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Junfang Fan, Denghui Dou and Yi Ji
In this study, two different impact-angle-constrained guidance and control strategies using deep reinforcement learning (DRL) are proposed. The proposed strategies are based on the dual-loop and integrated guidance and control types. To address comprehen...
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Luca Scrucca
Imbalanced data present a pervasive challenge in many real-world applications of statistical and machine learning, where the instances of one class significantly outnumber those of the other. This paper examines the impact of class imbalance on the perfo...
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Jingjing Zhang, Yanlong Liu and Weidong Zhou
Adaptive sampling of the marine environment may improve the accuracy of marine numerical prediction models. This study considered adaptive sampling path optimization for a three-dimensional (3D) marine observation platform, leading to a path-planning str...
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Meng Wu and Pudong Shi
To address the problem of poor detection and under-utilization of the spatial relationship between nodes in human pose estimation, a method based on an improved spatial temporal graph convolutional network (ST-GCN) model is proposed. Firstly, upsampling ...
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Everistus Zeluwa Orji, Ali Haydar, Ibrahim Ersan and Othmar Othmar Mwambe
This paper comprehensively assesses the application of active learning strategies to enhance natural language processing-based optical character recognition (OCR) models for image-to-LaTeX conversion. It addresses the existing limitations of OCR models a...
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