About
Jianhua Zhang has been Professor in AI at Oslo Metropolitan University (OsloMet), Norway since 2018. Before joining OsloMet, he spent a stint working at a French IT company, Vekia (Lille, France) as Scientific Director. He was Professor in intelligent systems at East China U. of Science and Technology (Shanghai, China) between 2007 and 2017. He received PhD from Ruhr University Bochum, Germany and did postdoc. at University of Sheffield, UK. He was Guest Scientist at Dresden University of Technology from 2002 to 2003 and Visiting Professor at Technical University of Berlin during 2008-2015. His research interests include computational intelligence, machine learning, intelligent control, biomedical signal processing, neuroergonomics, and affective computing. He has published 4 books, 11 book chapters, and over 200 technical papers (including over 80 journal papers). He serves as Chair of IFAC TC on Human-Machine Systems, Vice Chair of IEEE Norway Section, and Vice Chair of IEEE CIS Norway Chapter. He is on editorial board of 4 international scientific journals, including Frontiers in Neuroscience, Cognitive Neurodynamics, and Cognition, Technology and Work. He was chair or keynote speaker for a number of international scientific conferences.
Fields of study
Academic disciplines
Computer technology Technical cybernetics
Subject areas
Artificial intelligence Machine learning Pattern Recognition Control technology Datamining Neuroinformatics Intelligent systems Human-machine system Computational intelligence Non Stationary signal analysis
Countries
Research groups
Publications and research
Scientific publications
Kim, Moon Keun; Cremers, Bart; Liu, Jiying; Zhang, Jianhua; Wang, Junqi (2022). Prediction and correlation analysis of ventilation performance in a residential building using artificial neural network models based on data-driven analysis. 13 p. Sustainable Cities and Society (SCS) . Vol. 83.
Hua, Yue; Zhong, Xiaolong; Zhang, Bingxue; Yin, Zhong; Zhang, Jianhua (2021). Manifold feature fusion with dynamical feature selection for cross-subject emotion recognition. 24 p. Brain Sciences . Vol. 11.
Zhang, Jianhua; Li, Jianrong (2020). Classifying Mental Workload Levels Using Semi-Supervised Learning Technique. IFAC-PapersOnLine . Vol. 53.
Zhang, Jianhua; Yin, Zhong (2020). Operator Performance Prediction based on Fuzzy Modeling Approach. IFAC-PapersOnLine . Vol. 53.
Zhang, Jianhua; Liu, Xiuling (2020). Automatic Analysis of Large-scale Nanopore Data Using Hidden Markov Models. IFAC-PapersOnLine . Vol. 53.
Zhang, Jianhua; Xia, Jiajun (2020). Hierarchical MPC of Hybrid Power Systems based on Fuzzy Discrete Abstraction. IFAC-PapersOnLine . Vol. 53.
Zhang, Jianhua; Chen, Peng (2020). Selection of Optimal EEG Electrodes for Human Emotion Recognition. IFAC-PapersOnLine . Vol. 53.
Cao, Zixuan; Yin, Zhong; Zhang, Jianhua (2020). The Mental Workload Rating Method Based on Stacking Model Fusion Strategy. Software Guide. Vol. 19.
Zhang, Jianhua; Li, Jianrong; Wang, Rubin (2020). Instantaneous mental workload assessment using time–frequency analysis and semi-supervised learning. Cognitive Neurodynamics . Vol. 14.
Zheng, Zhanpeng; Yin, Zhong; Zhang, Jianhua
(2020).
An ELM-based Deep SDAE Ensemble for Inter-Subject Cognitive Workload Estimation with Physiological Signals.
Chinese Control Conference (CCC)
.
https://ieeexplore.ieee.org/document/9188806
Cao, Zixuan; Yin, Zhong; Zhang, Jianhua (2020). Recognition of cognitive load with a stacking network ensemble of denoising autoencoders and abstracted neurophysiological features. Cognitive Neurodynamics . Vol. 15.
Zhang, Jianhua; Yin, Zhong; Chen, Peng; Nichele, Stefano (2020). Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Information Fusion . Vol. 59.
Zhong, Xiaolong; Yin, Zhong; Zhang, Jianhua (2020). Cross-Subject emotion recognition from EEG using Convolutional Neural Networks. Chinese Control Conference (CCC) . Vol. 2020-.
Pontes-Filho, Sidney; Lind, Pedro; Yazidi, Anis; Zhang, Jianhua; Hammer, Hugo Lewi; Mello, Gustavo; Sandvig, Ioanna; Tufte, Gunnar; Nichele, Stefano
(2020).
EvoDynamic: A Framework for the Evolution of Generally Represented Dynamical Systems and Its Application to Criticality.
