TY - CHAP A1 - Fuhrmann, Thomas A1 - Niemetz, Michael T1 - Ideas on Digitally Supported Individualization of Teaching and Learning for Evolving Competency Requirements T2 - Ninth International Conference on Higher Education Advances (HEAd 23), València, June 19, 2023 – June 22, 2023 N2 - The world is changing rapidly, mainly due to the digitalization of all areas of living. A huge amount of information is accessible via the Internet, and since it is no longer possible for individual humans to keep track of it, artificial intelligence (AI) is analyzing this data. In this rapidly changing world, students have to be educated for a successful career during their whole working life. These boundary conditions lead to completely new challenges for the education of students that are unprecedented in this form. Digitalization in education can help to cope with these challenges but can only be a means, not a goal. Personal interaction with students remains the most important task in education to address individual weaknesses and further develop strengths and talents. With the increasing amount of openly available information and the consequently increasing diversity of experiences within the group of students, differentiation is advancing to become the key to successful education. Digitization can help with this challenging task and support communication between students and their experienced instructors. But computers cannot replace human interaction and attempts to improve teaching efficiency by replacing this communication with electronic means endangers the learning success for complex concepts. This article analyzes education demands and possibilities for digitally supported teaching and learning. KW - Digitalization KW - higher education KW - student competencies KW - differentiation Y1 - 2023 U6 - https://doi.org/10.4995/HEAd23.2023.16232 SN - 2603-5871 SP - 1399 EP - 1406 PB - Universitat Polit`ecnica de Val`encia ER - TY - CHAP A1 - Reindl, Andrea A1 - Wetzel, Daniel A1 - Niemetz, Michael A1 - Meier, Hans T1 - Leader Election in a Distributed CAN-Based Multi-Microcontroller System T2 - 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), 19-21 July 2023, Tenerife, Canary Islands, Spain N2 - In a distributed system, functionally equivalent nodes work together to form a system with improved availability, reliability and fault tolerance. Thereby, the purpose is to achieve a common control objective. As multiple components cooperate to accomplish tasks, coordination between them is required. Electing a node as the temporary leader can be a possible solution to perform coordination. This work presents a self-stabilizing algorithm for the election of a leader in dynamically reconfigurable bus topology-based broadcast systems with a message and time complexity of O(1). The election is performed dynamically, i.e., not only when the leader node fails, and is criterion-based. The criterion used is a performance related value which evaluates the properties of the node regarding the ability to perform the tasks of the leader. The increased demands on the leader are taken into account and a re-election is started when the criterion value drops below a predefined level. The goal here is to distribute the load more evenly and to reduce the probability of failure due to overload of individual nodes. For improved system availability and reduced fault rates, a management level consisting of leader, assistant and co-assistant is introduced. This reduces the number of required messages and the duration in case of non-initial election. For further reduction of required messages to uniquely determine a leader, the CAN protocol is exploited. The proposed algorithm selects a node with an improved failure rate and a reduced message and hence time complexity while satisfying the safety and termination constraints. The operation of the algorithm is validated using a hardware test setup. KW - broadcast communication KW - computational complexity KW - controller area networks KW - coordination KW - decentralized applications KW - distributed systems KW - failure analysis KW - Fault tolerance KW - fault tolerant computing KW - Fault tolerant systems KW - Hardware KW - Heuristic algorithms KW - Leader election KW - load balancing KW - Mechatronics KW - message complexity KW - microcontrollers KW - performance related election KW - probability KW - Protocols KW - self-stabilization KW - telecommunication network topology KW - Voting Y1 - 2023 SN - 979-8-3503-2297-2 U6 - https://doi.org/10.1109/ICECCME57830.2023.10252250 SP - 1 EP - 8 PB - IEEE CY - Piscataway, NJ, USA ER - TY - CHAP A1 - Reindl, Andrea A1 - Lausser, Florian A1 - Eriksson, Lars A1 - Park, Sangyoung A1 - Niemetz, Michael A1 - Meier, Hans ED - Pinker, Jiří T1 - Control Oriented Mathematical Modeling of a Bidirectional DC-DC Converter - Part 1: Buck Mode T2 - 28th International Conference on Applied Electronics (AE) 2023, Pilsen, 6-7 September 2023 N2 - Parallel connection of different batteries equipped with bidirectional DC-DC converters offers an increase of the total storage capacity, the provision of higher currents and an improvement of reliability and system availability. To share the load current among the DC-DC converters while maintaining the safe operating range of the batteries, appropriate controllers are needed. The basis for the design of these control approaches requires knowledge of both the static and dynamic characteristics of the DC-DC converter used. In this paper, the small signal analysis of a DC-DC converter in buck mode is shown using the circuit averaging technique. The paper gives an overview of all required transfer functions:. The control and line to output transfer functions for CCM and DCM relevant for average current mode control as well as for voltage control are derived and their poles and zeros are determined. This provides the basis for stability consideration, analysis of the overall control structure and controller design. KW - Analytical models KW - Average modeling KW - Batteries KW - bidirectional dc-dc converter KW - buck mode KW - circuit-averaging technique KW - continuous conduction mode KW - DC-DC power converters KW - derivation of transfer functions KW - discontinuous conduction mode KW - half-bridge KW - Mathematical models KW - Reliability KW - Signal analysis KW - small signal analysis KW - Stability analysis Y1 - 2023 SN - 979-8-3503-3554-5 U6 - https://doi.org/10.1109/AE58099.2023.10274168 SP - 1 EP - 7 PB - University of West Bohemia CY - Pilsen ER - TY - CHAP A1 - Fuhrmann, Thomas A1 - Niemetz, Michael T1 - Analysis and Improvement of Engineering Exams Toward Competence Orientation by Using an AI Chatbot T2 - Towards a Hybrid, Flexible and Socially Engaged Higher Education: Proceedings of the 26th International Conference on Interactive Collaborative Learning (ICL2023), Volume 1 N2 - ChatGPT is currently one of the most advanced general chatbots. This development leads to diverse challenges in higher education, like new forms of teaching and learning, additional exam methods, new possibilities for plagiarism, and many more topics. On the other side with the development of advanced AI tools, pure knowledge will be less and less important, and demands from industry will change toward graduates with higher competencies. Education has therefore to be changed from knowledge-centered toward competence centered. The goal of this article is to use ChatGPT for analyzing and improving the competence orientation of exams in engineering education. The authors use ChatGPT to analyze exams from different engineering subjects to evaluate the performance of this chatbot and draw conclusions about the competence orientation of the tested exams. The obtained information is used to develop ideas for increasing the competence orientation of exams. From this analysis, it is visible that ChatGPT gives good performance mainly where knowledge is tested. It has, however, much more problems with transfer questions or tasks where students need creativity or complex insights for finding new solutions. Based on this result, exams and also lectures can be optimized toward competence orientation. KW - Engineering education KW - Competence orientation KW - Exam analysis KW - AI chatbot KW - ChatGPT Y1 - 2024 SN - 9783031519789 U6 - https://doi.org/10.1007/978-3-031-51979-6_42 SN - 2367-3370 SP - 403 EP - 411 PB - Springer Nature CY - Cham ER -