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The wandering logic intelligence a hyperactive approach to network evolution and its application to adaptive mobile multimedia communications /Simeonov, Plamen L. January 2002 (has links) (PDF)
Ilmenau, Techn. University, Diss., 2002.
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Mehrwertdienste intelligenter Netze zur Realisierung der universellen, persönlichen MobilitätFröhlich, Hanno Matthias. Unknown Date (has links) (PDF)
Techn. Hochsch., Diss., 1997--Aachen.
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Multimedia networking coordination of multimedia services in next generation mobile networksGuenkova-Luy, Teodora January 2007 (has links)
Zugl.: Ulm, Univ., Diss., 2007 u.d.T.: Guenkova-Luy, Teodora: Coordination of multimedia services and applications in mobile, heterogeneous network environment
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Mobile agents in telecommunicationsBusse, Ingo. Unknown Date (has links)
Techn. University, Diss., 1999--Berlin.
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IN eine verteilte Service-Plattform mobiler Prozessarchitekturen für verkehrstelematische AnwendungenRiegelmayer, Wolfgang P. January 2006 (has links)
Zugl.: Dresden, Techn. Univ., Diss., 2006
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XML based service provisioning in converged voice and data networksCetiner, Sertac. January 2001 (has links)
Ulm, Univ., Diplomarbeit, 2001.
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Report on the first international workshop on energy data management (EnDM 2012)Pedersen, Torben Bach, Lehner, Wolfgang, Hackenbroich, Gregor 13 December 2022 (has links)
The energy sector is one of the most active application domains being forced to re-think the current practice and apply data-management based IT solutions to provide a scalable and sustainable supply and distribution of energy. Challenges range from energy production by seamlessly incorporating renewable energy resources over energy distribution and monitoring to controlling energy consumption. Decisions are based on huge amounts of empirically collected data from smart meters, new energy sources (increasingly RES - renewable energy sources such as wind, solar, hydro, thermal, etc), new distributions mechanisms (Smart Grid), and new types of consumers and devices, e.g., electric cars.
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Report on the second international workshop on energy data management (EnDM 2013)Pedersen, Torben Bach, Lehner, Wolfgang 13 December 2022 (has links)
The energy sector is in transition–being forced to rethink the current practice and apply data-management based IT solutions to provide a scalable and sustainable supply and distribution of energy. Novel challenges range from renewable energy production over energy distribution and monitoring to controlling and moving energy consumption. Huge amounts of “Big Energy Data,” i.e., data from smart meters, new renewable energy sources (RES–such as wind, solar, hydro, thermal, etc), novel distributions mechanisms (Smart Grid), and novel types of consumers and devices, e.g., electric cars, are being collected and must be managed and analyzed to yield their potential.
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Systematic Review of Deep Learning and Machine Learning for Building EnergyArdabili, Sina, Abdolalizadeh, Leila, Mako, Csaba, Torok, Bernat, Mosavi, Amir 02 February 2024 (has links)
The building energy (BE) management plays an essential role in urban sustainability and smart cities. Recently, the novel data science and data-driven technologies have shown significant progress in analyzing the energy consumption and energy demand datasets for a smarter energy management. The machine learning (ML) and deep learning (DL) methods and applications, in particular, have been promising for the advancement of accurate and high-performance energy models. The present study provides a comprehensive review of ML- and DL-based techniques applied for handling BE systems, and it further evaluates the performance of these techniques. Through a systematic review and a comprehensive taxonomy, the advances of ML and DL-based techniques are carefully investigated, and the promising models are introduced. According to the results obtained for energy demand forecasting, the hybrid and ensemble methods are located in the high-robustness range, SVM-based methods are located in good robustness limitation, ANN-based methods are located in medium-robustness limitation, and linear regression models are located in low-robustness limitations. On the other hand, for energy consumption forecasting, DL-based, hybrid, and ensemble-based models provided the highest robustness score. ANN, SVM, and single ML models provided good and medium robustness, and LR-based models provided a lower robustness score. In addition, for energy load forecasting, LR-based models provided the lower robustness score. The hybrid and ensemble-based models provided a higher robustness score. The DL-based and SVM-based techniques provided a good robustness score, and ANNbased techniques provided a medium robustness score.
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IN - eine verteilte Service-Plattform mobiler Prozeßarchitekturen für verkehrstelematische Anwendungen / IN - adistributed service platform of mobile process architectures for traffic telematic applications / traffic telematics IN (eng)Riegelmayer, Wolfgang P. 14 February 2006 (has links) (PDF)
Der Paradigmenwechsel zur Entwicklungsmethodik innerhalb verteilter Kommunikationssysteme schlägt sich auch in der Telematik zum Anwendungspotential und Systemkomplexität nieder. Dies liefert eine neue Auffasung dessen, was den transparenten Datenkanal ausmacht.
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