TY - THES A1 - Wenninger, Marc T1 - Demand Response Management Using Machine Learning Methods N2 - The worldwide transformation of electricity production from fossil and nuclear energy sources to renewable energy sources is accompanied by many challenges. One of those challenges is finding an equilibrium of supply and demand – an important balance for the stability of electric grids. Production and consumption are kept in balance by adapting electricity production to consumption. Most renewable energy sources do not produce energy when demanded, but when natural conditions are suitable. As energy cannot yet be stored efficiently, over-production is as much of a problem as underproduction. Demand Response (DR) is the means for end-users to contribute to the balancing challenge. Providing the end-users with an incentive such as time-based pricing that changes according to the supply will encourage users to contribute to the equilibrium. Users’ contribution usually has implications for their daily habits and can be associated with discomfort, thus it requires a high level of involvement. Lowering the required involvement is therefore seen as an important step toward an acceptance of time-based pricing. Since the 1980s, machine learning has been seen as a solution to lower the barrier for private households to participate. The idea is to provide households with information about their electricity consumption, make recommendations on behavior changes or take automated actions. Such information can be retrieved from monitoring a household’s electricity consumption. This thesis contributes to the process of extracting information and knowledge from monitored electricity consumption in private households using machine learning. An overview of data sources and general approaches is provided. Based on this research, the Machine Learning Demand Response Model (MLDR) is introduced, defining the relation between data, knowledge, and actions. This model enhances the understanding of the individual steps required to transform monitored electricity consumption data into individual recommendations or automated actions. These steps are: data monitoring, appliance identification, appliance usage segmentation, and appliance usage prediction. For each of these steps, this thesis provides an overview of the current research state and introduces new approaches. A new monitoring system for both individual appliances and household mains is introduced. The system was used to collect a scientific dataset called Domestic Energy Demand Dataset of Individual Appliances in Germany (DEDDIAG). It contains measurements of 50 individual appliances located in 15 homes, recorded with a sample rate of 1 Hz over a period of up to 3.5 years. The dataset has been enriched with manual appliance usage annotations as well as demographic data describing the household. The system, as well as the dataset, has been published under an open-source license. Based on this dataset, an appliance category identification algorithm is introduced. The algorithm extracts features using a wavelet transformation and classifies data using the k-Nearest-Neighbor (kNN) classifier. It was evaluated and published as a challenge baseline for DEDDIAG. Next to this approach that relies on low sample rates, a high sample rate algorithm is introduced. The algorithm is based on transforming one voltage-current cycle, known as the voltage-current (V-I) trajectory, into two separate Recurrence Plots (RPs) which are then classified using a Convolutional Neural Network (CNN) in combination with Spacial Pyramid Pooling (SPP). The al-gorithm is evaluated on three different datasets and compared to previously proposed algorithms. Finding the start and stop of an appliance is the basis for deriving usage patterns. This appliance event segmentation has received little attention from other researchers, and the most commonly used algorithm, a lower-bound thresholding approach, has never been evaluated. Using the manual annotations created for DEDDIAG, this approach is evaluated using a newly introduced performance metrics called Jaccard-Time-Span-Event-Score (JTES). Together with this, a new segmentation algorithm using Support Vector Machine (SVM) is presented. Finally, based on the usage events that were determined, a combined statistical model for appliance usage prediction is introduced. It predicts future appliance usage based on the preferred time of day and the elapsed time since it was used last. It is evaluated on the GREEND dataset as well as the DEDDIAG. The thesis concludes with an outlook of potential future work. KW - Machine Learning KW - Demand Response KW - NILM KW - Smart Grids KW - Smart Metering Y1 - 2024 U6 - https://doi.org/10.25593/open-fau-448 ER - TY - JOUR A1 - Pott, Claudia A1 - Dreischulte, Tobias A1 - Koller, Daniela A1 - Fegl, Marlene A1 - Langemeyer, Jürgen A1 - Bauer, Petra T1 - Development of an interprofessional diagnostic toolkit to enhance outside walking gait-related participation of people after stroke in Germany: study protocol of an ongoing multi-methods study JF - BMJ Open N2 - Introduction: Persons after stroke experience limitations in activities of daily living even in the chronic phase. Many patients who had a stroke report mobility limitations with loss