His Quantum mechanics study frequently draws connections between adjacent fields such as Nonlinear system and Phase (matter). His research on Geotechnical engineering often connects related areas such as Water flow and Hydrology (agriculture). His study in Geotechnical engineering extends to Hydrology (agriculture) with its themes. His studies link Pattern recognition (psychology) with Artificial intelligence. Pattern recognition (psychology) is often connected to Artificial intelligence in his work. In most of his Flow (mathematics) studies, his work intersects topics such as Geometry. He frequently studies issues relating to Flow (mathematics) and Geometry. His study deals with a combination of Machine learning and Time series. He undertakes interdisciplinary study in the fields of Time series and Machine learning through his research.
In his works, Zhong-Ke Gao conducts interdisciplinary research on Artificial intelligence and Algorithm. Zhong-Ke Gao performs multidisciplinary study in the fields of Algorithm and Artificial intelligence via his papers. Flow (mathematics) is closely attributed to Geometry in his work. As part of his studies on Geometry, he frequently links adjacent subjects like Flow (mathematics). Many of his studies on Quantum mechanics apply to Nonlinear system as well. His Nonlinear system study frequently links to adjacent areas such as Quantum mechanics. His research on Mechanics frequently connects to adjacent areas such as Two-phase flow. His work in Two-phase flow is not limited to one particular discipline; it also encompasses Mechanics. His Machine learning study typically links adjacent topics like Multivariate statistics.
His study looks at the intersection of Decoding methods and topics like Algorithm with State (computer science). State (computer science) is closely attributed to Algorithm in his work. Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Emotion classification. Zhong-Ke Gao conducted interdisciplinary study in his works that combined Electroencephalography and Evoked potential. Zhong-Ke Gao regularly links together related areas like Artificial intelligence in his Pattern recognition (psychology) studies. He integrates Convolutional neural network with Softmax function in his study. Zhong-Ke Gao undertakes interdisciplinary study in the fields of Softmax function and Convolutional neural network through his works. In most of his Neuroscience studies, his work intersects topics such as Visual evoked potentials. His research is interdisciplinary, bridging the disciplines of Neuroscience and Visual evoked potentials.
Zhong-Ke Gao merges Artificial intelligence with Convolutional neural network in his research. Convolutional neural network and Deep learning are two areas of study in which he engages in interdisciplinary research. Deep learning and Feature extraction are two areas of study in which Zhong-Ke Gao engages in interdisciplinary research. His Electroencephalography study frequently links to related topics such as Psychiatry. Much of his study explores Psychiatry relationship to Electroencephalography. His research links Artificial intelligence with Pattern recognition (psychology). His research ties Evoked potential and Neuroscience together. Zhong-Ke Gao regularly ties together related areas like Neuroscience in his Evoked potential studies. In his study, he carries out multidisciplinary Machine learning and Feature extraction research.
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.
Complex network analysis of time series
Zhong Ke Gao;Michael Small;Jürgen Kurths;Jürgen Kurths;Jürgen Kurths.
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)
Complex network from time series based on phase space reconstruction
Zhongke Gao;Ningde Jin.
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)
Flow-pattern identification and nonlinear dynamics of gas-liquid two-phase flow in complex networks.
Zhongke Gao;Ningde Jin.
Physical Review E (2009)
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)
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)
A directed weighted complex network for characterizing chaotic dynamics from time series
Zhong-Ke Gao;Ning-De Jin.
Nonlinear Analysis-real World Applications (2012)
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.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: