@incollection{HubertGuoDolletal.2025, author = {Hubert, Andreas and Guo, C. and Doll, Konrad and Kuhn, F. and Kr{\"o}hn, M.}, title = {Multimodales hochautomatisiert lernendes Assistenzsystem f{\"u}r manuelle Montageprozesse}, series = {Automation 2025}, booktitle = {Automation 2025}, publisher = {VDI Verlag}, isbn = {9783181024577}, doi = {https://doi.org/10.51202/9783181024577-575}, pages = {575 -- 590}, year = {2025}, subject = {Maschinelles Lernen}, language = {de} } @article{KandlerWinter2025, author = {Kandler, Lucas and Winter, Kai}, title = {Corporate Digital Responsibility - Vertrauen in der digitalen Wertsch{\"o}pfung}, series = {Marketing Review St. Gallen}, volume = {2025}, journal = {Marketing Review St. Gallen}, number = {4}, editor = {Reinecke, Sven}, pages = {28 -- 37}, year = {2025}, subject = {Corporate Social Responsibility}, language = {de} } @book{VilainKirchhoffKestelHeuberger2025, author = {Vilain, Michael and Kirchhoff-Kestel, Susanne and Heuberger, Matthias}, title = {Nonprofit-Organisationen im Wandel - Gestaltungsm{\"o}glichkeiten in Zeiten des Umbruchs}, editor = {Vilain, Michael and Kirchhoff-Kestel, Susanne and Heuberger, Matthias}, publisher = {Nomos}, address = {Baden-Baden}, isbn = {978-3-7560-1988-5}, pages = {315}, year = {2025}, abstract = {Welche Gestaltungsm{\"o}glichkeiten und Handlungsoptionen haben Nonprofit Organisationen (NPOs) in Zeiten des Umbruchs? Wie kann Anpassung angesichts von Megatrends wie Digitalisierung und KI, demografischem Wandel und Klimawandel unter den Bedingungen von Personal- und Finanzierungsengp{\"a}ssen gelingen? Der Tagungsband versammelt 25 Beitr{\"a}ge zu vier thematischen Schwerpunkten: Herausforderungen des gesellschaftlichen Wandels f{\"u}r NPOs, Management im Wandel, Freiwilligenarbeit und Engagement sowie Genossenschaften, Vereine und Stiftungen im Wandel. Die Beitr{\"a}ge reflektieren aus einer interdisziplin{\"a}ren Perspektive aktuelle wissenschaftliche Erkenntnisse und liefern praxisrelevante Vorschl{\"a}ge f{\"u}r Transformationsprozesse in NPOs.}, subject = {Nonprofit-Organisation}, language = {de} } @inproceedings{WeegerStiehlvonKistowskietal.2025, author = {Weeger, Nicolas and Stiehl, Annika and von Kistowski, J{\´o}akim and Geißels{\"o}der, Stefan and Uhl, Christian}, title = {Towards Practicable Machine Learning Development Using AI Engineering Blueprints}, series = {2025 IEEE 22nd International Conference on Software Architecture Companion (ICSA-C)}, booktitle = {2025 IEEE 22nd International Conference on Software Architecture Companion (ICSA-C)}, publisher = {IEEE}, doi = {10.1109/ICSA-C65153.2025.00078}, pages = {525 -- 528}, year = {2025}, abstract = {The implementation of artificial intelligence (AI) in business applications holds considerable promise for significant improvements. The development of AI systems is becoming increasingly complex, thereby underscoring the growing importance of AI engineering and MLOps techniques. Small and medium-sized enterprises (SMEs) face considerable challenges when implementing AI in their products or processes. These enterprises often lack the necessary resources and expertise to develop, deploy, and operate AI systems that are tailored to address their specific problems. Given the lack of studies on the application of AI engineering practices, particularly in the context of SMEs, this paper proposes a research plan designed to develop blueprints for the creation of proprietary machine learning (ML) models using AI engineering and MLOps practices. These blueprints enable SMEs to develop, deploy, and operate AI systems by providing reference architectures and suitable automation approaches for different types of ML. The efficacy of the blueprints is assessed through their application to a series of field projects. This process gives rise to further requirements and additional development loops for the purpose of generalization. The benefits of using the blueprints for organizations are demonstrated by observing the process of developing ML models and by conducting interviews with the developers.}, subject = {Maschinelles Lernen}, language = {en} } @article{ObmannMuenchGrafetal.2025, author = {Obmann, Dirk and M{\"u}nch, Philipp and Graf, Bernhard and von Jouanne-Diedrich, Holger and Zausig, York A.}, title = {Comparison of different AI systems for diagnosing sepsis, septic shock, and cardiogenic shock: a retrospective study}, series = {Scientific reports}, volume = {2025}, journal = {Scientific reports}, number = {15}, doi = {doi.org/10.1038/s41598-025-00830-9}, pages = {1 -- 10}, year = {2025}, abstract = {Sepsis, septic shock, and cardiogenic shock are life-threatening conditions associated with high mortality rates, but differentiating them is complex because they share certain symptoms. Using the Medical Information Mart for Intensive Care (MIMIC)-III database and artificial intelligence (AI), we aimed to increase diagnostic precision, focusing on Bayesian network classifiers (BNCs) and comparing them with other AI methods. Data from 5970 adults, including 950 patients with cardiogenic shock, 1946 patients with septic shock, and 3074 patients with sepsis, were extracted for this study. Of the original 51 variables included in the data records, 12 were selected for constructing the predictive model. The data were divided into training and validation sets at an 80:20 ratio, and the performance of the BNCs was evaluated and compared with that of other AI models, such as the one rule classifier (OneR), classification and regression tree (CART), and an artificial neural network (ANN), in terms of accuracy, sensitivity, specificity, precision, and F1-score. The BNCs exhibited an accuracy of 87.6\% to 91.5\%. The CART model demonstrated a notable 91.6\% accuracy when only three decision levels were used, whereas the intricate ANN model reached 90.5\% accuracy. Both the BNCs and the CART model allowed clear interpretation of the predictions. BNCs have the potential to be valuable tools in diagnostic tasks, with an accuracy, sensitivity, and precision comparable, in some cases, to those of ANNs while demonstrating superior interpretability.