of social roles such as reduced gait- related participation. International best- practice recommendations for patients who had a stroke include interprofessional diagnostics as a core element for goal setting and intervention planning to improve social participation. Interprofessional diagnostics has not yet been implemented in Germany. Methods and analysis: The aim is to develop an interprofessional diagnostic toolkit. This will be done in a multi- step process: first, an integrative review is conducted to synthesise the literature. Second, the experiences regarding diagnostics and walking outside is captured in focus groups with persons after stroke, relatives and health professionals. Third, a toolkit for the interprofessional diagnostic process of gait- related- participation will be developed based on the results of the previous steps in a future workshop. Fourth, the results of each work package will be integrated into the iterative development process for evaluation and implementation. All steps will be performed in accordance with the respective reporting guidelines. Ethics and dissemination: This study has been approved by the ethics committee at the Ludwig Maximilians University (LMU), Germany and is overseen by LMU-Medical Institutional Review Board. Written informed consent will be obtained from all participants. Results will be disseminated through knowledge exchange with stakeholders and in peer- reviewed journal publications, scientific conferences, formal and informal reports. Stakeholders, patients and providers will be involved in most steps of the development from the beginning, which will facilitate later implementation at a larger scale. Trial registration number: German Register Clinical Trials/Deutsches Register Klinischer Studien DRKS00032389. KW - Stroke KW - Interprofessional Collaboration KW - Diagnostic techniques and procedures KW - Community-Based Participatory Research KW - Patient-Centered Care Y1 - 2024 U6 - https://doi.org/10.1136/bmjopen-2024-084316 N1 - Wir danken Denise Leonhardt für Ihre Unterstützung. VL - 14 IS - 9 ER - TY - CHAP A1 - Oberle, Christina A1 - Sailer, Marcel ED - Zegwaard, Karsten ED - Flemming, Jenny T1 - Reducing the gap between theoretical and practical learning by means of complex simulation scenarios in health sciences. T2 - Refereed Proceedings of the 5th WACE International Research Symposium on Cooperative and Work-Integrated Education, 2024, University West, Sweden N2 - Diagnostic skills and decision-making processes are a substantial part of nursing and midwifery education. In a real-life clinical setting, the situation is complex and time-critical with a major impact on patient safety. The goal of simulation-based training allows the reduction of the complexity and offers the focus on new skills, diagnostic steps, engaging evidence-based-practice and receiving real-time feedback. According to Work-integrated-learning (WIL) it is an ideal method fostering authentic work-focused experience, students learning through doing and students discipline of professional development (Zegwaard et al., 2023). Simulation in nursing and midwifery education as a pedagogical tool has been used widely in the clinical health-care of students (WHO Regional office for Europe, 2018). Within complex scenario-based-simulation students may be engaged and immersed by working with the “patient or women” as autonomous clinicians, making their own diagnoses, clinical-decisions and evaluating their interventions (Olaussen, Heggdal & Tvedt, 2019). For the evaluating process a two-fold process that focuses on the assessment of students performance and the simulation process per se is recommended (World Health Organization, 2018). However, the research findings in competence development and learning outcomes during complex scenario-based simulations are limited in nursing (Koukourikos et al., 2021) and especially in the midwifery profession (Changuiti et al., 2021). The aim of this study was to evaluate the competence progress in midwifery-students through complex simulation scenarios. Thus to define, if the scenarios are an improved teaching and learning method. KW - work-integrated-learning KW - midwifery KW - simulation Y1 - 2024 SP - 207 EP - 213 PB - WACE Inc. CY - Waterloo, CA ER - TY - CHAP A1 - Seliger, Norbert A1 - Leirich, Nico T1 - Compliant Mechanism based Planar Inductors for Reconfigurable Magneto-Inductive Wave Devices T2 - 18th International Congress on Artificial Materials for Novel Wave Phenomena - Metamaterials 2024 N2 - A reconfigurable planar inductor based on bistable compliant mechanism is proposed. Flexible snapping of wire elements is used for modification of the loop area and self inductance as well as for the mutual coupling between laterally arranged multiple inductors in a magneto-inductive wave (MIW) device. A mechanical model is presented building the basis for an analytical inductance model. The bistable reconfigurability is demonstrated experimentally and confirmed by numerical simulation results. KW - Magneto-Inductive-Wave Device KW - Reconfigurable Inductor KW - Compliant Mechanism Y1 - 2024 SN - 979-8-3503-7349-3 SP - X-243 EP - X-245 PB - IEEE CY - Crete ER - TY - JOUR A1 - Seeber, Isabella A1 - Fleischmann, Carolin A1 - Cardon, Peter A1 - Aritz, Jolanta