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Qishun Mei and Xuhui Li
To address the limitations of existing methods of short-text entity disambiguation, specifically in terms of their insufficient feature extraction and reliance on massive training samples, we propose an entity disambiguation model called COLBERT, which f...
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Puti Yan, Zhen Cao, Jiangbo Peng, Chaobo Yang, Xin Yu, Penghua Qiu, Shanchun Zhang, Minghong Han, Wenbei Liu and Zuo Jiang
A flame?s structural feature is a crucial parameter required to comprehensively understand the interaction between turbulence and flames. The generation and evolution processes of the structure feature have rarely been investigated in lean blowout (LBO) ...
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Hang Li, Shengjie Zhao and Hao Deng
The extraction of community-scale green infrastructure (CSGI) poses challenges due to limited training data and the diverse scales of the targets. In this paper, we reannotate a training dataset of CSGI and propose a three-stage transfer learning method ...
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Wenqi Lyu, Wei Ke, Hao Sheng, Xiao Ma and Huayun Zhang
In response to the challenge of handling large-scale 3D point cloud data, downsampling is a common approach, yet it often leads to the problem of feature loss. We present a dynamic downsampling algorithm for 3D point cloud maps based on an improved voxel...
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Wenhao Sun, Yidong Zou, Yunhe Wang, Boyi Xiao, Haichuan Zhang and Zhihuai Xiao
In the practical production environment, the complexity and variability of hydroelectric units often result in a need for more fault data, leading to inadequate accuracy in fault identification for data-driven intelligent diagnostic models. To address th...
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