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Zhong-Ke Gao

Showing results (1-10 of 23) with videos related to

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Chaos (Woodbury, N.Y.)|April 3, 2017
Directed weighted network structure analysis of complex impedance measurements for characterizing oil-in-water bubbly flowZhong-Ke Gao, Wei-Dong Dang, Le Xue, et al.
Chaos (Woodbury, N.Y.)|April 3, 2017
Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behaviorZhong-Ke Gao, Wei-Dong Dang, Yu-Xuan Yang, et al.
Chaos (Woodbury, N.Y.)|November 30, 2019
A recurrence network-based convolutional neural network for fatigue driving detection from EEGZhong-Ke Gao, Yan-Li Li, Yu-Xuan Yang, et al.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|September 28, 2010
Motif distributions in phase-space networks for characterizing experimental two-phase flow patterns with chaotic featuresZhong-Ke Gao, Ning-De Jin, Wen-Xu Wang, et al.
Chaos (Woodbury, N.Y.)|February 1, 2023
Characterizing gas-liquid two-phase flow behavior using complex network and deep learningMeng-Yu Li, Rui-Qi Wang, Jian-Bo Zhang, et al.
Chaos (Woodbury, N.Y.)|June 5, 2023
Interconnected ordinal pattern complex network for characterizing the spatial coupling behavior of gas-liquid two-phase flowMeng Du, Jie Wei, Meng-Yu Li, et al.
International Journal of Neural Systems|November 12, 2016
Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEGZhong-Ke Gao, Qing Cai, Yu-Xuan Yang, et al.
International Journal of Neural Systems|February 20, 2019
Multiplex Limited Penetrable Horizontal Visibility Graph from EEG Signals for Driver Fatigue DetectionQing Cai, Zhong-Ke Gao, Yu-Xuan Yang, et al.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|October 16, 2013
Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flowZhong-Ke Gao, Xin-Wang Zhang, Ning-De Jin, et al.
Chaos (Woodbury, N.Y.)|May 16, 2025
Two-phase flow pattern transition behaviors on experimental established ordinal pattern networksMeng Du, Zhenqian Zhang, Yang Cao, et al.
Pageof 3

Showing results (1-10 of 23) with videos related to

Sort By:
Pageof 3
Chaos (Woodbury, N.Y.)|April 3, 2017
Directed weighted network structure analysis of complex impedance measurements for characterizing oil-in-water bubbly flowZhong-Ke Gao, Wei-Dong Dang, Le Xue, et al.
Chaos (Woodbury, N.Y.)|April 3, 2017
Multiplex multivariate recurrence network from multi-channel signals for revealing oil-water spatial flow behaviorZhong-Ke Gao, Wei-Dong Dang, Yu-Xuan Yang, et al.
Chaos (Woodbury, N.Y.)|November 30, 2019
A recurrence network-based convolutional neural network for fatigue driving detection from EEGZhong-Ke Gao, Yan-Li Li, Yu-Xuan Yang, et al.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|September 28, 2010
Motif distributions in phase-space networks for characterizing experimental two-phase flow patterns with chaotic featuresZhong-Ke Gao, Ning-De Jin, Wen-Xu Wang, et al.
Chaos (Woodbury, N.Y.)|February 1, 2023
Characterizing gas-liquid two-phase flow behavior using complex network and deep learningMeng-Yu Li, Rui-Qi Wang, Jian-Bo Zhang, et al.
Chaos (Woodbury, N.Y.)|June 5, 2023
Interconnected ordinal pattern complex network for characterizing the spatial coupling behavior of gas-liquid two-phase flowMeng Du, Jie Wei, Meng-Yu Li, et al.
International Journal of Neural Systems|November 12, 2016
Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEGZhong-Ke Gao, Qing Cai, Yu-Xuan Yang, et al.
International Journal of Neural Systems|February 20, 2019
Multiplex Limited Penetrable Horizontal Visibility Graph from EEG Signals for Driver Fatigue DetectionQing Cai, Zhong-Ke Gao, Yu-Xuan Yang, et al.
Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics|October 16, 2013
Multivariate recurrence network analysis for characterizing horizontal oil-water two-phase flowZhong-Ke Gao, Xin-Wang Zhang, Ning-De Jin, et al.
Chaos (Woodbury, N.Y.)|May 16, 2025
Two-phase flow pattern transition behaviors on experimental established ordinal pattern networksMeng Du, Zhenqian Zhang, Yang Cao, et al.
Pageof 3