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30 results on '"Li, Lanxi"'

1. Computational, Label, and Data Efficiency in Deep Learning for Sparse 3D Data

Author

Li, Lanxiao

Subjects
Efficiency, 3D Data, Artificial Intelligence, Effizienz, 3D-Daten, Künstliche Intelligenz, Deep Learning, thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TH Energy technology and engineering::THR Electrical engineering
Abstract

Deep learning is widely applied to sparse 3D data to perform challenging tasks, e.g., 3D object detection and semantic segmentation. However, the high performance of deep learning comes with high costs, including computational costs and the effort to capture and label data. This work investigates and improves the efficiency of deep learning for sparse 3D data to overcome the obstacles to the further development of this technology.

Published
2024
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10. Twinning Engineering of Platinum/Iridium Nanonets as Turing-Type Catalysts for Efficient Water Splitting

Author

Gu, Jialun, primary, Li, Lanxi, additional, Yang, Qi, additional, Tian, Fubo, additional, Zhao, Wei, additional, Xie, Youneng, additional, Yu, Jinli, additional, Zhang, An, additional, Zhang, Lei, additional, Li, Hongkun, additional, Zhong, Jing, additional, Jiang, Jiali, additional, Wang, Yanju, additional, Liu, Jiahua, additional, and Lu, Jian, additional

Published
2024
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21. Evaluation of different deep convolutional neural networks for detection of broadleaf weed seedlings in wheat.

Author

Zhuang, Jiayao, Li, Xuehan, Bagavathiannan, Muthukumar, Jin, Xiaojun, Yang, Jie, Meng, Wenting, Li, Tao, Li, Lanxi, Wang, Yundi, Chen, Yong, and Yu, Jialin

Subjects
CONVOLUTIONAL neural networks, GRISELINIA littoralis, SEEDLINGS, WHEAT farming, WEEDS, WHEAT
Abstract

BACKGROUND: In‐field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for detecting broadleaf weed seedlings growing in wheat. RESULTS: The object detection neural networks, including CenterNet, Faster R‐CNN, TridenNet, VFNet, and You Only Look Once Version 3 (YOLOv3) were insufficient for weed detection in wheat because the recall never exceeded 0.58 in the testing dataset. The image classification neural networks including AlexNet, DenseNet, ResNet, and VGGNet were trained with small (5500 negative and 5500 positive images) or large training datasets (11 000 negative and 11 000 positive images) and three training image sizes (200 × 200, 300 × 300, and 400 × 400 pixels). For the small training dataset, increasing image sizes decreased the F1 scores of AlexNet and VGGNet but generally increased the F1 scores of DenseNet and ResNet. For the large training dataset, no obvious difference was detected between the training image sizes since all neural networks exhibited remarkable classification accuracies with high F1 scores (≥0.96). All image classification neural networks exhibited high F1 scores (≥0.99) when trained with the large training dataset and the training images of 200 × 200 pixels. CONCLUSION: CenterNet, Faster R‐CNN, TridentNet, VFNet, and YOLOv3 were insufficient, while AlexNet, DenseNet, ResNet, and VGGNet trained with a large training dataset were highly effective for detection of broadleaf weed seedlings in wheat. © 2021 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Published
2022
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22. Novel Bi‐Doped Amorphous SnOxNanoshells for Efficient Electrochemical CO 2 Reduction into Formate at Low Overpotentials

Author

Yang, Qi, primary, Wu, Qilong, additional, Liu, Yang, additional, Luo, Shuiping, additional, Wu, Xiaotong, additional, Zhao, Xixia, additional, Zou, Haiyuan, additional, Long, Baihua, additional, Chen, Wen, additional, Liao, Yujia, additional, Li, Lanxi, additional, Shen, Pei Kang, additional, Duan, Lele, additional, and Quan, Zewei, additional

Published
2020
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30. Novel Bi-Doped Amorphous SnO x Nanoshells for Efficient Electrochemical CO 2 Reduction into Formate at Low Overpotentials.

Author

Yang Q, Wu Q, Liu Y, Luo S, Wu X, Zhao X, Zou H, Long B, Chen W, Liao Y, Li L, Shen PK, Duan L, and Quan Z

Abstract

Engineering novel Sn-based bimetallic materials could provide intriguing catalytic properties to boost the electrochemical CO 2 reduction. Herein, the first synthesis of homogeneous Sn 1- x Bi x alloy nanoparticles (x up to 0.20) with native Bi-doped amorphous SnO x shells for efficient CO 2 reduction is reported. The Bi-SnO x nanoshells boost the production of formate with high Faradaic efficiencies (>90%) over a wide potential window (-0.67 to -0.92 V vs RHE) with low overpotentials, outperforming current tin oxide catalysts. The state-of-the-art Bi-SnO x nanoshells derived from Sn 0.80 Bi 0.20 alloy nanoparticles exhibit a great partial current density of 74.6 mA cm -2 and high Faradaic efficiency of 95.8%. The detailed electrocatalytic analyses and corresponding density functional theory calculations simultaneously reveal that the incorporation of Bi atoms into Sn species facilitates formate production by suppressing the formation of H 2 and CO., (© 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Published
2020
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