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Yong Peng [3]Yongjie Peng [3]Yong G. Peng [1]Yonghong Peng [1]
  1.  34
    A New Dynamic Path Planning Approach for Unmanned Aerial Vehicles.Chenxi Huang, Yisha Lan, Yuchen Liu, Wen Zhou, Hongbin Pei, Longzhi Yang, Yongqiang Cheng, Yongtao Hao & Yonghong Peng - 2018 - Complexity 2018:1-17.
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  2.  25
    Manifold Adaptive Kernelized Low-Rank Representation for Semisupervised Image Classification.Yong Peng, Wanzeng Kong, Feiwei Qin & Feiping Nie - 2018 - Complexity 2018:1-11.
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  3.  16
    Effect of Anesthetics on Functional Connectivity of Developing Brain.Xu Chen, Xuemei Zheng, Jianghui Cai, Xiao Yang, Yonghong Lin, Mengjun Wu, Xiaofan Deng & Yong G. Peng - 2022 - Frontiers in Human Neuroscience 16.
    The potential anesthetic neurotoxicity on the neonate is an important focus of research investigation in the field of pediatric anesthesiology. It is essential to understand how these anesthetics may affect the development and growth of neonatal immature and vulnerable brains. Functional magnetic resonance imaging has suggested that using anesthetics result in reduced functional connectivity may consider as core sequence for the neurotoxicity and neurodegenerative changes in the developed brain. Anesthetics either directly impact the primary structures and functions of the brain (...)
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  4.  7
    Zhongguo zheng zhi zhe xue shi.Tao Liang, Yongjie Peng & Chunsong Gan (eds.) - 2017 - Beijing: Zhongguo ren min da xue chu ban she.
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  5.  18
    Corrigendum to “Manifold Adaptive Kernelized Low-Rank Representation for Semisupervised Image Classification”.Yong Peng, Wanzeng Kong, Feiwei Qin & Feiping Nie - 2018 - Complexity 2018:1-1.
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  6.  5
    Zhang Liwen xue pai.Yongjie Peng (ed.) - 2014 - Baoding: Hebei da xue chu ban she.
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  7. Zhu Lu zhi bian: Zhu Xi Lu Jiuyuan zhe xue bi jiao yan jiu = Zhu Lu zhi bian.Yongjie Peng - 2002 - Beijing: Ren min chu ban she.
     
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  8.  15
    Integrated Modeling, Simulation, and Visualization for Nanomaterials.Feiwei Qin, Haibin Xia, Yong Peng & Zizhao Wu - 2018 - Complexity 2018:1-16.
    Computer aided modeling and simulation of nanomaterials can describe the correlation between the material’s microstructure and its macroscopic properties quantitatively. In this paper, we propose an integrated modeling, simulation, and visualization approach for designing nanomaterials. Firstly, a fast parametric modeling method for important nanomaterials such as graphene, nanotubes, and MOFs is proposed; secondly, the material model could be edited adaptively without affecting the validity of the model on the physical level; thirdly a preliminary calculation for nanomaterials’ energy is implemented based (...)
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