@inproceedings{48422, author = {{Humpert, Lynn and Tihlarik, Amelie and Wäschle, Moritz and Anacker, Harald and Dumitrescu, Roman and Albers, Albert and Röbenack, Silke and Pfeifer, Sabine}}, booktitle = {{IEEE International Conference on Technology Management, Operations and Decisions (IEEE ICTMOD)}}, location = {{Rabat, Marokko}}, title = {{{Investigating the potential of artificial intelligence for the employee from the perspective of AI-experts}}}, year = {{2023}}, } @inproceedings{48421, author = {{Humpert, Lynn and Zagatta, Kristin and Anacker, Harald and Dumitrescu, Roman}}, booktitle = {{IEEE International Conference on Technology Management, Operations and Decisions (IEEE ICTMOD)}}, location = {{Rabat, Marokko}}, title = {{{Identification of fields of action for validation in Systems Engineering}}}, year = {{2023}}, } @inproceedings{48427, author = {{Gabriel, Stefan and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{Procedia CIRP}}, location = {{Dublin, Ireland}}, title = {{{Strategic planning of the collaboration between humans and artificial intelligence in production}}}, year = {{2023}}, } @inproceedings{48425, author = {{Mundt, Enrik and Wilke, Daria and Anacker, Harald and Dumitrescu, Roman}}, location = {{Maui, Hawaii}}, title = {{{Principles for the effective application of Systems Engineering: A systematic literature review and application use case}}}, year = {{2023}}, } @inproceedings{48426, author = {{Tekaat, Julian and Wilke, Daria and Anacker, Harald and Dumitrescu, Roman}}, location = {{Würzburg}}, title = {{{Integration von Design Thinking in Systems Engineering mit Hilfe des Systemdenkens}}}, year = {{2023}}, } @inproceedings{46069, author = {{Seebauer, Fritz and Kuhlmann, Michael and Haeb-Umbach, Reinhold and Wagner, Petra}}, booktitle = {{12th Speech Synthesis Workshop (SSW) 2023}}, title = {{{Re-examining the quality dimensions of synthetic speech}}}, year = {{2023}}, } @unpublished{48502, abstract = {{The prediction of photon echoes is an important technique for gaining an understanding of optical quantum systems. However, this requires a large number of simulations with varying parameters and/or input pulses, which renders numerical studies expensive. This article investigates how we can use data-driven surrogate models based on the Koopman operator to accelerate this process. In order to be successful, we require a model that is accurate over a large number of time steps. To this end, we employ a bilinear Koopman model using extended dynamic mode decomposition and simulate the optical Bloch equations for an ensemble of inhomogeneously broadened two-level systems. Such systems are well suited to describe the excitation of excitonic resonances in semiconductor nanostructures, for example, ensembles of semiconductor quantum dots. We perform a detailed study on the required number of system simulations such that the resulting data-driven Koopman model is sufficiently accurate for a wide range of parameter settings. We analyze the L2 error and the relative error of the photon echo peak and investigate how the control positions relate to the stabilization. After proper training, the dynamics of the quantum ensemble can be predicted accurately and numerically very efficiently by our methods.}}, author = {{Peitz, Sebastian and Hunstig, Anna and Rose, Hendrik and Meier, Torsten}}, title = {{{Accelerating the analysis of optical quantum systems using the Koopman operator}}}, year = {{2023}}, } @inproceedings{48532, author = {{Philipo, Godiana Hagile and Kakande, Josephine Nakato and Krauter, Stefan}}, booktitle = {{Proceedings of the 2023 IEEE PES/IAS PowerAfrica Conference}}, location = {{Marrakech, Morocco}}, title = {{{Combined Economic and Emission Dispatch of a Microgrid Considering Multiple Generators}}}, year = {{2023}}, } @inproceedings{48533, author = {{Kakande, Josephine Nakato and Philipo, Godiana Hagile and Krauter, Stefan}}, booktitle = {{Proceedings of the 2023 IEEE PES/IAS PowerAfrica Conference}}, location = {{Marrakech, Morocco}}, title = {{{Demand side management potential of refrigeration appliances}}}, year = {{2023}}, } @inproceedings{48531, author = {{Philipo, Godiana Hagile and Kakande, Josephine Nakato and Krauter, Stefan}}, booktitle = {{Proceedings of the 2023 IEEE AFRICON, Nairobi, Kenya}}, location = {{ Nairobi, Kenya}}, title = {{{Demand-Side-Management for Optimal dispatch of an Isolated Solar Microgrid}}}, year = {{2023}}, } @article{35602, abstract = {{Continuous Speech Separation (CSS) has been proposed to address speech overlaps during the analysis of realistic meeting-like conversations by eliminating any overlaps before further processing. CSS separates a recording of arbitrarily many speakers into a small number of overlap-free output channels, where each output channel may contain speech of multiple speakers. This is often done by applying a conventional separation model trained with Utterance-level Permutation Invariant Training (uPIT), which exclusively maps a speaker to an output channel, in sliding window approach called stitching. Recently, we introduced an alternative training scheme called Graph-PIT that teaches the separation network to directly produce output streams in the required format without stitching. It can handle an arbitrary number of speakers as long as never more of them overlap at the same time than the separator has output channels. In this contribution, we further investigate the Graph-PIT training scheme. We show in extended experiments that models trained with Graph-PIT also work in challenging reverberant conditions. Models trained in this way are able to perform segment-less CSS, i.e., without stitching, and achieve comparable and often better separation quality than the conventional CSS with uPIT and stitching. We simplify the training schedule for Graph-PIT with the recently proposed Source Aggregated Signal-to-Distortion Ratio (SA-SDR) loss. It eliminates unfavorable properties of the previously used A-SDR loss and thus enables training with Graph-PIT from scratch. Graph-PIT training relaxes the constraints w.r.t. the allowed numbers of speakers and speaking patterns which allows using a larger variety of training data. Furthermore, we introduce novel signal-level evaluation metrics for meeting scenarios, namely the source-aggregated scale- and convolution-invariant Signal-to-Distortion Ratio (SA-SI-SDR and SA-CI-SDR), which are generalizations of the commonly used SDR-based metrics for the CSS case.