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369 results on '"Fischer, Dominik"'

1. Data-driven Feedback Control of Lattice Structures with Localized Actuation and Sensing

Author

Fischer, Dominik, Do, Loi, Smith, Miana, and Zemánek, Jiří

Subjects
Computer Science - Robotics, Electrical Engineering and Systems Science - Systems and Control
Abstract

Assembling lattices from discrete building blocks enables the composition of large, heterogeneous, and easily reconfigurable objects with desirable mass-to-stiffness ratios. This type of building system may also be referred to as a digital material, as it is constituted from discrete, error-correcting components. Researchers have demonstrated various active structures and even robotic systems that take advantage of the reconfigurable, mass-efficient properties of discrete lattice structures. However, the existing literature has predominantly used open-loop control strategies, limiting the performance of the presented systems. In this paper, we present a novel approach to feedback control of digital lattice structures, leveraging real-time measurements of the system dynamics. We introduce an actuated voxel which constitutes a novel means for actuation of lattice structures. Our control method is based on the Extended Dynamical Mode Decomposition algorithm in conjunction with the Linear Quadratic Regulator and the Koopman Model Predictive Control. The key advantage of our approach lies in its purely data-driven nature, without the need for any prior knowledge of a system's structure. We illustrate the developed method via real experiments with custom-built flexible lattice beam, showing its ability to accomplish various tasks even with minimal sensing and actuation resources. In particular, we address two problems: stabilization together with disturbance attenuation, and reference tracking.

Published
2024

5. Optimal Control Policies to Address the Pandemic Health-Economy Dilemma

Author

Salgotra, Rohit, Seidelmann, Thomas, Fischer, Dominik, Mostaghim, Sanaz, and Moshaiov, Amiram

Subjects
Computer Science - Neural and Evolutionary Computing
Abstract

Non-pharmaceutical interventions (NPIs) are effective measures to contain a pandemic. Yet, such control measures commonly have a negative effect on the economy. Here, we propose a macro-level approach to support resolving this Health-Economy Dilemma (HED). First, an extension to the well-known SEIR model is suggested which includes an economy model. Second, a bi-objective optimization problem is defined to study optimal control policies in view of the HED problem. Next, several multi-objective evolutionary algorithms are applied to perform a study on the health-economy performance trade-offs that are inherent to the obtained optimal policies. Finally, the results from the applied algorithms are compared to select a preferred algorithm for future studies. As expected, for the proposed models and strategies, a clear conflict between the health and economy performances is found. Furthermore, the results suggest that the guided usage of NPIs is preferable as compared to refraining from employing such strategies at all. This study contributes to pandemic modeling and simulation by providing a novel concept that elaborates on integrating economic aspects while exploring the optimal moment to enable NPIs.

