TY - CHAP A1 - Mann, Michael A1 - Wenzler, Sebastian A1 - Döhring, Thorsten A1 - Stollenwerk, Manfred A1 - Pribulla, Theodor A1 - Sivanič, Peter A1 - Komžík, Richard ED - Karl Friedrich, Zieghahn T1 - Stromversorgung als Umweltfaktor: Analyse der elektrischen Netzqualität in astronomischen Observatorien T2 - Umwelteinflüsse erfassen, simulieren, bewerten KW - Netzqualität KW - Astronomische Observatorien KW - EMV KW - Erdung KW - Sternwarte KW - Elektrizitätsversorgung Y1 - 2022 SN - 978-3-9818507-7-2 VL - 2022 SP - 193 EP - 207 PB - Gesellschaft für Umweltsimulation e.V. ER - TY - CHAP A1 - Probst, Anne-Catherine A1 - Neumaier, Marcel A1 - Hufgard, Christopher A1 - Wölfel, Maximilian A1 - Schmitt, Christoph A1 - Bochtler, Ulrich T1 - Network Planning a Digital TETRA-Simulcast-System T2 - Kleinheubacher Tagung, Miltenberg, 23.09.2025 - 25.09.2025 KW - TETRA, Telekommunikation Y1 - 2025 ER - TY - CHAP A1 - Büdel, Johannes A1 - Teigelkötter, Johannes A1 - Kuhlmann, Kai T1 - Stoßstromversuche zur Qualifizierung von sicherheitsrelevanten Komponenten im Antriebsstrang eines Elektrofahrzeuges T2 - Testen und Validieren elektrischer Antriebsstränge 2023 N2 - Progress in the development of traction batteries requires high-performance test benches to qualify the safety-relevant components in the battery management system (BMS) for use in electric vehicles. Since all possible fault cases under all permissible operating conditions must be covered during qualification, only synthetic test circuits, in which a test requires only a short setup time, are used for this task. In this paper, an n-stage surge current generator for the generation of high short-circuit currents is presented. Using the example of a four-stage resonant circuit surge current converter, the advantages of this test circuit are explained and demonstrated by measurements. Furthermore, instructions for measuring the surge currents are given. KW - Elektrofahrzeug KW - Elektroantrieb Y1 - 2023 U6 - https://doi.org/doi.org/10.51202/9783181024126 ER - TY - BOOK A1 - Borgeest, Kai T1 - Messtechnik und Prüfstände für Verbrennungsmotoren N2 - Dieses Buch vermittelt sowohl Studenten, als auch Planern und Betreibern in Industrie und Wissenschaft das nötige umfangreiche Wissen, um Messungen an Motorenprüfständen durchführen zu können. Messtechnik und Prüfstände für Verbrennungsmotoren helfen, Kraftstoff einzusparen, Treibhausgase und Schadstoffe zu reduzieren, mit kleineren Motoren mehr Leistung abzugeben sowie Komponenten und Betriebsstoffe zu optimieren. Mit den Motoren und der Abgasgesetzgebung entwickelt sich auch die für die Entwicklung erforderliche mechanische, thermodynamische und Abgasmesstechnik weiter. KW - Kraftfahrzeugtechnik KW - Verbrennungsmotor KW - Messtechnik KW - Regelungstechnik KW - Verbrennungsmotor KW - Abgas KW - Messtechnik KW - Motorenprüfstand KW - Thermodynamik KW - Prüfstand Y1 - 2024 UR - https://link.springer.com/book/10.1007/978-3-658-43284-3 SN - 978-3-658-43283-6 U6 - https://doi.org/10.1007/978-3-658-43284-3 VL - 2024 PB - Springer Vieweg CY - Wiesbaden ET - 3., überarbeitete und erweiterte Auflage ER - TY - BOOK A1 - Borgeest, Kai A1 - Hu, Xingyu A1 - Wang, Yuanteng T1 - 汽车电子系统电磁兼容与功能安全 T2 - Elektromagnetische Verträglichkeit und funktionale Sicherheit von elektronischen Systemen in Kraftfahrzeugen KW - Kraftfahrzeug KW - Elektromagnetische Verträglichkeit KW - Kraftfahrzeugelektronik Y1 - 2020 SN - 9787111656234 PB - China Machine Press ER - TY - BOOK A1 - Borgeest, Kai T1 - 内燃机测量技术和试验台架 T2 - Messtechnik und Prüfstände für Verbrennungsmotoren KW - Verbrennungsmotor KW - Prüfstand KW - Verbrennungsanalyse KW - Abgasemission KW - internal combustion engines KW - engine testing KW - exhaust gas analysis KW - combustion analysis Y1 - 2023 SN - 978-7-111-71807-9 PB - China Machine Press ER - TY - CHAP A1 - Haug, Jim A1 - Sapsai, Iryna A1 - Hock, Isabell A1 - Abke, Jörg A1 - Hagel, Georg ED - Haug, Jim T1 - EVALUATING AN AI-BASED ADAPTIVE LEARNING SYSTEM: GOALS, METHODS AND INITIAL RESULTS T2 - 16th International Conference on Education and New Learning Technologies, 1-3 July, 2024, Palma, Spain N2 - 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. KW - Adaptive learning environment, tertiary education, personalized e-learning, evaluation. KW - E-Learning KW - Evaluation KW - Künstliche Intelligenz Y1 - 2024 UR - https://library.iated.org/view/HAUG2024EVA U6 - https://doi.org/10.21125/edulearn.2024.0834 VL - 2024 IS - EDULEARN24 Proceedings SP - 3157 EP - 3166 ER - TY - JOUR A1 - Sapsai, Iryna A1 - Haug, Jim A1 - Abke, Jörg A1 - Hagel, Georg T1 - IDENTIFYING STUDENT EMOTIONS IN AN ADAPTIVE LEARNING SYSTEM WITH A BAYESIAN NETWORK MODEL JF - IATED Digital Library Home N2 - Analyzing scientific literature on the impact of emotions in e-learning highlights the importance of assessing students' emotional states and their role during online learning processes. This assessment often relies on physiological sensors, which may not be accessible to all institutions or acceptable to all students. Therefore, detecting students' emotional states in an online learning environment remains a complex challenge requiring a fundamental understanding of the role of emotions in such environments. This paper explores the potential benefits of using self-reported surveys to identify students' affective states and improve their learning experience by responding to specific emotional states during their learning progress. Our objectives include identifying emotional states through surveys, pinpointing frequently selected emotion-descriptive words, analyzing survey results, and developing a Bayesian Network (BN) model for automatic emotional state analysis. This BN model aims to facilitate real-time interactions within an adaptive learning system by autonomously assessing students' emotions without direct educator intervention. We present findings from an emotion identification survey conducted among engineering students in an e-learning course provided in an adaptive learning environment. Practically, we plan to implement the BN model within the aforementioned environment to interact with students through real-time pop-up messages, identifying and responding to their emotional states. Its generic character structure allows for changes in the set of analyzed words, ensuring its broad applicability. The challenges of identifying emotions during different online learning activities and the question of the optimal word selection remain for future exploration. Effective identification of students' emotional states is expected to enhance online learning, improving emotional well-being, motivation, and individual success. KW - e-learning, Emotion Identification, Automatic Detection Techniques, Bayesian Networks. KW - Bayes-Netz KW - E-Learning Y1 - 2024 UR - https://library.iated.org/view/SAPSAI2024IDE U6 - https://doi.org/10.21125/iceri.2024.1192 VL - 2024 IS - ICERI2024 Proceedings SP - 4829 EP - 4837 ER -