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649 results on '"Li, Hai-bo"'

11. 50 Years of quantum chromodynamics: Introduction and Review

Author

Gross, Franz, Klempt, Eberhard, Brodsky, Stanley J., Buras, Andrzej J., Burkert, Volker D., Heinrich, Gudrun, Jakobs, Karl, Meyer, Curtis A., Orginos, Kostas, Strickland, Michael, Stachel, Johanna, Zanderighi, Giulia, Brambilla, Nora, Braun-Munzinger, Peter, Britzger, Daniel, Capstick, Simon, Cohen, Tom, Crede, Volker, Constantinou, Martha, Davies, Christine, Del Debbio, Luigi, Denig, Achim, DeTar, Carleton, Deur, Alexandre, Dokshitzer, Yuri, Dosch, Hans Günter, Dudek, Jozef, Dunford, Monica, Epelbaum, Evgeny, Escobedo, Miguel A., Fritzsch, Harald, Fukushima, Kenji, Gambino, Paolo, Gillberg, Dag, Gottlieb, Steven, Grafstrom, Per, Grazzini, Massimiliano, Grube, Boris, Guskov, Alexey, Iijima, Toru, Ji, Xiangdong, Karsch, Frithjof, Kluth, Stefan, Kogut, John B., Krauss, Frank, Kumano, Shunzo, Leinweber, Derek, Leutwyler, Heinrich, Li, Hai-Bo, Li, Yang, Malaescu, Bogdan, Mariotti, Chiara, Maris, Pieter, Marzani, Simone, Melnitchouk, Wally, Messchendorp, Johan, Meyer, Harvey, Mitchell, Ryan Edward, Mondal, Chandan, Nerling, Frank, Neubert, Sebastian, Pappagallo, Marco, Pastore, Saori, Peláez, José R., Puckett, Andrew, Qiu, Jianwei, Rabbertz, Klaus, Ramos, Alberto, Rossi, Patrizia, Rustamov, Anar, Schäfer, Andreas, Scherer, Stefan, Schindler, Matthias, Schramm, Steven, Shifman, Mikhail, Shuryak, Edward, Sjöstrand, Torbjörn, Sterman, George, Stewart, Iain W., Stroth, Joachim, Swanson, Eric, de Téramond, Guy F., Thoma, Ulrike, Vairo, Antonio, van Dyk, Danny, Vary, James, Virto, Javier, Vos, Marcel, Weiss, Christian, Wobisch, Markus, Wu, Sau Lan, Young, Christopher, Yuan, Feng, Zhao, Xingbo, and Zhou, Xiaorong

Published
2023
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31. Robust Landslide Recognition Using UAV Datasets: A Case Study in Baihetan Reservoir.

Author

Li, Zhi-Hai, Shi, An-Chi, Xiao, Huai-Xian, Niu, Zi-Hao, Jiang, Nan, Li, Hai-Bo, and Hu, Yu-Xiang

Subjects
DEEP learning, LANDSLIDES, SEMANTICS, BLUEPRINTS
Abstract

The task of landslide recognition focuses on extracting the location and extent of landslides over large areas, providing ample data support for subsequent landslide research. This study explores the use of UAV and deep learning technologies to achieve robust landslide recognition in a more rational, simpler, and faster manner. Specifically, the widely successful DeepLabV3+ model was used as a blueprint and a dual-encoder design was introduced to reconstruct a novel semantic segmentation model consisting of Encoder1, Encoder2, Mixer and Decoder modules. This model, named DeepLab for Landslide (DeepLab4LS), considers topographic information as a supplement to DeepLabV3+, and is expected to improve the efficiency of landslide recognition by extracting shape information from relative elevation, slope, and hillshade. Additionally, a novel loss function term—Positive Enhanced loss (PE loss)—was incorporated into the training of DeepLab4LS, significantly enhancing its ability to understand positive samples. DeepLab4LS was then applied to a UAV dataset of Baihetan reservoir, where comparative tests demonstrated its high performance in landslide recognition tasks. We found that DeepLab4LS has a stronger inference capability for landslides with less distinct boundary information, and delineates landslide boundaries more precisely. More specifically, in terms of evaluation metrics, DeepLab4LS achieved a mean intersection over union (mIoU) of 76.0% on the validation set, which is a substantial 5.5 percentage point improvement over DeepLabV3+. Moreover, the study also validated the rationale behind the dual-encoder design and the introduction of PE loss through ablation experiments. Overall, this research presents a robust semantic segmentation model for landslide recognition that considers both optical and topographic semantics of landslides, emulating the recognition pathways of human experts, and is highly suitable for landslide recognition based on UAV datasets. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Structural identification of single boron-doped graphdiynes by computational XPS and NEXAFS spectroscopy.

Author

Li, Hai-Bo, Zhang, Jun-Rong, Song, Xiu-Neng, Wang, Chuan-Kui, Hua, Weijie, and Ma, Yong

Abstract

Boron-doped graphdiyne (B-GDY) material exhibits an excellent performance in electrocatalysis, ion transport, and energy storage. However, accurately identifying the structures of B-GDY in experiments remains a challenge, hindering further selection of suitable structures with the most ideal performance for various practical applications. In the present work, we employed density functional theory (DFT) to simulate the X-ray photoelectron spectra (XPS) and near-edge X-ray absorption fine-structure (NEXAFS) spectra of pristine graphdiyne (GDY) and six representative single boron-doped graphdiynes at the B and C K-edges to establish the structure–spectroscopy relationship. A notable disparity in the C 1s ionization potentials (IPs) between substituted and adsorbed structures is observed upon doping with a boron atom. By analyzing the C and B 1s NEXAFS spectra on energy positions, spectral widths, spectral intensities, and different spectral profiles, we found that the six single boron-doped graphdiyne configurations can be sensitively identified. Moreover, this study provides a reliable theoretical reference for distinguishing different single boron-doped graphdiyne structures, enabling accurate selection of B-GDY structures for diverse practical applications. [ABSTRACT FROM AUTHOR]

Published
2024
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