}}, author = {{von Neumann, Thilo and Kinoshita, Keisuke and Boeddeker, Christoph and Delcroix, Marc and Haeb-Umbach, Reinhold}}, issn = {{2329-9290}}, journal = {{IEEE/ACM Transactions on Audio, Speech, and Language Processing}}, keywords = {{Continuous Speech Separation, Source Separation, Graph-PIT, Dynamic Programming, Permutation Invariant Training}}, pages = {{576--589}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Segment-Less Continuous Speech Separation of Meetings: Training and Evaluation Criteria}}}, doi = {{10.1109/taslp.2022.3228629}}, volume = {{31}}, year = {{2023}}, } @inbook{46460, author = {{Ngonga Ngomo, Axel-Cyrille and Demir, Caglar and Kouagou, N'Dah Jean and Heindorf, Stefan and Karalis, Nikoloas and Bigerl, Alexander}}, booktitle = {{Compendium of Neurosymbolic Artificial Intelligence}}, pages = {{272–286}}, publisher = {{IOS Press}}, title = {{{Class Expression Learning with Multiple Representations}}}, year = {{2023}}, } @inproceedings{49109, abstract = {{We propose a diarization system, that estimates “who spoke when” based on spatial information, to be used as a front-end of a meeting transcription system running on the signals gathered from an acoustic sensor network (ASN). Although the spatial distribution of the microphones is advantageous, exploiting the spatial diversity for diarization and signal enhancement is challenging, because the microphones’ positions are typically unknown, and the recorded signals are initially unsynchronized in general. Here, we approach these issues by first blindly synchronizing the signals and then estimating time differences of arrival (TDOAs). The TDOA information is exploited to estimate the speakers’ activity, even in the presence of multiple speakers being simultaneously active. This speaker activity information serves as a guide for a spatial mixture model, on which basis the individual speaker’s signals are extracted via beamforming. Finally, the extracted signals are forwarded to a speech recognizer. Additionally, a novel initialization scheme for spatial mixture models based on the TDOA estimates is proposed. Experiments conducted on real recordings from the LibriWASN data set have shown that our proposed system is advantageous compared to a system using a spatial mixture model, which does not make use of external diarization information.}}, author = {{Gburrek, Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. Asilomar Conference on Signals, Systems, and Computers}}, keywords = {{Diarization, time difference of arrival, ad-hoc acoustic sensor network, meeting transcription}}, title = {{{Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks}}}, year = {{2023}}, } @article{47854, author = {{Scholtysik, Michel and Koldewey, Christian and Rohde, Malte and Dumitrescu, Roman}}, issn = {{2212-8271}}, journal = {{Procedia CIRP}}, keywords = {{General Medicine}}, pages = {{841--846}}, publisher = {{Elsevier BV}}, title = {{{Integrative conceptualization of products and business models for the circular economy: A systematic literature review}}}, doi = {{10.1016/j.procir.2023.03.129}}, volume = {{119}}, year = {{2023}}, } @techreport{47855, author = {{Scholtysik, Michel and Koldewey, Christian and Dumitrescu, Roman and Rasor, Anja and Ködding, Patrick and Wegel, Arthur and Fischer, Lena}}, title = {{{Die Transformation zum Smart Service-Anbieter}}}, year = {{2023}}, } @inproceedings{49349, author = {{Scholtysik, Michel and Koldewey, Christian and Dumitrescu, Roman and Pierenkemper, Christoph and Hensen, Christian}}, title = {{{Einstieg in die Kreislaufwirtschaft: Integrative Planung von Produkten und Geschäftsmodellen}}}, year = {{2023}}, } @inproceedings{49322, author = {{Ködding, Patrick and Tissen, Denis and Koldewey, Christian}}, booktitle = {{Proceedings of the 56th CIRP Conference on Manufacturing Systems, CIRP CMS ‘23}}, location = {{Capetown, South Africa}}, publisher = {{Elsevier Ltd.}}, title = {{{A Data Map for Product Creation: Tasks, Data Flows, and IT-Systems from the Initial Idea to the Start of Production}}}, year = {{2023}}, } @inproceedings{49365, author = {{Brock, Jonathan and Rempe, Niclas and von Enzberg, Sebastian and Kühn, Arno and Dumitrescu, Roman}}, booktitle = {{5th Conference on Production Systems and Logistics }}, location = {{STELLENBOSCH, SOUTH AFRICA}}, title = {{{A Framework For The Domain-Driven Utilization Of Manufacturing Sensor Data In Process Mining: An Action Design Approach}}}, year = {{2023}}, } @inproceedings{49362, author = {{Weller, Julian and Migenda, Nico and Wegel, Arthur and Kohlhase, Martin and Schenk, Wolfram and Dumitrescu, Roman}}, booktitle = {{IEEE ADACIS 2023}}, location = {{Marrakesh, Marokko}}, title = {{{Conceptual Framework for Prescriptive Analytics based on decision Theory}}}, year = {{2023}}, } @inproceedings{47836, author = {{Ködding, Patrick and Koldewey, Christian and Dumitrescu, Roman}}, booktitle = {{Proceedings of the XXXII ISPIM Innovation Conference}}, title = {{{A Reference Process Model for Scenario-based Foresight}}}, year = {{2023}}, }