Deep Sensor Fusion with Constraint Safety Bounds for High Precision Localization
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2024
Xianrui Yin
Positive Tensor Network Simulations of the Driven-Dissipative Bose-Hubbard Model
Masterarbeit
2024
Hanqi Huo
Transformations between fully connected and convolutional neural networks
Masterarbeit
2024
Collaboration Miner: Discovering Collaboration Petri Nets (Extended Version)
Most existing process discovery techniques aim to mine models of process orchestrations that represent behavior of cases within one business process. Collaboration process discovery techniques mine models of collaboration processes that represent behavior of collaborating cases within multiple process orchestrations that interact via collaboration concepts such as organizations, agents, and services. While workflow nets are mostly mined for process orchestrations, a standard model for collaboration processes is missing. Hence, in this work, we rely on the newly proposed collaboration Petri nets and show that in combination with the newly proposed Collaboration Miner (CM), the resulting representational bias is lower than for existing models. Moreover, CM can discover heterogeneous collaboration concepts and types such as resource sharing and message exchange, resulting in fitting and precise collaboration Petri nets. The evaluation shows that CM achieves its design goals: no assumptions on concepts and types as well as fitting and precise models, based on 26 artificial and real-world event logs from literature.
2024
INEXA: Interactive and Explainable Process Model Abstraction Through Object-Centric Process Mining
Process events are recorded by multiple information systems at different granularity levels. Based on the resulting event logs, process models are discovered at different granularity levels, as well. Events stored at a fine-grained granularity level, for example, may hinder the discovered process model to be displayed due the high number of resulting model elements. The discovered process model of a real-world manufacturing process, for example, consists of 1,489 model elements and over 2,000 arcs. Existing process model abstraction techniques could help reducing the size of the model, but would disconnect it from the underlying event log. Existing event abstraction techniques do neither support the analysis of mixed granularity levels, nor interactive exploration of a suitable granularity level. To enable the exploration of discovered process models at different granularity levels, we propose INEXA, an interactive, explainable process model abstraction method that keeps the link to the event log. As a starting point, INEXA aggregates large process models to a "displayable" size, e.g., for the manufacturing use case to a process model with 58 model elements. Then, the process analyst can explore granularity levels interactively, while applied abstractions are automatically traced in the event log for explainability.
2024
Design of a Quality Management System Based on the EU AI Act
Frontiers in Artificial Intelligence and Applications
Savelka, Jaromir;Harasta, Jakub;Novotna, Tereza;Misek, Jakub
IOS Press
2024
Philipp Kutz
Evaluating Convolutional Neural Networks in Multi-Fidelity Modeling
Masterarbeit
2024