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @inproceedings{HaugSapsaiHocketal.2024, author = {Haug, Jim and Sapsai, Iryna and Hock, Isabell and Abke, J{\"o}rg and Hagel, Georg}, title = {EVALUATING AN AI-BASED ADAPTIVE LEARNING SYSTEM: GOALS, METHODS AND INITIAL RESULTS}, series = {16th International Conference on Education and New Learning Technologies, 1-3 July, 2024, Palma, Spain}, volume = {2024}, booktitle = {16th International Conference on Education and New Learning Technologies, 1-3 July, 2024, Palma, Spain}, number = {EDULEARN24 Proceedings}, editor = {Haug, Jim}, doi = {10.21125/edulearn.2024.0834}, pages = {3157 -- 3166}, year = {2024}, abstract = {The aim of this paper is to describe the evaluation process and findings of an AI-based Adaptive Learning System for the Computer Science discipline at two different German universities and discuss an array of methods in regard to assessing such a system. The primary objectives have been twofold: firstly, to examine the reception of selected learning elements, which were conceptually outlined based on relevant literature, among the student body; and secondly, to investigate the efficacy of individualized adaptive learning paths. These paths were generated by employing a variety of algorithms to analyze students learning style tendencies, with a particular emphasis on adaptive navigational techniques. The used algorithms encompassed a modified version of a literature based adaptive mechanism, an Ant-Colony-Algorithm and a Genetic Algorithm, alongside a lecturer-recommended learning path for a non-adaptive comparison. While the system suggested suitable learning paths based on student data, it never forced the individuals to give up their self-directed learning. The evaluation criteria revolved around the evolution of student motivation, interest levels, and knowledge acquisition during the time they spent working in the system. The evaluation sought to facilitate comparative analyses and assess algorithmic fitness for proficient learning path generation. The methods included both quantitative and qualitative approaches to gather data, seeking to strike a balance between being student-friendly and scientifically informative. They ranged from Likert Scale self-assessments to screen and video observations with retrospective interviews. Since the purpose of adaptive learning systems is intertwined with personalized learning it seems imperative to already take the preferences and opinions of students into account while the system is still in development. This complexity underscores the challenge of evaluating such systems, as significant constraints on student choice - though simplifying evaluation - directly oppose the ethos of individualized, self-directed learning. Initial findings suggest that the underlying theoretical considerations on sequencing and structuring of learning elements are confirmed, coupled with providing adequate flexibility to meet diverse learning needs. Cross-site evaluation of the literature-based learning elements indicated a high comprehensibility and positive student ratings. While significant positive trends were observed regarding knowledge acquisition, they cannot be definitively attributed to a specific method of learning path generation. Motivation and interest analyses show no significant differences among learning path types, albeit heavily limited by sample size. Similarly, emotion measurements, though limited, hint at positive impacts from HASKI system use. Despite limitations, early indications suggest student acceptance and potential effectiveness of learning paths, highlighting the need for larger sample sizes for validation and expansion. Ensuring alignment with student needs and user-friendly design are crucial considerations.}, subject = {E-Learning}, language = {en} } @incollection{MadsenWeidl2024, author = {Madsen, Anders L. and Weidl, Galia}, title = {Bayes'sche Netze als Methode zur Implementierung transparenter, erkl{\"a}rbarer und vertrauensw{\"u}rdiger K{\"u}nstlicher Intelligenz}, series = {Vertrauen in K{\"u}nstliche Intelligenz - Eine multi-perspektivische Betrachtung}, booktitle = {Vertrauen in K{\"u}nstliche Intelligenz - Eine multi-perspektivische Betrachtung}, publisher = {Springer Verlag}, pages = {139 -- 162}, year = {2024}, abstract = {Dieser Beitrag betrachtet die Verwendung von Bayes'schen Netzen als Methode zur Implementierung von transparenter, erkl{\"a}rbarer und vertrauensw{\"u}rdiger K{\"u}nstlicher Intelligenz (KI). Er beginnt mit einer Darstellung und Diskussion von Schl{\"u}sselkonzepten im Zusammenhang mit der Verwendung von Methoden der K{\"u}nstlichen Intelligenz und der Implementierung von erkl{\"a}rbarer und vertrauensw{\"u}rdiger KI. Der Beitrag diskutiert mehrere Konzepte aus dem Bereich der Bayes'schen Netze, die f{\"u}r die praktische Anwendung dieser Modelle in Systemen mit KI relevant sind. Zu den betrachteten Konzepten geh{\"o}ren unter anderem der Bayes-Faktor, die wahrscheinlichste Erkl{\"a}rung und die relevanteste Erkl{\"a}rung. Der Beitrag endet mit einem Fallbeispiel aus dem Bereich des automatisierten Fahrens, das veranschaulicht, wie transparente und erkl{\"a}rbare KI mithilfe von Bayes'schen Netzen umgesetzt werden kann, um eine vertrauensw{\"u}rdige L{\"o}sung zu schaffen.}, subject = {K{\"u}nstliche Intelligenz}, language = {de} } @techreport{Schork2024, type = {Working Paper}, author = {Schork, Sabrina}, title = {GenAI as a collaborative creativity booster for innovation}, doi = {10.5281/zenodo.13281421}, pages = {2}, year = {2024}, abstract = {GenAI has the potential to enhance individual creativity but may also reduce the diversity of collective content. When combined with human interaction, GenAI can lead to more creative outcomes. This is particularly relevant in collaborative work.}, subject = {Innovationsmanagement}, language = {en} } @book{OPUS4-2454, title = {Vertrauen in K{\"u}nstliche Intelligenz}, editor = {Schork, Sabrina}, publisher = {Springer Vieweg}, isbn = {978-3-658-43816-6}, doi = {https://doi.org/10.1007/978-3-658-43816-6}, pages = {VIII, 302 S.