H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Engineering and Technology D-index 31 Citations 3,611 108 World Ranking 5885 National Ranking 686

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Topology

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.

His most cited work include:

  • Complex network analysis of time series (177 citations)
  • Multivariate weighted complex network analysis for characterizing nonlinear dynamic behavior in two-phase flow (149 citations)
  • Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks. (128 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Artificial intelligence (34.92%)
  • Flow (38.10%)
  • Two-phase flow (33.33%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (34.92%)
  • Pattern recognition (23.02%)
  • Electroencephalography (20.63%)

In recent papers he was focusing on the following fields of study:

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.

Between 2019 and 2021, his most popular works were:

  • A GPSO-optimized convolutional neural networks for EEG-based emotion recognition (12 citations)
  • ADP-Based Robust Tracking Control for a Class of Nonlinear Systems With Unmatched Uncertainties (8 citations)
  • A Channel-fused Dense Convolutional Network for EEG-based Emotion Recognition (7 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Topology

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Complex network analysis of time series

Zhong Ke Gao;Michael Small;Jürgen Kurths;Jürgen Kurths;Jürgen Kurths.
EPL (2016)

212 Citations

Complex network from time series based on phase space reconstruction

Zhongke Gao;Ningde Jin.
Chaos (2009)

178 Citations

Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.

Zhongke Gao;Ningde Jin.
Physical Review E (2009)

173 Citations

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)

172 Citations

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)

148 Citations

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)

144 Citations

A directed weighted complex network for characterizing chaotic dynamics from time series

Zhong-Ke Gao;Ning-De Jin.
Nonlinear Analysis-real World Applications (2012)

139 Citations

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)

134 Citations

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)

130 Citations

Multiscale complex network for analyzing experimental multivariate time series

Zhong-Ke Gao;Yu-Xuan Yang;Peng-Cheng Fang;Yong Zou.
EPL (2015)

123 Citations

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Best Scientists Citing Zhong-Ke Gao

Jürgen Kurths

Jürgen Kurths

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