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Nannan Hu on Muck Rack

Nannan Hu

As seen in: Nature, Wiley Online Library, MDPI, Virology Journal, Science Magazine, BioRxiv, Dove Medical Press
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Articles

An F1-score-weighted ensemble of deep learning models for enhanced cloud detection in remote sensing imagery

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By Nan Ma, Lin Sun, Yanhui Guo, Nannan Hu, Shiyuan Han
| Nature Verified
Abstract Cloud detection algorithms in remote sensing imagery exploit spectral and spatial characteristics, either independently or synergistically, to facilitate the discrimination between cloud and underlying surfaces. Individual cloud detection methods manifest unique operational advantages but suffer from inherent limitations, such as bright surface misclassification and thin cloud omission, leading to unstable performance across heterogeneous landscapes.
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A triple-branch multi-scale network for real-time semantic segmentation

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By Yuefeng Zhao, Guicong Zhang, Nannan Hu, Zhiqiang Shen, Yanxuan Wang, Yuxin Song
| Nature Verified
Abstract At present, multi-scale learning is a popular and effective method to improve the accuracy of real-time semantic segmentation. However, these multi-scale methods do not consider the influence of CNN receptive field on the feature discriminability. They still suffer from the small receptive field and insufficient feature extraction, which limits the accuracy of real-time segmentation.
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Adaptive multimodal swimming gaits in a reconfigurable modular soft robotic fish

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By Adi Rotem, Sixian Qi, Aristeidis G Telonis, Bo Wang, Mengfan Xu, Nannan Hu, Wenzhuo Gao, Jie Zhang, Bo Yin, Junzhi Yu, Zhanhua Xin
| Science Magazine Verified
Abstract Fish swim with four main gaits—anguilliform, subcarangiform, carangiform, and thunniform—produced by waves along varying portions of the body. However, how muscle activation length influences swimming performance remains poorly understood. We present a reconfigurable robotic fish that replicates all four gaits in a single platform by rapidly tuning its body stiffness.
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