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Your search keyword '"Xu, Tongge"' showing total 86 results
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86 results on '"Xu, Tongge"'

1. Identification of Key Factors Affecting the Safety Design of Aviation Piston Engine Based on Kriging-Sobol

Author

Li, Guo, Teng, Yida, Bao, Mengyao, Xu, Tongge, Wang, Zilu, Ding, Shuiting, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, and Fu, Song, editor

Published
2024
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5. An Efficient Encryption Algorithm for Perfect Forward Secrecy in Satellite Communication

Author

Murtaza, Abid, Pirzada, Syed Jahanzeb Hussain, Hasan, Muhammad Noman, Xu, Tongge, Jianwei, Liu, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Anbar, Mohammed, editor, Abdullah, Nibras, editor, and Manickam, Selvakumar, editor

Published
2020
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6. Hierarchical and Pairwise Document Embedding for Plagiarism Detection

Author

Zhang, Ruitong, Liu, Lianzhong, Zhang, Jiaofu, Huang, Zihang, Yang, Caiwei, Zhao, Liangxuan, Xu, Tongge, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, Xiaochun, editor, Wang, Chang-Dong, editor, Islam, Md. Saiful, editor, and Zhang, Zheng, editor

Published
2020
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7. High-Pressure Induced Continuous Structural Evolution of Kagome Antiferromagnet MgMn3(OH)6Cl2: A Structural Analogue to Quantum Spin Liquid Herbertsmithite

Author

Yang, Xiaoying, Xu, Tongge, Zhang, Jian, Cui, Hang, Jiang, Lina, Ma, Yanmei, and Cui, Qiliang

Abstract

MgMn3(OH)6Cl2serves readily as the classical Heisenberg kagome antiferromagnet lattice spin frustration material, due to its similarity to herbertsmithite in composition and crystal structure. In this work, nanosheets of MgMn3(OH)6Cl2are synthesized through a solid-phase reaction. Low-temperature magnetic measurements revealed two antiferromagnetic transitions, occurring at ∼8 and 55 K, respectively. Utilizing high-pressure synchrotron radiation X-ray diffraction techniques, the topological structural evolution of MgMn3(OH)6Cl2under pressures up to 24.8 GPa was investigated. The sample undergoes a second-order structural phase transition from the rhombohedral phase to the monoclinic phase at pressures exceeding 7.8 GPa. Accompanying the disappearance of the Fano-like line shape in the high-pressure Raman spectra were the emergence of new Raman active modes and discontinuities in the variations of Raman shifts in the high-frequency region. The phase transition to a structure with lower symmetry was attributed to the pressure-induced enhancement of cooperative Jahn–Teller distortion, which is caused by the mutual substitution of Mn2+ions from the kagome layer and Mg2+ions from the triangular interlayer. High-pressure ultraviolet–visible absorption measurements support the structural evolution. This research provides a robust experimental approach and physical insights for further exploration of classical geometrical frustration materials with kagome lattice.

Published
2024
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10. Preliminary study on the cultivation mode of innovative talents in the first-class cyber security college

Author

XU Tongge, LIU Lianzhong, LIU Jianwei, MAO Jian

Subjects
cyberspace security, cultivation model, academic competition, innovation and entrepreneurship, Electronic computers. Computer science, QA75.5-76.95
Abstract

Cyberspace security is a matter of national security, and the talent cultivation is of top priority. Based on the experience of developing the first-class cyber security college of Beihang University, this paper analyzes some common issues, and discusses the ideas, mechanisms and patterns of cyberspace security talents cultivation from the aspects of the talents selection, curriculum system, comprehensive training platform, practical training and academic competitions. It may provide a reference for the training of cyber security professionals.

Published
2019
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21. Semantic Social Event Evolution Discovering

Author

Huang, Zihang, primary, Liu, Lianzhong, additional, Zhang, Jiaofu, additional, Han, Lihua, additional, Wang, Shuhai, additional, Xu, Tongge, additional, Li, Yangyang, additional, Liu, Yifeng, additional, and Bhuiyan, Md Zakirul Alam, additional

Published
2021
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22. Robust Social Event Detection via Deep Clustering

Author

Zhang, Jiaofu, primary, Liu, Lianzhong, additional, Huang, Zihang, additional, Han, Lihua, additional, Wang, Shuhai, additional, Xu, Tongge, additional, Zhang, Jingyi, additional, Li, Yangyang, additional, Liu, Yifeng, additional, and Zakirul Alam Bhuiyan, Md, additional

Published
2021
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33. Machine learning‐based efficient stress intensity factor calculation for aeroengine disk probabilistic risk assessment under polynomial stress fields.

Author

Xu, Tongge, Ding, Shuiting, Zhou, Huimin, and Li, Guo

Subjects
*OPTICAL disks, *ARTIFICIAL neural networks, *KRIGING, *RISK assessment, *MACHINE learning, *POLYNOMIALS
Abstract

In probabilistic failure risk assessment, the accuracy and efficiency of the stress intensity factor calculation are important. The universal weight function method has been widely adopted for efficiency, but this method still has some debatable parts. For accurate and efficient stress intensity factor prediction, two approaches for machine learning techniques are specially designed. Three tests are conducted for the first approach where Gaussian process regression, tree‐structure models, and artificial neural network are evaluated and compared for the ability of interpolation and extrapolation. Results show that the artificial neural network and extremely randomized trees perform better. Hybrid models in the second approach are also proposed, and results show that the accuracy of SIF calculation can be improved by 5–35% compared with the weight function. Real aeroengine disks are adopted, and the errors of machine learning methods are less than 20% in the disk life calculation. [ABSTRACT FROM AUTHOR]

Published
2022
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