Published
2021

6. Artificial intelligence extension of the OSCAR‐IB criteria

Author

Petzold, Axel, Albrecht, Philipp, Balcer, Laura, Bekkers, Erik, Brandt, Alexander U, Calabresi, Peter A, Deborah, Orla Galvin, Graves, Jennifer S, Green, Ari, Keane, Pearse A, Bijvank, Jenny A Nij, Sander, Josemir W, Paul, Friedemann, Saidha, Shiv, Villoslada, Pablo, Wagner, Siegfried K, Yeh, E Ann, Aktas, Orhan, Antel, Jack, Asgari, Nasrin, Audo, Isabelle, Avasarala, Jagannadha, Avril, Daly, Bagnato, Francesca R, Banwell, Brenda, Bar‐Or, Amit, Behbehani, Raed, Manterola, Arnaldo Belzunce, Bennett, Jeffrey, Benson, Leslie, Bernard, Jacqueline, Bremond‐Gignac, Dominique, Britze, Josefine, Burton, Jodie, Calkwood, Jonathan, Carroll, William, Chandratheva, Arvind, Cohen, Jeffrey, Comi, Giancarlo, Cordano, Christian, Costa, Silvana, Costello, Fiona, Courtney, Ardith, Cruz‐Herranz, Anes, Cutter, Gary, Crabb, David, Delott, Lindsey, De Seze, Jerome, Diem, Ricarda, Dollfuss, Helene, Ayoubi, Nabil K El, Fasser, Christina, Finke, Carsten, Fischer, Dominik, Fitzgerald, Kathryn, Fonseca, Pedro, Frederiksen, Jette L, Frohman, Elliot, Frohman, Teresa, Fujihara, Kazuo, Cuellar, Iñigo Gabilondo, Galetta, Steven, Garcia‐Martin, Elena, Giovannoni, Gavin, Glebauskiene, Brigita, Suárez, Inés González, Jensen, Gorm Pihl, Hamann, Steffen, Hartung, Hans‐Peter, Havla, Joachim, Hemmer, Bernhard, Huang, Su‐Chun, Imitola, Jaime, Jasinskas, Vytautas, Jiang, Hong, Kafieh, Rahele, Kappos, Ludwig, Kardon, Randy, Keegan, David, Kildebeck, Eric, Kim, Ungsoo Samuel, Klistorner, Sasha, Knier, Benjamin, Kolbe, Scott, Korn, Thomas, Krupp, Lauren, Lagrèze, Wolf, Leocani, Letizia, Levin, Netta, Liskova, Petra, Preiningerova, Jana Lizrova, Lorenz, Birgit, May, Eugene, Miller, David, Mikolajczak, Janine, Saïd, Saddek Mohand, Montalban, Xavier, Morrow, Mark, Mowry, Ellen, and Murta, Joaquim

Subjects
Biomedical and Clinical Sciences, Neurosciences, Bioengineering, Biomedical Imaging, Autoimmune Disease, Neurodegenerative, Aging, Brain Disorders, Neurological, Algorithms, Artificial Intelligence, Big Data, Cohort Studies, Humans, Nervous System Diseases, Retina, Tomography, Optical Coherence, IMSVISUAL, ERN-EYE Consortium, Clinical Sciences, Clinical and health psychology
Abstract

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

Published
2021

9. The biggest business process management problems to solve before we die

Author

Beerepoot, Iris, Di Ciccio, Claudio, Reijers, Hajo A., Rinderle-Ma, Stefanie, Bandara, Wasana, Burattin, Andrea, Calvanese, Diego, Chen, Tianwa, Cohen, Izack, Depaire, Benoît, Di Federico, Gemma, Dumas, Marlon, van Dun, Christopher, Fehrer, Tobias, Fischer, Dominik A., Gal, Avigdor, Indulska, Marta, Isahagian, Vatche, Klinkmüller, Christopher, Kratsch, Wolfgang, Leopold, Henrik, Van Looy, Amy, Lopez, Hugo, Lukumbuzya, Sanja, Mendling, Jan, Meyers, Lara, Moder, Linda, Montali, Marco, Muthusamy, Vinod, Reichert, Manfred, Rizk, Yara, Rosemann, Michael, Röglinger, Maximilian, Sadiq, Shazia, Seiger, Ronny, Slaats, Tijs, Simkus, Mantas, Someh, Ida Asadi, Weber, Barbara, Weber, Ingo, Weske, Mathias, and Zerbato, Francesca

Published
2023
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10. How swarm size during evolution impacts the behavior, generalizability, and brain complexity of animats performing a spatial navigation task

Author

Fischer, Dominik, Mostaghim, Sanaz, and Albantakis, Larissa

Subjects
Computer Science - Neural and Evolutionary Computing, Nonlinear Sciences - Adaptation and Self-Organizing Systems, Quantitative Biology - Neurons and Cognition
Abstract

