His main research concerns Multivariate statistics, Clustering coefficient, Nonlinear system, Flow and Series. Zhong-Ke Gao has researched Multivariate statistics in several fields, including Conductance sensor, Statistical physics and Interdependent networks. His studies in Clustering coefficient integrate themes in fields like Complex system, Graph drawing, Visibility graph and Pattern recognition.
The concepts of his Nonlinear system study are interwoven with issues in Multiphase flow, Entropy, Two-phase flow and Time series. His Flow research includes elements of Structure and Data mining. His Series research is multidisciplinary, incorporating perspectives in Discrete mathematics, Variety, Chaotic and Complex network analysis.
The scientist’s investigation covers issues in Artificial intelligence, Flow, Two-phase flow, Pattern recognition and Electroencephalography. His Flow study incorporates themes from Time series, Measure, Flow conditions, Statistical physics and Series. His Time series study combines topics from a wide range of disciplines, such as Biological system and Multivariate statistics.
The Multivariate statistics study which covers Interdependent networks that intersects with Conductance sensor. His research in Statistical physics focuses on subjects like Chaotic, which are connected to Discrete mathematics. His studies deal with areas such as Fluid mechanics, Flow measurement and Nonlinear system as well as Two-phase flow.
His primary scientific interests are in Artificial intelligence, Pattern recognition, Electroencephalography, Deep learning and Convolutional neural network. He has included themes like Decoding methods and Brain–computer interface in his Artificial intelligence study. His Electroencephalography research includes themes of Multivariate statistics, Entropy, Brain network, Transition network and Convolution.
His work in Deep learning is not limited to one particular discipline; it also encompasses Flow. His study in Flow is interdisciplinary in nature, drawing from both Algorithm, Measure, Two-phase flow and Flow conditions. His Flow conditions research integrates issues from Mutual information, Series, Slug flow and Time series.
Zhong-Ke Gao mainly focuses on Artificial intelligence, Feature extraction, Pattern recognition, Convolutional neural network and Deep learning. His work deals with themes such as Channel and DEAP, Electroencephalography, which intersect with Feature extraction. Zhong-Ke Gao interconnects Evoked potential, Convolution and Network science in the investigation of issues within Electroencephalography.
The study incorporates disciplines such as Dimension, Information extraction and Generalization in addition to Convolution. His Convolutional neural network study integrates concerns from other disciplines, such as Structure, Representation and Swarm intelligence. His work on Visibility graph expands to the thematically related Deep learning.
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Complex network analysis of time series
Zhong Ke Gao;Michael Small;Jürgen Kurths;Jürgen Kurths;Jürgen Kurths.
Complex network from time series based on phase space reconstruction
Zhongke Gao;Ningde Jin.
Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.
Zhongke Gao;Ningde Jin.
Physical Review E (2009)
Multivariate weighted complex network analysis for characterizing nonlinear dynamic behavior in two-phase flow
Zhong-Ke Gao;Peng-Cheng Fang;Mei-Shuang Ding;Ning-De Jin.
Experimental Thermal and Fluid Science (2015)
EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation
Zhongke Gao;Xinmin Wang;Yuxuan Yang;Chaoxu Mu.
IEEE Transactions on Neural Networks (2019)
Visibility Graph from Adaptive Optimal Kernel Time-Frequency Representation for Classification of Epileptiform EEG.
Zhong-Ke Gao;Qing Cai;Yu-Xuan Yang;Na Dong.
International Journal of Neural Systems (2017)
A directed weighted complex network for characterizing chaotic dynamics from time series
Zhong-Ke Gao;Ning-De Jin.
Nonlinear Analysis-real World Applications (2012)
Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series
Zhong-Ke Gao;Qing Cai;Yu-Xuan Yang;Wei-Dong Dang.
Scientific Reports (2016)
Multi-frequency complex network from time series for uncovering oil-water flow structure
Zhong-Ke Gao;Yu-Xuan Yang;Peng-Cheng Fang;Ning-De Jin.
Scientific Reports (2015)
Multiscale complex network for analyzing experimental multivariate time series
Zhong-Ke Gao;Yu-Xuan Yang;Peng-Cheng Fang;Yong Zou.
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