}, year = {2024}, abstract = {Der vorliegende Band markiert einen initialen Schritt zur umfassenden Er{\"o}rterung des Themenfelds ‚Vertrauen in KI' aus vielf{\"a}ltigen Blickwinkeln. Dabei wird eine Herangehensweise sowohl aus wirtschafts- und sozialwissenschaftlicher als auch aus informationstechnischer Perspektive gew{\"a}hlt, die zudem interdisziplin{\"a}re Aspekte einbezieht. Insgesamt pr{\"a}sentiert der Band f{\"u}nfzehn Beitr{\"a}ge von 25 renommierten Autorinnen und Autoren, die ihre Expertise aus vierzehn unterschiedlichen Einrichtungen einbringen. Ziel dieser Debatten ist es, Lernende, Lehrende, Forschende sowie Entscheidungstr{\"a}gerinnen und -tr{\"a}ger aus Politik und Wirtschaft dazu zu bef{\"a}higen, auf Basis von Fakten eine fundierte Meinung zu bilden. Auf dieser Grundlage sollen sie in der Lage sein, gut durchdachte pers{\"o}nliche Entscheidungen im Umgang mit KI zu treffen.}, subject = {K{\"u}nstliche Intelligenz}, language = {de} } @incollection{Roetzel2024, author = {R{\"o}tzel, Peter}, title = {K{\"u}nstliche Intelligenz (KI) - unser bester Freund? Wie Menschen auf KI-Entscheidungsempfehlungen reagieren}, series = {Vertrauen in K{\"u}nstliche Intelligenz}, booktitle = {Vertrauen in K{\"u}nstliche Intelligenz}, publisher = {Springer Fachmedien}, address = {Wiesbaden}, doi = {10.1007/978-3-658-43816-6_2}, pages = {17 -- 31}, year = {2024}, abstract = {K{\"u}nstliche Intelligenz (KI) hat sich zu einer transformativen Kraft entwickelt, die verschiedene Aspekte der t{\"a}glichen Arbeit beeinflusst. Es stellt sich die Frage: K{\"o}nnen Menschen freundschaftliche Beziehungen zu KI-Entscheidungsunterst{\"u}tzungssystemen aufbauen oder werden diese Systeme nur als Werkzeuge betrachtet? In diesem Kapitel werden die Dynamik, die Herausforderungen und die M{\"o}glichkeiten von Mensch-KI-Interaktionen (MKI) untersucht, wobei ein besonderer Fokus auf die entscheidende Rolle des Vertrauens in dieser Interaktion gelegt wird. Das Vertrauen in KI wird durch kognitive, emotionale und soziale Faktoren beeinflusst. Zu den kognitiven Faktoren geh{\"o}ren die Transparenz und Interpretierbarkeit von KI-Systemen, zu den emotionalen Faktoren geh{\"o}ren die emotionale Bindung und das Verh{\"a}ltnis zwischen Menschen und KI-Agenten und zu den sozialen Faktoren geh{\"o}ren gesellschaftliche Normen und kulturelle Einfl{\"u}sse. Das Spannungsverh{\"a}ltnis zwischen Automatisierungs- und Algorithmusvermeidungstendenzen stellt eine komplexe Herausforderung f{\"u}r MKI dar. Automatisierungsbias bedeutet, sich unhinterfragt auf KI-Empfehlungen zu verlassen. Die Tendenz zur Algorithmusvermeidung beschreibt die Ablehnung oder das {\"U}bergehen von KI-Empfehlungen zugunsten eines menschlichen Urteils. Um dieses Spannungsfeld zu bew{\"a}ltigen, m{\"u}ssen transparente und erkl{\"a}rbare KI-Systeme entwickelt und eine effektive Zusammenarbeit zwischen Menschen und KI gef{\"o}rdert werden. Durch die Ber{\"u}cksichtigung dieser Faktoren und die St{\"a}rkung des Vertrauens kann MKI zu einer informierteren Entscheidungsfindung und einer effektiven Nutzung der KI-Funktionen f{\"u}hren}, subject = {K{\"u}nstliche Intelligenz}, language = {de} } @article{SauerZindlerJumar2024, author = {Sauer, Timm and Zindler, Klaus and Jumar, Ulrich}, title = {KI-basierte Regelungskonzepte zur automatischen Spurregelung von Flurf{\"o}rderzeugen}, series = {at - Automatisierungstechnik}, volume = {72}, journal = {at - Automatisierungstechnik}, number = {4}, doi = {https://doi.org/10.1515/auto-2023-0155}, pages = {336 -- 353}, year = {2024}, abstract = {In diesem Beitrag werden zwei KI-basierte Regelungskonzepte zur automatischen Spurf{\"u}hrung von Flurf{\"o}rderzeugen (FFZ) einer heterogenen Logistikflotte vorgestellt. W{\"a}hrend die modellfreien Verfahren des best{\"a}rkenden Lernens f{\"u}r diese Anwendung als Vertreter der direkten neuronalen Regelung vorgestellt werden, l{\"a}sst sich das zweite Regelungskonzept in die Klasse der indirekten neuronalen Regelungen einordnen. Beide Konzepte zeichnen sich dadurch aus, dass sie dazu in der Lage sind, die Reglerparameter an verschiedene Varianten von FFZ anzupassen sowie vorab bekanntes Wissen bez{\"u}glich des Regelstreckenverhaltens beim Entwurf der Regler gezielt zu ber{\"u}cksichtigen.}, subject = {Flurf{\"o}rderer}, language = {de} } @inproceedings{SmeetsRoetzel2024, author = {Smeets, Mario and R{\"o}tzel, Peter}, title = {The Moderating Role of Relative Performance Information in Reducing Algorithm Aversion In The Adoption Of AI-Based Decision Support Systems in Insolvency Prediction Tasks}, series = {4th ENEAR Conference at Erasmus University Rotterdam}, volume = {4}, booktitle = {4th ENEAR Conference at Erasmus University Rotterdam}, number = {1}, pages = {1 -- 18}, year = {2024}, abstract = {The integration of Artificial Intelligence (AI) into decision-making processes emerges as a pivotal strategy for enhancing organizational performance. The paper delves into the criticality of trust in AI-based Decision Support Systems (DSSs), similar to the trust required for other (Accounting) information systems to integrate them efficiently. We explore the disruptive phenomenon known as "algorithm aversion" - a significant barrier to the trust and acceptance of AI. Although AI recommendations outperform human recommendations in different decision-making fields, there exists a tendency among individuals to underweight AI-based DSSs recommendations relative to those from human decision-makers. This underutilization is attributed to the lack of trust in AI. We conducted a laboratory experiment designed to investigate the role of AI recommendations in a workplace-related task in the field of financial accounting. The study is twofold: firstly, it examines how AI trust mediates and algorithm aversion adversely impacts decision-making performance, while also considering the moderating role of technical competence. Secondly, it investigates the potential of gamification by using means of Relative Performance Information (RPI) as a strategy to mitigate the effects of algorithm aversion. Through this experiment, we provide empirical evidence on methods to enhance decision-making performance in the context of AI recommendations. Additionally, we identify and propose counterstrategies to combat algorithm aversion, thereby facilitating the broader adoption and integration of AI-based DSSs in accounting and auditing settings. This study contributes to the accounting and auditing research community by offering insights into how AI can be more effectively incorporated into decision-making processes, addressing both psychological and technical barriers to its acceptance.