge for Sentiment Analysis - Kersting, Joschka; Bäumer, Frederik Simon (Proceedings of the Fourteenth International Conference on Pervasive Patterns and Applications (PATTERNS 2022): Special Track AI-DRSWA: Maturing Artificial Intelligence - Data Science for Real-World Applications)
This paper aims at discussing past limitations set in sentiment analysis research regarding explicit and implicit mentions of opinions. Previous studies have regularly neglected this question in favor of methodical research on standard-datasets. Furthermore, they were limited to linguistically less-diverse domains, such as commercial product reviews. We face this issue by annotating a German-language physician review dataset that contains numerous implicit, long, and complex statements that indicate aspect ratings, such as the physician’s friendliness. We discuss the nature of implicit statements and present various samples to illustrate the challenge described.
Keywords: Sentiment analysis; Natural language processing; Aspect phrase extraction

Network Disruption and the Common-Enemy Effect - Hoyer, Britta; De Jaegher, Kris (International Journal of Game Theory)

End-to-End Dereverberation, Beamforming, and Speech Recognition in A Cocktail Party - Zhang, Wangyou; Chang, Xuankai; Boeddeker, Christoph; Nakatani, Tomohiro; Watanabe, Shinji; Qian, Yanmin (IEEE/ACM Transactions on Audio, Speech, and Language Processing)
Far-field multi-speaker automatic speech recognition (ASR) has drawn increasing attention in recent years. Most existing methods feature a signal processing frontend and an ASR backend. In realistic scenarios, these modules are usually trained separately or progressively, which suffers from either inter-module mismatch or a complicated training process. In this paper, we propose an end-to-end multi-channel model that jointly optimizes the speech enhancement (including speech dereverberation, denoising, and separation) frontend and the ASR backend as a single system. To the best of our knowledge, this is the first work that proposes to optimize dereverberation, beamforming, and multi-speaker ASR in a fully end-to-end manner. The frontend module consists of a weighted prediction error (WPE) based submodule for dereverberation and a neural beamformer for denoising and speech separation. For the backend, we adopt a widely used end-to-end (E2E) ASR architecture. It is worth noting that the entire model is differentiable and can be optimized in a fully end-to-end manner using only the ASR criterion, without the need of parallel signal-level labels. We evaluate the proposed model on several multi-speaker benchmark datasets, and experimental results show that the fully E2E ASR model can achieve competitive performance on both noisy and reverberant conditions, with over 30% relative word error rate (WER) reduction over the single-channel baseline systems.

Characterization of Usage Data with the Help of Data Classifications - Panzner, Melina; von Enzberg, Sebastian; Meyer, Maurice; Dumitrescu, Roman (Journal of the Knowledge Economy)
Abstract Comprehensive data understanding is a key success driver for data analytics projects. Knowing the characteristics of the data helps a lot in selecting the appropriate data analysis techniques. Especially in data-driven product planning, knowledge about the data is a necessary prerequisite because data of the use phase is very heterogeneous. However, companies often do not have the necessary know-how or time to build up solid data understanding in connection with data analysis. In this paper, we develop a methodology to organize and categorize and thus understand use phase data in a way that makes it accessible to general data analytics workflows, following a design science research approach. We first present a knowledge base that lists typical use phase data from a product planning view. Second, we develop a taxonomy based on standard literature and real data objects, which covers the diversity of the data considered. The taxonomy provides 8 dimensions that support classification of use phase data and allows to capture data characteristics from a data analytics view. Finally, we combine both views by clustering the objects of the knowledge base according to the taxonomy. Each of the resulting clusters covers a typical combination of analytics relevant characteristics occurring in practice. By abstracting from the diversity of use phase data into artifacts with manageable complexity, our approach provides guidance to choose appropriate data analysis and AI techniques.
Keywords: Economics and Econometrics

Using vibration data to classify conditions in disk stack separators - Merkelbach, Silke; Afroze, Lameya; Janssen, Nils; von Enzberg, Sebastian; Kühn, Arno; Dumitrescu, Roman (Vibroengineering PROCEDIA)
Mounting sensors in disk stack separators is often a major challenge due to the operating conditions. However, a process cannot be optimally monitored without sensors. Virtual sensors can be a solution to calculate the sought parameters from measurable values. We measured the vibrations of disk stack separators and applied machine learning (ML) to detect whether the separator contains only water or whether particles are also present. We combined seven ML classification algorithms with three feature engineering strategies and evaluated our model successfully on vibration data of an experimental disk stack separator. Our experimental results demonstrate that random forest in combination with manual feature engineering using domain specific knowledge about suitable features outperforms all other models with an accuracy of 91.27 %.
Keywords: General Medicine

Soziotechnische Herausforderungen bei der Einführung von RPA managen - Hobscheidt, Daniela; Menzefricke, Jörn Steffen; Gabriel, Stefan; Kühn, Arno; Dumitrescu, Roman (Praxishandbuch Robotic Process Automation (RPA))

Transfer von Arbeit 4.0-Anwendungsszenarien - Bansmann, Michael; Dumitrescu, Roman; Fechtelpeter, Christian (Gestaltung digitalisierter Arbeitswelten)

Nutzung von Process Mining in RPA-Projekten - Brock, Jonathan; von Enzberg, Sebastian; Kühn, Arno; Dumitrescu, Roman (Praxishandbuch Robotic Process Automation (RPA))

Rekonstruktive Evaluationsforschung im Kontext praxeologischer Kompetenzdiskurse. Kritische Reflexionen und konzeptionelle Überlegungen zur Dokumentarischen Evaluationsforschung - Bloh, Thiemo (Zeitschrift für Evaluation)
Praxeologische Kompetenzansätze verstehen Kompetenz als sozial erlernt und folglich als relativ zum sozialen Kontext. Damit einher geht die Frage, wie solche praxeologisch gerahmten Kompetenzen eigentlich unabhängig von der sie hervorbringenden Praxis evaluiert werden können – und eben dadurch erst für einen breiteren Kompetenzdiskurs fruchtbar sind. Die Dokumentarische Evaluationsforschung bietet hierzu erste Anhaltspunkte, offenbart aber auch Grenzen, die mit dem Evaluationsverständnis zusammenhängen, sich jedoch in der Forschungspraxis so nicht finden lassen. Aus der Differenz zwischen Methode und Praxis dokumentarischer Evaluation lässt sich formulieren, wie eine praxeologische Evaluation gestaltet werden könnte. Dabei spielt die Formulierung von Referenzrahmen eine zentrale Rolle, welche einerseits der zu evaluierenden Praktik external sein, andererseits praktisch formuliert werden müssen, damit sie soziale Praktiken jenseits ihrer eigenen Sinnhaftigkeit evaluativ (er-)fassen können.
Keywords: Strategy and Management; Applied Psychology; Social Sciences (miscellaneous); Education; Communication; Statistics and Probability

Ein kritisch-multiperspektivischer Blick auf Forschendes Lernen in der Lehrkräftebildung. Fragen, Erwägungen und Rekonstruktionen. - Bloh, Thiemo; Caruso, Carina (die hochschullehre)

Grundlagentheoretische Differenzen in der Lehrkräftekooperationsforschung - Bloh, Thiemo (Zeitschrift für Pädagogik)