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Auflistung nach Autor:in "Landsiedel, Olaf"

1 - 4 von 4
  • Zeitschriftenartikel
    Cross domain fusion for spatiotemporal applications: taking interdisciplinary, holistic research to the next level
    (Informatik Spektrum: Vol. 45, No. 5, 2022) Renz, Matthias; Kröger, Peer; Koschmider, Agnes; Landsiedel, Olaf; Tavares de Sousa, Nelson
    Exploiting the power of collective use of complementing data sources for the discovery of new correlations and findings offers enormous additional value compared to the summed values of isolated analysis of the individual information sources. In this article, we will introduce the concept of “cross domain fusion” (CDF) as a machine learning and pattern mining driven and multi-disciplinary research approach for fusing data and knowledge from a variety of sources enabling the discovery of answers of the question to be examined from a more complete picture. The article will give a basic introduction in this emerging field and will highlight examples of basic CDF tasks in the field of marine science.
  • Zeitschriftenartikel
    Cross domain fusion in power electronics dominated distribution grids
    (Informatik Spektrum: Vol. 45, No. 5, 2022) Sante, Pugliese; Landsiedel, Olaf; Kuprat, Johannes; Liserre, Marco
    In the near future, a drastic change in the structure of the electric grid is expected due to the increasing penetration of power electronics interfaced renewable energy sources (e.g. solar and wind), highly variable loads (e.g. electric vehicles and air conditioning) and unexpected energy demanding events (e.g. pandemics or natural disasters). Energy balancing management, voltage and frequency stability, reduced system inertia, grid resilience to fault conditions, and power quality of the supply are a few of the main challenges in the future power electronics dominated grids. Power electronics can solve these by integrating information and communication technology in new intelligent, highly reliable, and efficient devices like smart transformers. Smart transformers can increase the power flow flexibility by enabling the correct meshed-hybrid grid operations, as long as load mission and power generation profiles are known. Those profile are generally driven by heterogeneous, highly sparse and often incomplete data that belong to different domains. This article highlights the necessity of new approaches and models to identify patterns and events of interest that can serve as a common base. The resulting patterns can then be cross-fused in a common language and form the basis of further data analytics in future distribution grids.
  • Zeitschriftenartikel
    Cross-domain fusion in smart seafloor sensor networks
    (Informatik Spektrum: Vol. 45, No. 5, 2022) Zainab, Tayyaba; Karstens, Jens; Landsiedel, Olaf
    Many of the socio-economic and environmental challenges of the 21st century like the growing energy and food demand, rising sea levels and temperatures put stress on marine ecosystems and coastal populations. This requires a significant strengthening of our monitoring capacities for processes in the water column, at the seafloor and in the subsurface. However, present-day seafloor instruments and the required infrastructure to operate these are expensive and inaccessible. We envision a future Internet of Underwater Things, composed of small and cheap but intelligent underwater nodes. Each node will be equipped with sensing, communication, and computing capabilities. Building on distributed event detection and cross-domain data fusion, such an Internet of Underwater Things will enable new applications. In this paper, we argue that to make this vision a reality, we need new methodologies for resource-efficient and distributed cross-domain data fusion. Resource-efficient, distributed neural networks will serve as data-analytics pipelines to derive highly aggregated patterns of interest from raw data. These will serve as (1) a common base in time and space for fusion of heterogeneous data, and (2) be sufficiently small to be transmitted efficiently in resource-constrained settings.
  • Zeitschriftenartikel
    Quantifying the Re-identication Risk of Event Logs for Process Mining.
    (EMISA Forum: Vol. 40, No. 1, 2020) Voigt, Saskia Nuñez von; Fahrenkrog-Petersen, Stephan A.; Janssen, Dominik; Koschmider, Agnes; Tschorsch, Florian; Mannhardt, Felix; Landsiedel, Olaf; Weidlich, Matthias