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @inproceedings{SmeetsRoetzel2024, author = {Smeets, Mario and R{\"o}tzel, Peter}, title = {The Moderating Role of Non-Monetary Gamification in Reducing Algorithm Aversion in the Adoption of AI-based Decision Support Systems}, series = {ECIS - European Conference on Information Systems}, volume = {2024}, booktitle = {ECIS - European Conference on Information Systems}, number = {1}, pages = {1}, year = {2024}, abstract = {Integrating artificial intelligence (AI) into decision-making processes is key to improving organizational performance. However, trust in AI-based decision support systems (DSSs), similar to other information systems, is important for successful integration. A disruptive phenomenon, "algorithm aversion", can impede AI trust and, thus, acceptance. Although AI recommendations outperform human recommendations in different decision-making fields, individuals underweight recommendations from AI-based DSSs compared to human decision-makers due to a lack of AI trust. We conducted a lab experiment to investigate the role of AI recommendations in workplace-related tasks, first focusing on the mediating effect of AI trust and the negative impact of algorithm aversion on decision-making performance and the moderating effect of technical competence. Second, we analyzed the ability of gamification to reduce this phenomenon. We provide evidence regarding how to enhance decision-making performance when AI recommendations are deployed and identify countermeasures against algorithm aversion to facilitate the adoption of AI-based DSSs.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @book{Hofmann2023, author = {Hofmann, Georg Rainer}, title = {Vertrauen als ein Verh{\"a}ltnis zwischen Menschen und Maschinen}, editor = {Alm, Wolfgang and Hofmann, Georg Rainer}, publisher = {IMI Verlag, TH Aschaffenburg}, address = {Aschaffenburg}, isbn = {978-3-9823413-7-8}, pages = {40}, year = {2023}, abstract = {Der technische Fortschritt von IT-Systemen, wie zum Beispiel der sogenannten „K{\"u}nstlichen Intelligenz" (KI) oder den „Common Data Spaces" (CDSs), bringt die Frage nach der Akzeptanz dieser Systeme mit sich. Sowohl die aktive ({\"o}konomische) Akzeptanz in Form des Erwerbs und der Nutzung, als auch die passive (soziale) Akzeptanz in Form der Duldung der aktiven Nutzung durch Dritte, sind f{\"u}r den Erfolg von IT-Systemen ausschlaggebend. Eine geringe Akzeptanz der Systeme und ein mangelndes Vertrauen in dieselben wird in einen Zusammenhang gebracht: Man fragt nach der Gestaltung „zuverl{\"a}ssiger und vertrauensw{\"u}rdiger Systeme", da diese besonders akzeptiert werden. In diesem Beitrag werden „Vertrauen" und dessen Zusammenhang mit „Zuverl{\"a}ssigkeit" und „Verstehen" im Kontext von IT-Systemen er{\"o}rtert.}, subject = {K{\"u}nstliche Intelligenz}, language = {de} } @inproceedings{SauerGorksSpielmannetal.2023, author = {Sauer, Timm and Gorks, Manuel and Spielmann, Luca and Hepp, Nils and Zindler, Klaus and Jumar, Ulrich}, title = {AI-based control approaches for lateral vehicle guidance of industrial trucks}, series = {IFAC WC 2023 - The 22nd World Congress of the International Federation of Automatic Control 2023, Yokohama, Japan, 09.07.2023 - 14.07.2023}, booktitle = {IFAC WC 2023 - The 22nd World Congress of the International Federation of Automatic Control 2023, Yokohama, Japan, 09.07.2023 - 14.07.2023}, doi = {https://doi.org/10.1016/j.ifacol.2023.10.1501.}, year = {2023}, abstract = {Two different control concepts for the automatic track guidance of forklifts are proposed. Both approaches are based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI), and are able to take into account time-variant parameters, such as the vehicle velocity, and to reduce the influence of the path curvature, the most important disturbance variable of lateral vehicle control. In the first approach, both, the path curvature and the vehicle velocity signal, are provided to the controller in addition to the state variables of the controlled system. By varying the corresponding parameters in the training process, both signals can be considered and the control parameters can be optimized accordingly. In the second approach, several controllers (multi-model concept) considering the path curvature are used and the varying vehicle velocity is taken into account using a gain-scheduling concept. Considering time-variant vehicle parameters and the influence of the disturbance variable during operation, a stable track guidance is guaranteed within the whole speed range of the industrial trucks.}, subject = {Flurf{\"o}rderer}, language = {en} } @article{StierXuGoldetal.2024, author = {Stier, Simon P. and Xu, Xukuan and Gold, Lukas and M{\"o}ckel, Michael}, title = {Ontology-Based Battery Production Dataspace and Its Interweaving with Artificial Intelligence-Empowered Data Analytics}, series = {Energy Technology}, volume = {2024}, journal = {Energy Technology}, number = {2301305}, doi = {https://doi.org/10.1002/ente.202301305}, pages = {1 -- 13}, year = {2024}, abstract = {Heart disease, also known as cardiovascular disease, encompasses a variety of heart conditions that can result in sudden death for many people. Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method's utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia.