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pro vyhledávání: '"Shen, Jingran"'
Autor:
Huang, Zhuoyao, Zhang, Nan, Shen, Jingran, Diamantopoulos, Georgios, Hua, Zhengchang, Tziritas, Nikos, Theodoropoulos, Georgios
Digital Twin technology facilitates the monitoring and online analysis of large-scale communication networks. Faster predictions of network performance thus become imperative, especially for analysing Quality of Service (QoS) parameters in large-scal
Externí odkaz:
http://arxiv.org/abs/2405.20815
Autor:
Zhang, Nan, Vergara-Marcillo, Christian, Diamantopoulos, Georgios, Shen, Jingran, Tziritas, Nikos, Bahsoon, Rami, Theodoropoulos, Georgios
Dynamic data-driven Digital Twins (DDTs) can enable informed decision-making and provide an optimisation platform for the underlying system. By leveraging principles of Dynamic Data-Driven Applications Systems (DDDAS), DDTs can formulate computationa
Externí odkaz:
http://arxiv.org/abs/2405.14411
Publikováno v:
IIP 2024, vol 703, pp 34-47, 2024
With the rapid development of Deep Learning, more and more applications on the cloud and edge tend to utilize large DNN (Deep Neural Network) models for improved task execution efficiency as well as decision-making quality. Due to memory constraints,
Externí odkaz:
http://arxiv.org/abs/2312.06440
Publikováno v:
IEEE Access, vol. 10, pp. 86423-86434, 2022
Ridesharing has received global popularity due to its convenience and cost efficiency for both drivers and passengers and its strong potential to contribute to the implementation of the UN Sustainable Development Goals. As a result, recent years have
Externí odkaz:
http://arxiv.org/abs/2207.04709
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