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Multi-Source Fusion with Patch-Guided Multi-Task Learning for Power Prediction of Offshore Wind Farm Clusters 认领 引用
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作者 Weijia Tang Qiang Li Ningyu Zhang 《Energy Engineering》 EI 2026年第7期371-388,共18页
Large-scale offshore wind farm clusters(OWFCs)have been increasingly connected to the power grid,and requires advanced forecasting models to enhance the prediction accuracy of OWFC's power output.This paper propos... Large-scale offshore wind farm clusters(OWFCs)have been increasingly connected to the power grid,and requires advanced forecasting models to enhance the prediction accuracy of OWFC's power output.This paper proposes a multi-source fusion with patch-guided multi-task learning for power prediction of offshore wind farm clusters.Unlike traditional graph-based approaches that rely on predefined topological relationships,which are limited in capturing the highly similar but rapidly changing meteorological conditions among closely spaced offshore farms,the proposed model employs a parameter-sharing multi-task learning network to achieves both independence and correlation among offshore wind farm clusters,followed by utilizing a dynamically weighted multi-task loss function to gradually optimize the network parameters.Moreover,the proposed model applies the patch-guided feature learning module further enables natural alignment and fusion of multi-source data.To demonstrate the performance of the proposed model,experiments were conducted on offshore wind farm clusters in three different regions.The results show that the proposed model can obtain an average accuracy improvement of around 24.31%for MAE and 19.l4%for RMSE,ensuring prediction accuracy and robustness. 展开更多
关键词 Wind farm clusters patch-based feature learning dynamic weight loss function
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FNon R-CNN:A multi-scale ground object detection and recognition network 认领 引用
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作者 Zhuo Yan Wenyuan Zhu +5 位作者 Xinyu Zhong Deyuan Zhang Chuanyun Wang Linlin Wang Xiaocong Zhang Feng Han 《High-Confidence Computing》 EI CSCD 2026年第2期98-104,共7页
With the rapid development of smart cities and intelligent transportation,traffic issues such as congestion and accidents have become increasingly critical.This paper addresses the challenge of multi-scale object dete... With the rapid development of smart cities and intelligent transportation,traffic issues such as congestion and accidents have become increasingly critical.This paper addresses the challenge of multi-scale object detection in complex scenes by proposing an improved Faster R-CNN model named FNon R-CNN.The model enhances global context modeling through integrating Non-Local Blocks into the backbone network,achieves multi-level feature fusion via Feature Pyramid Network,and optimizes training with a dynamic loss function.Experimental results on the SODA-D dataset demonstrate significant improvements in detection accuracy,particularly for small objects. 展开更多
关键词 Multi-scale object detection Faster R-CNN Non-Local Block Feature Pyramid Network Dynamic loss function
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