While it is relatively easy to imitate and evolve natural swarm behavior in simulations, less is known about the social characteristics of simulated, evolved swarms, such as the optimal (evolutionary) group size, why individuals in a swarm perform certain actions, and how behavior would change in swarms of different sizes. To address these questions, we used a genetic algorithm to evolve animats equipped with Markov Brains in a spatial navigation task that facilitates swarm behavior. The animats' goal was to frequently cross between two rooms without colliding with other animats. Animats were evolved in swarms of various sizes. We then evaluated the task performance and social behavior of the final generation from each evolution when placed with swarms of different sizes in order to evaluate their generalizability across conditions. According to our experiments, we find that swarm size during evolution matters: animats evolved in a balanced swarm developed more flexible behavior, higher fitness across conditions, and, in addition, higher brain complexity., Comment: 10 pages, 14 figures, GECCO 2018, July 15-19, 2018, Kyoto, Japan

Published
2018
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17. Enhancing Event Log Quality: Detecting and Quantifying Timestamp Imperfections

Author

Fischer, Dominik Andreas, Goel, Kanika, Andrews, Robert, van Dun, Christopher Gerhard Johannes, Wynn, Moe Thandar, Röglinger, Maximilian, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Fahland, Dirk, editor, Ghidini, Chiara, editor, Becker, Jörg, editor, and Dumas, Marlon, editor

Published
2020
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19. Early REperfusion Therapy with Intravenous Alteplase for Recovery of VISION in Acute Central Retinal Artery Occlusion (REVISION): Study Protocol of a Phase III Trial

Author

Poli, Sven, primary, Grohmann, Carsten, additional, Wenzel, Daniel A., additional, Poli, Khouloud, additional, Tuennerhoff, Johannes, additional, Nedelmann, Max, additional, Fiehler, Jens, additional, Agostini, Hansjürgen, additional, Campbell, Bruce, additional, Fischer, Dominik M., additional, Sykora, Marek, additional, Mac Grory, Brian, additional, Feltgen, Nicolas, additional, Seiffge, David J, additional, Strbian, Daniel, additional, Schultheiß, Maximilian, additional, and Spitzer, Martin S., additional

Published
2024
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24. Non-equilibrium scale invariance and shortcuts to adiabaticity in a one-dimensional Bose gas

Author

Rohringer, Wolfgang, Fischer, Dominik, Steiner, Florian, Mazets, Igor E, Schmiedmayer, Jörg, and Trupke, Michael

Subjects
Condensed Matter - Quantum Gases
Abstract

We present experimental evidence for scale invariant behaviour of the excitation spectrum in phase-fluctuating quasi-1d Bose gases after a rapid change of the external trapping potential. Probing density correlations in free expansion, we find that the temperature of an initial thermal state scales with the spatial extension of the cloud as predicted by a model based on adiabatic rescaling of initial eigenmodes with conserved quasiparticle occupation numbers. Based on this result, we demonstrate that shortcuts to adiabaticity for the rapid expansion or compression of the gas do not induce additional heating., Comment: 8 pages, 5 figures

Published
2013
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30. Process-Data Quality: The True Frontier of Process Mining

Author

Hofstede, Arthur H.M. ter, primary, Koschmider, Agnes, additional, Marrella, Andrea, additional, Andrews, Robert, additional, Fischer, Dominik A., additional, Sadeghianasl, Sareh, additional, Wynn, Moe Thandar, additional, Comuzzi, Marco, additional, De Weerdt, Jochen, additional, Goel, Kanika, additional, Martin, Niels, additional, and Soffer, Pnina, additional

Published
2023
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32. AI in Strategic Foresight - Evaluation of ChatGPT, BARD and Perplexity.

Author

Fischer, Dominik, Heffeter, Florian, Grothe, Stine-Renee, Joachim, Verena, and Jung, Hans H.

