Titlebook: Intelligent Distributed Computing XVI; 16th International S Michael Köhler-Bußmeier,Wolfgang Renz,Jan Sudeikat Conference proceedings 2024

Pelvic-Floor

Trust Assessment in Distributed Energy SystemsAssessing the state of distributed power systems is necessary for operational management and critical anomaly detection. Their increasing complexity, e.g., due to digitalization, sector coupling, privatization, poses major challenges and leads to still unsolved fundamental sub-problems.

异常

Why NOT Blockchain?Blockchain technology is often seen as a technology that adds value to industrial business processes in terms of security, trust and efficiency. Due to the successful development of cryptocurrencies over the past 14 years, researchers and innovators have been inspired by the technology to decentralise all kinds of business processes.

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Future Proofing Systems Engineering: Reliability, Responsibility, and ResilienceAutonomy, resource distribution, and connectedness in the context of new legislation require a rethinking of systems design and engineering. This article sheds some light on minimal requirements.

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Local Distribution Privacy in Federated Learning cannot be reconstructed by other local learners or a central server. Thus, it is the individual data points that should be private, but not the entire distribution of locally available data. In many cases, this makes sense because each data point comes from a different individual while all data poi

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Analyzing the Transformation Path of the Local Energy Infrastructurehe interaction between urban development and the local energy infrastructure and its potential to provide energy flexibility. We outline a corresponding framework and interdisciplinary research challenges.

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Regional Planning Support as a Combination of Expert Judgement, Open Data, Simulation, and Machine Lo on - each a challenging task of its own right. Consequently, regional planners tend to simplify things to be able to cope with the “explosion” of mutual inter-dependencies and the problems arising from them. However, these simplifications often are problematic and can lead to sub-optimal solutions

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Anhydrous

Solar System Object Detection in Time Series Data Using Synthetically Trained Neural Networks algorithms have been proposed. However, for the largest datasets these methods prove to be computationally expensive or even unfeasible. With our work, we explore if and how artificial neural networks can help us with this task and which network architectures are particularly suited to distinguish

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