T1 - Fostering Psychological Safety in Global Virtual Teams: The Role of Team-Based Interventions and Digital Reminder Nudges JF - Group Decision and Negotiation N2 - Psychological safety (PS), the feeling of being comfortable to express one’s ideas or opinions in teams, is a key determinant of successful global virtual teams (GVT). Even though considerable knowledge exists about its antecedents, it is unknown how team-based interventions (TBI) and technology-based interventions (digital reminder nudges, DRN) foster PS among team members. Based on a survey involving 235 participants, our data show that TBI and DRN foster psychological safety in GVT. However, only the effect of TBI on psychological safety can be explained with a higher-quality coordination process. It remains unclear what causal mechanism explains the effect of DRN. These findings contribute to the literature on PS by showing that TBI facilitate effective coordination processes and to the literature on digital nudges by demonstrating that technology-based reminders drive PS. KW - Artificial Intelligence KW - Collaboration KW - Digital nudging KW - Global virtual teams KW - Psychological safety KW - Team intervention Y1 - 2024 U6 - https://doi.org/10.1007/s10726-024-09899-5 ER - TY - GEN A1 - Wee, K.E. A1 - Ott, Holly C. A1 - Cheah, S.M. T1 - Bottlenecks and Batching in Dragon Fruit Jam Production KW - Capacity Management Y1 - 2024 PB - Harvard Business Publishing ER - TY - GEN A1 - Wee, K.E. A1 - Ott, Holly C. A1 - Cheah, S.M. T1 - ProGlove Smart Gloves: Let’s Save Four Million Dollars a Day! KW - Digitalisation KW - Industry 4.0 Y1 - 2024 PB - Harvard Business Publishing ER - TY - RPRT A1 - Seidlmeier, Heinrich T1 - Hidden networks in business processes - theoretical considerations and experimental verification N2 - The discipline of process mining was the first to demonstrate the construction of social networks from business processes. Collaboration in the process flow creates relationships between the participants. The objective of this article is to further develop this basic idea of “hidden process-induced networks” (in short: process networks) on the basis of the findings of social network research and to make a new contribution to the management and design of business processes. A contingency network theory of processes is presented for the first time as the methodological foundation for the article. In essence, it is demonstrated how the characteristics of a process network (contingency factors) can be utilized as design variables for achieving desirable process improvements. This is followed by an experimental verification and confirmation of this theory of business process design. Finally, use cases demonstrate the practical applicability of the findings. Based on experimentally obtained data, regression analyses show that there are strong correlations between certain network measures (quantitative network characteristics) and the process measures processing time and cycle time. Processes therefore not only form networks. Networks can also support the optimization of processes. Process networks can be regarded as novel, theoretically sound and empirically verified explanatory and design models for business process management. KW - business process management KW - business process network KW - social network analysis KW - process mining KW - experimental organizational research Y1 - 2024 U6 - https://doi.org/10.13140/RG.2.2.24967.00161 ER - TY - CHAP A1 - Schäfle, Claudia A1 - Stanzel, Silke A1 - Junker, Elmar A1 - Lux, Christine T1 - PTEE 2024 – Program and Abstracts BT - 12th Physics Teaching in Engineering Education Conference TH Rosenheim, 15-17 May; Facilitating Student Learning in a Changing World N2 - The 12th International Conference on Physics Teaching in Engineering Education (PTEE 2024) took place from 15th to 17th of May, 2024 at TH Rosenheim. This “Program and Abstracts” contains the conference time schedule as well as the abstracts and video links of the 3 keynotes and the abstracts of the 46 contributions. The contributions cover actual themes in teaching engineers at universities and universities of applied sciences. Among others they are related to: SoTL (Scholarship of Teaching and Learning), influence of the pandemic on teaching, artificial intelligence in learning and teaching including learning analytics, and innovative concepts for lectures and labs in physics and engineering subjects. T3 - Proceedings of the 12th International Conference on Physics Teaching in Engineering Education PTEE 2024 - 1 KW - Active Learning KW - Active Learning Spaces KW - Artificial Intelligence and Teaching KW - Flipped Classroom KW - Hybrid Lectures KW - HyFlex KW - Learning Analytics KW - Physics Education Research KW - Physics Labs KW - Physics Teaching in Engineering Education KW - SCALE-UP rooms KW - Tutorials in Introductory Physics Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:861-opus4-25953 ER - TY - CHAP A1 - Yalamanchili, R. A1 - Schlee, S. A1 - Märkl, M. A1 - King, F. A. T1 - Enhanced Time-Optimal Filter-Based Point-to-Point Trajectory Generation with Vibration Suppression T2 - The 10th International Conference on Mechatronics and Robotics Engineering (ICMRE), Milan, Italy, 2024 KW - Vibration Suppression Y1 - 2024 ER -