Subjects
CHATGPT, CHATBOTS, ARTIFICIAL intelligence, MANAGEMENT, SEARCH engines
Abstract

New AI techniques are available for a variety of applications, including ChatGPT, BARD, and Perplexity. ChatGPT is a chatbot that can generate text on almost any topic or theme, while BARD is a generative AI chatbot that can take written inputs and generate human-like responses to text inputs in a conversational manner. Perplexity is a conversational search engine that enhances the user experience and management decisions. These AI tools can be used separately or combined to form an AI Swarm to assist in strategic foresight within the scenario technique. However, the reliability of large language models like ChatGPT is highly unreliable, and they can generate misinformation and falsehoods. The practical impacts of these models are investigated to evaluate AI-generated results against an existing automotive scenario project. This paper aims to demonstrate AI's capacity to enhance the scenario technique, which streamlines and expedites management decisions. [ABSTRACT FROM AUTHOR]

Published
2023

34. Continuous surveillance of potentially zoonotic avian pathogens detects contemporaneous occurrence of highly pathogenic avian influenza viruses (HPAIV H5) and flaviviruses (USUV, WNV) in several wild and captive birds

Author

Günther, Anne, primary, Pohlmann, Anne, additional, Globig, Anja, additional, Ziegler, Ute, additional, Calvelage, Sten, additional, Keller, Markus, additional, Fischer, Dominik, additional, Staubach, Christoph, additional, Groschup, Martin H., additional, Harder, Timm, additional, and Beer, Martin, additional

Published
2023
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40. The biggest business process management problems to solve before we die

Author

Sub Process Management and Analytics, Business Process Management and Analytics, Beerepoot, Iris, Di Ciccio, Claudio, Reijers, Hajo A., Rinderle-Ma, Stefanie, Bandara, Wasana, Burattin, Andrea, Calvanese, Diego, Chen, Tianwa, Cohen, Izack, Depaire, Benoît, Di Federico, Gemma, Dumas, Marlon, van Dun, Christopher, Fehrer, Tobias, Fischer, Dominik A., Gal, Avigdor, Indulska, Marta, Isahagian, Vatche, Klinkmüller, Christopher, Kratsch, Wolfgang, Leopold, Henrik, Van Looy, Amy, Lopez, Hugo, Lukumbuzya, Sanja, Mendling, Jan, Meyers, Lara, Moder, Linda, Montali, Marco, Muthusamy, Vinod, Reichert, Manfred, Rizk, Yara, Rosemann, Michael, Röglinger, Maximilian, Sadiq, Shazia, Seiger, Ronny, Slaats, Tijs, Simkus, Mantas, Someh, Ida Asadi, Weber, Barbara, Weber, Ingo, Weske, Mathias, Zerbato, Francesca, Sub Process Management and Analytics, Business Process Management and Analytics, Beerepoot, Iris, Di Ciccio, Claudio, Reijers, Hajo A., Rinderle-Ma, Stefanie, Bandara, Wasana, Burattin, Andrea, Calvanese, Diego, Chen, Tianwa, Cohen, Izack, Depaire, Benoît, Di Federico, Gemma, Dumas, Marlon, van Dun, Christopher, Fehrer, Tobias, Fischer, Dominik A., Gal, Avigdor, Indulska, Marta, Isahagian, Vatche, Klinkmüller, Christopher, Kratsch, Wolfgang, Leopold, Henrik, Van Looy, Amy, Lopez, Hugo, Lukumbuzya, Sanja, Mendling, Jan, Meyers, Lara, Moder, Linda, Montali, Marco, Muthusamy, Vinod, Reichert, Manfred, Rizk, Yara, Rosemann, Michael, Röglinger, Maximilian, Sadiq, Shazia, Seiger, Ronny, Slaats, Tijs, Simkus, Mantas, Someh, Ida Asadi, Weber, Barbara, Weber, Ingo, Weske, Mathias, and Zerbato, Francesca

