Zobrazeno 1 - 10
of 97
pro vyhledávání: '"dynamic graph convolution"'
Autor:
Chien-Chou Lin, Po-Yu Chen
Publikováno v:
IEEE Access, Vol 12, Pp 111924-111931 (2024)
Recently, deep learning neural networks have been widely used in object classification. The process of object classification typically involves extracting features from the point cloud using neural networks and integrating these features into a globa
Externí odkaz:
https://doaj.org/article/e6c16ed6d50649a4aeac53e3a0f9ee8a
Autor:
Xiaoyan Zhang, Lin Feng
Publikováno v:
IEEE Access, Vol 12, Pp 70550-70558 (2024)
Aiming at the problem of poor edge effect segmentation in point cloud segmentation, which fails to fully utilize the correlation between the local geometric and semantic features of point cloud.We propose an edge-enhanced graph convolution point clou
Externí odkaz:
https://doaj.org/article/4a8545836e92413fb6eac2306bd1ff6e
Publikováno v:
Taiyuan Ligong Daxue xuebao, Vol 55, Iss 1, Pp 172-183 (2024)
Purposes Traffic flow prediction is crucial for the effective management and operation of urban transportation systems. The flows of different road sections or intersections in a traffic network change dynamically with time, meanwhile the flows of sp
Externí odkaz:
https://doaj.org/article/6cf03a7633d24ab6b1d7a9307c447a8e
Autor:
Jiange Jiang, Chen Chen, Yang Zhou, Stefano Berretti, Lei Liu, Qingqi Pei, Jianming Zhou, Shaohua Wan
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 3108-3122 (2024)
Accurate and timely flood forecasting, facilitated by remote sensing technology, is crucial to mitigate the damage and loss of life caused by floods. However, despite years of research, accurate flood prediction still faces numerous challenges, inclu
Externí odkaz:
https://doaj.org/article/5cb13d757cfc449f9bb32f4a0206bd00
Publikováno v:
Dianxin kexue, Vol 39, Pp 97-110 (2023)
Aiming at the problem that the traffic flow prediction model did not consider the correlation of road context and the dynamics of spatial dependency, a multi-channel spatial-temporal traffic flow prediction based on hybrid static-dynamic graph convol
Externí odkaz:
https://doaj.org/article/394e1c424ef94517ba3ebd5b9fc98a07
Publikováno v:
Applied Sciences, Vol 14, Iss 10, p 4130 (2024)
An essential component of autonomous transportation system management and decision-making is precise and real-time traffic flow forecast. Predicting future traffic conditionsis a difficult undertaking because of the intricate spatio-temporal relation
Externí odkaz:
https://doaj.org/article/07d5cb8d4e124f558a3f3f1a1f69ec1f
CLEP: Contrastive Learning for Epileptic Seizure Prediction Using a Spatio-Temporal-Spectral Network
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 31, Pp 3915-3926 (2023)
Seizure prediction of epileptic preictal period through electroencephalogram (EEG) signals is important for clinical epilepsy diagnosis. However, recent deep learning-based methods commonly employ intra-subject training strategy and need sufficient d
Externí odkaz:
https://doaj.org/article/3ee6e278d23443328270ab196b87da69
Publikováno v:
Symmetry, Vol 16, Iss 3, p 378 (2024)
Money laundering is an illicit activity that seeks to conceal the nature and origins of criminal proceeds, posing a substantial threat to the national economy, the political order, and social stability. To scientifically and reasonably predict money
Externí odkaz:
https://doaj.org/article/a35edd0053674530b26e4ac53e3806b9
Autor:
Haiqiang Yang, Zihan Li
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 13, Iss 2, p 34 (2024)
The objective imbalance between the taxi supply and demand exists in various areas of the city. Accurately predicting this imbalance helps taxi companies with dispatching, thereby increasing their profits and meeting the travel needs of residents. Th
Externí odkaz:
https://doaj.org/article/10e07444cf304ed7af50b37847a268d7
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