Titlebook: Algorithms and Architectures for Parallel Processing; 21st International C Yongxuan Lai,Tian Wang,Aniello Castiglione Conference proceeding

Ambulatory

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

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

宽度

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

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

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. The

MELD

MORPH

expansive

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