}, subject = {Batterie}, language = {en} } @incollection{WeidlMadsenDahlquist2008, author = {Weidl, Galia and Madsen, Anders L. and Dahlquist, Erik}, title = {Decision Support on Complex Industrial Process Operation}, series = {Bayesian Networks: A Practical Guide to Applications}, booktitle = {Bayesian Networks: A Practical Guide to Applications}, publisher = {Wiley}, doi = {10.1002/9780470994559.ch18}, pages = {313 -- 328}, year = {2008}, abstract = {Introduction: A methodology for Root Cause AnalysisPulp and paper applicationThe ABB Industrial IT platformConclusion}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @article{KasparWeidlDangetal.2012, author = {Kaspar, Dietmar and Weidl, Galia and Dang, Thao and Breuel, Gabi and Tamke, Andreas and Wedel, Andreas and Rosenstiel, Wolfgang}, title = {Object-Oriented Bayesian Networks for Detection of Lane Change Maneuvers}, series = {IEEE Intelligent Transportation Systems Magazine}, volume = {2012}, journal = {IEEE Intelligent Transportation Systems Magazine}, number = {4/3}, doi = {10.1109/MITS.2012.2203229}, pages = {19 -- 31}, year = {2012}, abstract = {This article introduces a novel approach towards the recognition of typical driving maneuvers in structured highway scenarios and shows some key benefits of traffic scene modeling with object-oriented Bayesian networks (OOBNs). The approach exploits the advantages of an introduced lane-related coordinate system together with individual occupancy schedule grids for all modeled vehicles. This combination allows an efficient classification of the existing vehicle-lane and vehicle-vehicle relations in traffic scenes and thus substantially improves the understanding of complex traffic scenes. Probabilities and variances within the network are propagated systematically which results in probabilistic sets of the modeled driving maneuvers. Using this generic approach, the network is able to classify a total of 27 driving maneuvers including merging and object following.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{WeidlSinghalPetrichetal.2013, author = {Weidl, Galia and Singhal, Virat and Petrich, Dominik and Kaspar, Dietmar and Wedel, Andreas and Breuel, Gabi}, title = {Collision Risk Prediction and Warning at Road Intersections Using an Object Oriented Bayesian Network}, series = {5th International Conference Automotive User Interfaces and Interactive Vehicular Applications (Automotive UI,13), Oct.28-30, 2013}, booktitle = {5th International Conference Automotive User Interfaces and Interactive Vehicular Applications (Automotive UI,13), Oct.28-30, 2013}, doi = {10.1145/2516540.2516577}, year = {2013}, abstract = {This paper describes a novel approach to situation analysis at intersections using object-oriented Bayesian networks. The Bayesian network infers the collision probability for all vehicles approaching the intersection, while taking into account traffic rules, the digital street map, and the sensors' uncertainties. The environment perception is fused from communicated data, vehicles local perception and self-localization. Thus, a cooperatively validated set of data is obtained to characterize all objects involved in a situation (resolving occlusions). The system is tested with data, acquired by vehicles with heterogenic equipment (without/with perception). In a first step the probabilistic mapping of a vehicle onto a fixed set of traffic lanes and forward motion predictions is introduced. Second, criticality measures are evaluated for these motion predictions to infer the collision probability. In our test vehicle this probability is then used to warn the driver of a possible hazardous situation. It serves as a likelihood alarm parameter for deciding the intensity of HMI acoustic signals to direct the driver's attention. First results in various simulated and live real-time scenarios show, that a collision can be predicted up to two seconds before a possible impact by applying the developed Bayesian network. The extension of this network to further situation features is the content of ongoing research.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{WeidlMadsenKasparetal.2014, author = {Weidl, Galia and Madsen, Anders L. and Kaspar, Dietmar and Breuel, Gabi}, title = {Optimizing Bayesian networks for recognition of driving maneuvers to meet the automotive requirements}, series = {The 2014 IEEE Multi-Conference on Systems and Control (MSC 2014)}, booktitle = {The 2014 IEEE Multi-Conference on Systems and Control (MSC 2014)}, doi = {10.1109/ISIC.2014.6967630}, year = {2014}, abstract = {An Object Oriented Bayesian Network for recognition of maneuver in highway traffic has demonstrated an acceptably high recognition performance on a prototype car with a Linux PC having an i7 processor. This paper is focusing on keeping the high recognition performance of the original OOBN, while evaluating alternative modelling techniques and their impact on the memory and time requirements of an ECU-processor for automotive applications. New challenges are faced, when the prediction horizon is to be further extended.}, subject = {Fahrerassistenzsystem}, language = {en} } @article{WeidlMadsenWangetal.2018, author = {Weidl, Galia and Madsen, Anders L. and Wang, Stevens Ruixi and Kaspar, Dietmar and Karlsen, Martin}, title = {Early and Accurate Recognition of Highway Traffic Maneuvers Considering Real-World Application : A Novel Framework Using Bayesian Networks}, series = {IEEE Intelligent Transportation Systems Magazine}, volume = {2018}, journal = {IEEE Intelligent Transportation Systems Magazine}, number = {10/3}, doi = {10.1109/MITS.2018.2842049}, pages = {146 -- 158}, year = {2018}, abstract = {This paper presents a novel application of artificial cognitive systems to traffic scene understanding and early recognition of highway maneuvers. This is achieved by use of Bayesian networks for knowledge representation, to mimic the human reasoning on situation analysis and to manage inherited uncertainties in the automotive domain, that requires efficient and effective analysis of high volume and frequency data streams. The maneuver recognition uses features, analyzing the observed vehicles behavior and available free space on the target lane. Dynamic Bayesian networks (DBN) capture the motion of the own and surrounding vehicles as a dynamic process, following the trend development of lateral motion features. The static and dynamic models for maneuver recognition are statistically evaluated with real highway driving data sequences. The DBN demonstrates earlier recognition (∼ 1.1 seconds) and higher accuracy (1.12\% error rate) with more stable performance than the static models. The system is deployed on an experimental vehicle, where a Divide-and-conquer approach to inference in object-oriented Bayesian networks is introduced and its implementation is shown to require computation time of 0.15 milliseconds and ROM memory ∼ 10 2 kilobytes. The promising performance evaluation results are confirmed by test drives in real highway traffic.