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Titlebook: Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications; 9th EAI Internationa Xuyun Zhang,Guanfeng Liu,Tao Huang Confere

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樓主: Exaltation
41#
發(fā)表于 2025-3-28 18:37:29 | 只看該作者
Cloud-Based Master Data Platform for Smart Manufacturing Processloyed to store the data emphasizing the relations between entities and Master Data Management is deployed to link the entities cross standalone databases. The efficiency of inspecting, managing and updating information across databases shall be improved by the features of the proposed platform.
42#
發(fā)表于 2025-3-28 18:57:30 | 只看該作者
43#
發(fā)表于 2025-3-28 23:21:03 | 只看該作者
44#
發(fā)表于 2025-3-29 04:50:56 | 只看該作者
Die Universit?t als organisierte Institutionef survey on the task of SISR. In general, we introduce the SR problem, some recent SR methods, public benchmark datasets and evaluation metrics. Finally, we conclude by denoting some points that could be further improved in the future.
45#
發(fā)表于 2025-3-29 09:54:37 | 只看該作者
Niklas Luhmann,Veronika Tacke,Ernst Lukas the use of the depthwise separable convolution reduces the network parameters and computational complexity in convolution operations. The experimental results on the CIFAR-10 dataset show that the proposed method improves the classification accuracy of the network while effectively compressing the network size.
46#
發(fā)表于 2025-3-29 15:17:12 | 只看該作者
47#
發(fā)表于 2025-3-29 17:02:30 | 只看該作者
48#
發(fā)表于 2025-3-29 19:49:09 | 只看該作者
A Lightweight Neural Network Combining Dilated Convolution and Depthwise Separable Convolution the use of the depthwise separable convolution reduces the network parameters and computational complexity in convolution operations. The experimental results on the CIFAR-10 dataset show that the proposed method improves the classification accuracy of the network while effectively compressing the network size.
49#
發(fā)表于 2025-3-30 02:31:58 | 只看該作者
50#
發(fā)表于 2025-3-30 04:38:21 | 只看該作者
A Multi-objective Computation Offloading Method in Multi-cloudlet Environmentecomes much difficult when there are multi-cloudlet near to the mobile users. The resources of the cloudlet are heterogeneous and finite, and thus it is challenge to choose the best cloudlet for the multi-user. The issue for multi-user in multi-cloudlet environment is well-investigated in this study
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