Published
2023

42. Vaccination of Zoo Birds against West Nile Virus—A Field Study

Author

Bergmann, Felicitas, primary, Fischer, Dominik, additional, Fischer, Luisa, additional, Maisch, Heike, additional, Risch, Tina, additional, Dreyer, Saskia, additional, Sadeghi, Balal, additional, Geelhaar, Dietmar, additional, Grund, Lisa, additional, Merz, Sabine, additional, Groschup, Martin H., additional, and Ziegler, Ute, additional

Published
2023
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46. Advancing Process Mining from the Core : Managing Process Mining Project Portfolios from Data Processing to Process Improvement

Author

Fischer, Dominik Andreas

Subjects
Process Improvement, Process Mining, Process Data Quality Management, Management of Process Mining, Project Portfolio Management
Abstract

Process mining is a specialized form of data-driven process analysis that organizations use to understand and improve their business processes. Applying process mining techniques such as process discovery, conformance checking, or enhancement using event logs as the central data source generates insights into process behavior, performance, and compliance. Turning these insights into action supports evidence-based process improvement and strategic decision-making. Therefore, process mining supports multiple phases of the business process management lifecycle (i.e., process discovery, process analysis, process improvement and implementation, and process monitoring and controlling) using data about the execution of a process. The groundwork for these outstanding developments has been laid in academia, where a huge research stream focuses on developing and improving new process mining algorithms for various use cases, resulting in a strong technology core for process mining and analysis techniques. The overall purpose of this dissertation is to advance process mining by building on its solid technological core around the numerous process mining analysis algorithms by adding the missing pieces in preceding and subsequent steps of end-to-end process mining projects. Furthermore, this dissertation also abstracts from a single-project perspective and contributes on the managerial side to the broad applicability of process mining in organizations. Applying design science research principles, the research objectives of this thesis are primarily addressed through design-oriented research by creating and evaluating multiple artifacts in the form of reference architectures, methods, and instantiations. Ultimately, researchers in the process mining field as well as practitioners on the vendor and adopter side should benefit equally from the contributions of this thesis. Therefore, this cumulative dissertation comprising five research papers addresses three challenges that slow down the widespread adoption of process mining in organizations. First, research on adopting process mining at the enterprise level is somewhat fragmented, leading to a call for better guidance on managing process mining project portfolios, complemented by a holistic understanding of the opportunities and challenges of using PM in organizational settings. Therefore, this dissertation provides two deliverables to address this research need: Research Paper P1 provides a holistic overview of the opportunities and challenges of using process mining in organizations. Further, Research Paper P2 developed a method to manage portfolios of process mining projects in a value-oriented manner. Second, for process data quality management, there is a need for a dedicated environment focused on detecting, measuring, and repairing data quality problems. Research Paper P3 proposes a reference architecture for process data quality management to address this research need. The reference architecture is designed to be comprehensive and flexible enough to incorporate current and future contributions to the multifaceted problem of process data quality. Before applying process mining techniques, the high quality of the underlying process data should be validated. However, to my knowledge, research on event log quality assessment of event logs remains scarce. Therefore, Research Paper P4 proposes a second contribution to process data quality management by proposing a user-guided and semi-automated approach for detecting and quantifying process data quality issues in event logs. Third, this dissertation aims to contribute to the data-driven process improvement enabled by process mining. There is still a lack of research on balancing tool-based automation and guidance of business process improvement tasks by incorporating process data and domain expertise. Research Paper P5 addresses this gap by proposing a reference architecture that guides users in improving business processes by leveraging existing process data. The dissertation concludes with a reflection on some limitations that stimulate future research. Overall, this dissertation and the embedded research papers contribute to the identified gaps and aim to enable organizations to realize the full potential of process mining. First, this thesis provides a foundation that enables organizations to tailor their process mining project roadmaps to take advantage of the relevant opportunities and be fully aware of the prevailing challenges. Second, this thesis provides two artifacts for process data quality management to support the preceding data processing step before applying process mining. Third, this thesis presents an approach for assisted business process redesign to support the subsequent process improvement step that can leverage insights from applying process mining.

Published
2023
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