}, subject = {Fahrerassistenzsystem}, language = {en} } @inproceedings{RaiynWeidl2023, author = {Raiyn, Jamal and Weidl, Galia}, title = {Naturalistic Driving Studies Data Analysis Based on a Convolutional Neural Network}, series = {VEHITS 2023: 9th International Conference on Vehicle Technology and Intelligent Transport Systems}, booktitle = {VEHITS 2023: 9th International Conference on Vehicle Technology and Intelligent Transport Systems}, doi = {10.5220/0011839600003479}, year = {2023}, abstract = {The new generation of autonomous vehicles (AVs) are being designed to act autonomously and collect travel data based on various smart devices and sensors. The goal is to enable AVs to operate under their own power. Naturalistic driving studies (NDSs) collect data continuously from real traffic activities, in order not to miss any safety-critical event. In NDSs of AVs, however, the data they collect is influenced by various sources that degrade their forecasting accuracy. A convolutional neural network (CNN) is proposed to process a large amount of traffic data in different formats. A CNN can detect anomalies in traffic data that negatively affect traffic efficiency and identify the source of data anomalies, which can help reduce traffic congestion and vehicular queuing.}, subject = {Autonomes Fahrzeug}, language = {en} } @inproceedings{RaiynWeidl2023, author = {Raiyn, Jamal and Weidl, Galia}, title = {Improving Autonomous Vehicle Reasoning with Non-Monotonic Logic: Advancing Safety and Performance in Complex Environments}, series = {IEEE International Smart Cities Conference (ISC2)}, booktitle = {IEEE International Smart Cities Conference (ISC2)}, doi = {10.1109/ISC257844.2023.10293463}, year = {2023}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @inproceedings{SauerSpielmannGorksetal.2023, author = {Sauer, Timm and Spielmann, Luca and Gorks, Manuel and Zindler, Klaus and Jumar, Ulrich}, title = {Model Predictive Control of Industrial Trucks with AI-based Plant Model Selection}, series = {9th International Conference on Control, Decision and Information Technologies (CoDIT)}, volume = {2023}, booktitle = {9th International Conference on Control, Decision and Information Technologies (CoDIT)}, number = {Conference Proceedings}, doi = {10.1109/CoDIT58514.2023.10284427}, pages = {263 -- 268}, year = {2023}, abstract = {In this paper, a new control concept for automatic track guidance of forklifts within a heterogeneous logistics fleet is presented. The proposed control scheme is universally applicable and based on Artificial Intelligence (AI). The lateral control is realized using a Model Predictive Controller (MPC). In order to take into account the diversity of the industrial truck variants, a multi-model approach is used. Therefore, a plant model for each truck variant is integrated into the MPC. In a practical application, the most suitable model has to be selected. This decision is based on AI in the form of an Artificial Neural Network (ANN). It is able to distinguish the different truck variants based on that part of the plant's state vector, which represents the vehicle dynamic characteristics. Thus, the appropriate model can be used, which significantly improves the control quality and guarantees an accurate track guidance of different forklifts. Due to the AI-based selection of the model, the computational effort can be kept low and real-time capability can be ensured. In order to classify the performance of the proposed control concept, its simulation results are compared to the closed-loop behavior, using a classical MPC.}, subject = {Flurf{\"o}rderer}, language = {en} } @techreport{SchorkSchleissMahetal.2023, type = {Working Paper}, author = {Schork, Sabrina and Schleiss, Johannes and Mah, Dana-Kristin and B{\"o}hme, Katrin and Fischer, David and Mesenh{\"o}ller, Janne and Paaßen, Benjamin and Schrumpf, Johannes}, title = {K{\"u}nstliche Intelligenz in der Bildung: Drei Zukunftsszenarien und f{\"u}nf Handlungsfelder}, volume = {Diskussionspapier / M{\"a}rz 2023}, year = {2023}, abstract = {Die voranschreitende Digitalisierung und insbesondere der Einsatz von K{\"u}nstlicher Intelligenz (KI) in der Bildung er{\"o}ffnen neue M{\"o}glichkeiten des Lernens. Bildung kann st{\"a}rker individuell sowie zeitlich und r{\"a}umlich entgrenzt stattfinden. ChatGPT verdeutlicht, wie dynamisch die Entwicklungen im Bereich KI sind. Das in Zunahme begriffene Interesse am Thema KI allgemein sowie KI in der Bildung kommt dem erforderlichen Austausch zwischen zentralen Akteur:innen zugute. Dieses Diskussionspapier pr{\"a}sentiert drei Zukunftsszenarien f{\"u}r den Einsatz von KI in der institutionellen Bildung: Hochschule, Weiterbildung und Schule. Im Fokus stehen die {\"u}bergeordneten Fragestellungen nach Voraussetzungen und Gelingensbedingungen einer erfolgreichen Anwendung sowie nach gemeinsamen Handlungsfeldern f{\"u}r zentrale Stakeholder. F{\"u}nf Handlungsfelder f{\"u}r den Einsatz von KI in institutioneller Bildung sind auf Grundlage der skizzierten Zukunftsszenarien hervorzuheben: (1) Interdisziplin{\"a}re Zusammenarbeit, (2) Qualifizierungsangebote und Kompetenzentwicklung, (3) Digitale Infrastruktur und Personal, (4) Ethik und Datensouver{\"a}nit{\"a}t sowie (5) Interoperabilit{\"a}t von Daten in Bildungskontexten. Ziel des vorliegenden Diskussionspapiers ist es, den kooperativen Austausch zwischen relevanten Stakeholdern anzuregen.