Ambulatory 发表于 2025-3-28 18:32:53
Versorgung und soziale Absicherung the spatio-temporal characteristic information of the one-stage detector. Experiments are carried out on MOT-17 and 2DMOT-15 which verifies that 43.27% and 63.7% improvement in tracking speed is obtained with a small accuracy compromise.JOG 发表于 2025-3-28 19:15:22
https://doi.org/10.1007/978-3-642-30926-7he Spatio-temporal characteristics of nodes in different snapshots are extracted. Finally, the similarity between nodes is calculated according to the Spatio-temporal characteristics extracted by nodes, so we propose a Spatio-temporal topology routing algorithm in opportunistic networks based on the稀释前 发表于 2025-3-29 00:32:15
http://reply.papertrans.cn/16/1531/153078/153078_43.png宽度 发表于 2025-3-29 05:50:57
https://doi.org/10.1007/978-3-662-06521-1s TCN-ATT, a temporal convolution network based on attention mechanism, to intelligent anomaly detection of wind turbine blades by combining dilation convolution, causal convolution, the skip connection of residual blocks and attention module. In this method, causal convolution and dilation convolut假装是我 发表于 2025-3-29 09:12:50
https://doi.org/10.1007/978-3-662-61172-2tensive experiments are conducted and the experimental results show that the proposed approach can derive more optimized mobile service composition with acceptable scalability compared with the traditional approach and other baselines.endure 发表于 2025-3-29 14:23:14
https://doi.org/10.1007/978-3-540-75983-6ther explore different parameter settings to optimize system performance and memory space efficiency. Finally, we implement the overall strategy as a memory library named UPM libs and integrate it into the SPDK framework. The official benchmarks, SPDK perf, are adopted to evaluate our solution. TheMELD 发表于 2025-3-29 17:28:13
http://reply.papertrans.cn/16/1531/153078/153078_47.pngMORPH 发表于 2025-3-29 21:08:57
http://reply.papertrans.cn/16/1531/153078/153078_48.pngexpansive 发表于 2025-3-30 02:14:39
CRFST-GCN: A Deeplearning Spatial-Temporal Frame to Predict Traffic Flowlution module captures the time-series relationship. Finally, it is verified on two real data sets that our proposed model effectively extracts similarities, and the results show that the model is 40 % more accurate than traditional methods during peak hours.Keratin 发表于 2025-3-30 05:46:33
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