}, subject = {K{\"u}nstliche Intelligenz}, language = {de} } @inproceedings{RoetzelBustorffKokott2023, author = {R{\"o}tzel, Peter and Bustorff, Naomi and Kokott, Peter}, title = {Analysis of the Impact of Recommendations from Intelligent Expert Systems on Personal Trust and Decision-Making Performance}, year = {2023}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @article{SauerGorksSpielmannetal.2023, author = {Sauer, Timm and Gorks, Manuel and Spielmann, Luca and Zindler, Klaus and Jumar, Ulrich}, title = {Adaptive self-learning controllers with disturbance compensation for automatic track guidance of industrial trucks}, series = {SICE Journal of Control, Measurement and System Integration}, volume = {2023}, journal = {SICE Journal of Control, Measurement and System Integration}, number = {Vol. 16, No. 1}, doi = {10.1080/18824889.2023.2183009}, pages = {84 -- 97}, year = {2023}, abstract = {This paper presents an extended control concept for automatic track guidance of industrial trucks in intralogistic systems. It is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI). The presented approach is able to adapt itself to different industrial truck variants and to the associated specific vehicle parameters. In order to avoid starting the whole training of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a simplified linear model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step using a more complex nonlinear model, representing the real industrial truck. In this way, the controller is adapted to the actual truck variant and the corresponding parameter values. By using the nonlinear model, it can be ensured that the forklift's dynamic is approximated within the entire operating range, even at high steering angles. Moreover, the influence of the disturbance variable of the system (path curvature) is compensated by considering this a priori knowledge within the control design. Therefore, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably adjusted. In this way, the occurring path curvatures can be considered in both training steps and the control parameters can be optimized accordingly. Thus, the influence of the disturbance variable can be compensated, which significantly improves the control quality. In order to demonstrate this, the new approach is compared to an RL control concept, which is not considering the disturbance variable and to a classical two-degrees-of-freedom (2DoF) control approach.}, subject = {Flurf{\"o}rderer}, language = {en} } @article{SauerGorksSpielmannetal.2022, author = {Sauer, Timm and Gorks, Manuel and Spielmann, Luca and Zindler, Klaus}, title = {Automatische Spurf{\"u}hrung von Flurf{\"o}rderzeugen mittels KI}, series = {ATZ heavyduty}, volume = {15}, journal = {ATZ heavyduty}, number = {04/2022}, pages = {44 -- 47}, year = {2022}, abstract = {Im Verbundprojekt KAnIS forscht die TH Aschaffenburg mit Linde Material Handling an neuen KI-basierten Algorithmen zur querdynamischen Fahrzeugf{\"u}hrung von Flurf{\"o}rderzeugen. Ziel des Projekts ist die Entwicklung eines universell einsetzbaren Regelungskonzepts, das verschiedene Arten von Flurf{\"o}rderzeugen automatisch f{\"u}hrt, im Betrieb auftretenden Fahrzeugparameterschwankungen gezielt Rechnung tr{\"a}gt und dar{\"u}ber hinaus auch den jeweiligen Fitnesszustand der Fahrzeuge ber{\"u}cksichtigt.}, subject = {Flurf{\"o}rderer}, language = {de} } @phdthesis{Kress2022, author = {Kreß, Viktor}, title = {Posenbasierte Intentionserkennung von ungesch{\"u}tzten Verkehrsteilnehmern aus einem Fahrzeug}, school = {Technische Hochschule Aschaffenburg}, year = {2022}, subject = {Fahrerassistenzsystem}, language = {de} } @inproceedings{SauerGorksSpielmannetal.2022, author = {Sauer, Timm and Gorks, Manuel and Spielmann, Luca and Zindler, Klaus and Jumar, Ulrich}, title = {Automatic track guidance of industrial trucks with time-variant vehicle parameters using AI-based controllers}, series = {Advanes in Automotive Control - AAC}, volume = {2022}, booktitle = {Advanes in Automotive Control - AAC}, pages = {7}, year = {2022}, abstract = {This paper presents an extension of a self-learning control concept for automatic track guidance of industrial trucks in intralogistic systems. The presented approach is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI) and is able to adapt itself to different industrial truck variants and the associated specific vehicle parameters. Moreover, time-variant parameters during operation, such as the vehicle's velocity are taken into account. In order to consider the existing a priori knowledge of the controlled system and to avoid starting the whole training process of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a model using parameters of a nominal vehicle variant. Based on this, the control parameters are only finetuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. In order to take into account the time-variant vehicle parameters during operation, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably extended. In this way, the varying speed can be considered in both training steps and the control parameters can be optimized accordingly. Thus, in case of the investigated scenarios a stable control loop behavior can be guaranteed for the entire speed range of industrial trucks. In order to demonstrate this, the new approach is compared with a RL control concept, not considering time-variant parameters.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @inproceedings{SauerSpielmannGorksetal.2022, author = {Sauer, Timm and Spielmann, Luca and Gorks, Manuel and Zindler, Klaus and Jumar, Ulrich}, title = {Automatic track guidance of industrial trucks using AI-based controllers with disturbance compensation}, series = {61st Annual Conference of the Society of Instrument and Control Engineers (SICE), 6-9 Sept. 2022, Japan}, booktitle = {61st Annual Conference of the Society of Instrument and Control Engineers (SICE), 6-9 Sept. 2022, Japan}, year = {2022}, abstract = {This paper presents a new control concept for automatic track guidance of industrial trucks in intralogistic systems. It is based on Reinforcement Learning (RL), a method of Artificial Intelligence (AI). The presented approach is able to adapt itself to different industrial truck variants and to the associated specific vehicle parameters. In order to avoid starting the whole training of the controller for each truck variant from scratch, the training process is divided into two steps. In the first step, the controller is trained on a model using parameters of a nominal vehicle variant. Based on this, the control parameters are only fine-tuned in the second step. In this way the controller is adapted to the actual truck variant and the corresponding parameter values. Moreover, the influence of the disturbance variable of the system (path curvature) is compensated by considering this a priori knowledge within the control design. Therefore, the Artificial Neural Networks (ANN) of the RL controller and the observation vector are suitably adjusted. In this way, the occurring path curvatures can be considered in both training steps and the control parameters can be optimized accordingly. Thus, the influence of the disturbance variable can be compensated, which significanlty improves the control quality. In order to demonstrate this, the new approach is compared to a RL control concept, not considering the disturbance variable and to a classical two-degrees-of-freedom (2DoF) control approach.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @inproceedings{SauerZindlerGorksetal.2021, author = {Sauer, Timm and Zindler, Klaus and Gorks, Manuel and Spielmann, Luca and Jumar, Ulrich}, title = {Automatic track guidance of industrial trucks using self-learning controllers considering a priori plant knowledge}, series = {IEEE - 5th International Conference on Control Automation and Diagnosis (ICCAD'21), November 3-5, 2021, Grenoble, France}, booktitle = {IEEE - 5th International Conference on Control Automation and Diagnosis (ICCAD'21), November 3-5, 2021, Grenoble, France}, year = {2021}, abstract = {This paper presents a new self-learning control scheme for lateral track guidance of industrial trucks using artificial intelligence. It is an universally applicable lateral dynamic control concept which is able to adapt itself to different truck variants. Moreover it shall consider vehicle parameter variations that occur during operation, such as the load dependent change of vehicle mass and moment of inertia. The proposed approach uses Reinforcement Learning. In order to reduce the training effort, a new concept is realized, taking into account a priori knowledge of vehicle behavior. Its fundamental idea consists of dividing the training process into two steps. In the first step the controller will be pre-trained on basis of a nominal model representing a priori knowledge of lateral dynamic vehicle behavior. Since this model is derived for an industrial truck with average vehicle parameter values, a fine tuning of the control parameters has to be performed in the second step. In this way the controller is adapted to the actual truck variant and the corresponding vehicle parameter values. In order to demonstrate the efficiency of the proposed control scheme, the simulation results given in this paper are compared to the closed loop behavior using standard LQR.}, subject = {Flurf{\"o}rderer}, language = {en} } @article{SmeetsOstendorfRoetzel2021, author = {Smeets, Mario and Ostendorf, Ralf and R{\"o}tzel, Peter}, title = {AI and its Opportunities for Decision Making in an Organizational Context: A Systematic Review of the Influencing Factors on the Intention to use AI}, series = {Die Unternehmung - Swiss Journal of Business Research and Practice}, volume = {2021}, journal = {Die Unternehmung - Swiss Journal of Business Research and Practice}, number = {OnlineFirst}, pages = {1 -- 28}, year = {2021}, abstract = {One domain of application of artificial intelligence (AI) is decision support, particularly in management. Although there are already research streams examining the interaction of AI and humans (e.g. the stream on "hybrid intelligence"), there are still numerous open research gaps - for example, a comprehensive overview of which factors favor the intention to use AI is missing. By conducting a systematic literature review, we identify the factors that potentially positively influence AI usage intentions for decision-making processes in organizations. From this, we create a framework that both provides practical implications for the successful use of AI in organizational decision-making processes and delivers further research approaches, for example, on the validity/ usability of proven IS adoption models in the present context.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @incollection{Borgeest2019, author = {Borgeest, Kai}, title = {Safe Development Environments for Radiation Tracing Robots}, series = {Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics}, booktitle = {Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics}, publisher = {IGI Global}, address = {Hershey, PA}, isbn = {9781799801375}, doi = {doi.org/10.4018/978-1-7998-0137-5.ch006}, pages = {126 -- 138}, year = {2019}, abstract = {Robots can substitute for men in radioactively-contaminated areas. This is a suitable field to deploy robots for measurements, repair, or clearance, but development and test of such robots could be dangerous, because radiation sources need to be handled. To avoid these hazards in development or public demonstrations, safe alternatives to radiation samples have been sought using an already existing robot (EtaBot). One proposed solution is an optical substitution ("light follower"), the other one a fully-digital simulation of the contaminated area and the robot movement inside it using a hardware-in-the-loop simulator (HiL).}, subject = {Mobiler Roboter}, language = {en} }