Castillo, Pedro A.; Laredo, Juan Luis Jiménez; de Vega, Francisco Fernández (Ed.).
Applications of Evolutionary Computation. Mangler. p. 133-148.
Springer.
https://hdl.handle.net/11250/2724664
Pontes-Filho, Sidney; Lind, Pedro; Yazidi, Anis; Zhang, Jianhua; Hammer, Hugo Lewi; Mello, Gustavo; Sandvig, Ioanna; Tufte, Gunnar; Nichele, Stefano
(2020).
A neuro-inspired general framework for the evolution of stochastic dynamical systems: Cellular automata, random Boolean networks and echo state networks towards criticality.
Cognitive Neurodynamics
.
https://hdl.handle.net/11250/2657791
Zhang, Jianhua; Li, Jianrong; Nichele, Stefano (2020). Instantaneous Mental Workload Recognition Using Wavelet-Packet Decomposition and Semi-Supervised Learning. Huang, Tingwen (Ed.). Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI2019). KAPITTEL. p. 410-416. IEEE. 10.1109/SSCI44817.2019.9002997
Zhang, Jianhua; Chen, Peng; Nichele, Stefano; Yazidi, Anis (2020). Emotion Recognition Using Time-frequency Analysis of EEG Signals and Machine Learning. Huang, Tingwen (Ed.). Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI2019). KAPITTEL. p. 403-408. IEEE. 10.1109/SSCI44817.2019.9003057
Gu, Hao; Yin, Zhong; Zhang, Jianhua (2019). EEG based mental workload assessment via a hybrid classifier of ELM and SVM. Liu, Zhixin; Qu, Ying (Ed.). The 38th Chinese Control Conference (CCC2019). paper in conf. Proceedings. p. 8398-8403. IEEE conference proceedings.
Zhang, Jianhua; Li, Jianrong (2019). Multi-modal Recognition of Mental Workload Using Empirical Mode Decomposition and Semi-Supervised Learning. Toyoura, Masahiro (Ed.). Proc of 2019 International Conference on Cyberworlds (CW). Conference paper in Proceedings. p. 215-218.
Zhang, Jianhua; Ling, Chen; Li, Sunan (2019). Human Movements Classification Using Multi-Channel Surface EMG Signals and Deep Learning Technique. Toyoura, Masahiro (Ed.). Proc of 2019 International Conference on Cyberworlds (CW). conference paper in Proceedings. p. 267-273.
Zhang, Jianhua; Ling, Chen; Li, Sunan (2019). EMG Signals based Human Action Recognition via Deep Belief Networks. IFAC-PapersOnLine . Vol. 52.
Cao, Z.; Yin, Zhong; Zhang, Jianhua
(2019).
Classification of mental workload levels using EEG and hybrid model of stacked denoising autoencoder.
Proceedings of the 9th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics. KAPITTEL.
IEEE.
http://www.nichele.eu/ICDL-EPIROB_NSM/5-Cao.pdf
Pontes-Filho, Sidney; Yazidi, Anis; Zhang, Jianhua; Hammer, Hugo Lewi; Mello, Gustavo; Sandvig, Ioanna; Nichele, Stefano; Tufte, Gunnar
(2019).
A general representation of dynamical systems for reservoir computing.
Proceedings of the 9th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics. paper No. 8.
IEEE.
https://hdl.handle.net/11250/2677637
Li, J.; Zhang, Jianhua; Xia, J.; Chen, P. (2019). Mental workload classification based on semi-supervised extreme learning machine. Journal of East China University of Science and Technology (Natural Science Edition). Vol. 45.
Wen, Z.; Zhang, Jianhua; Pan, X. (2019). Quantitative Analysis of Functional Connectivity Between Prefrontal Cortex and Striatum. Journal of East China University of Science and Technology (Natural Science Edition). Vol. 45.
Tao, J.; Yin, Z.; Liu, L.; Tian, Y.; Sun, Z.; Zhang, Jianhua (2019). Individual-specific classification of mental workload levels via an ensemble heterogeneous extreme learning machine for EEG modeling. Symmetry . Vol. 11.
Yin, Zhong; Zhao, Mengyuan; Zhang, Wei; Wang, Yongxiong; Wang, Yagang; Zhang, Jianhua (2019). Physiological-signal-based mental workload estimation via transfer dynamical autoencoders in a deep learning framework. Neurocomputing . Vol. 347.
Yang, Shuo; Yin, Zhong; Wang, Yagang; Zhang, Wei; Wang, Yongxiong; Zhang, Jianhua (2019). Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders. Computers in Biology and Medicine . Vol. 109.
Wen, Zaizhi; Zhang, Jianhua; Pan, Xiaochuan; Wang, Rubin (2018). Quantitative Analysis of Functional Connectivity Between Prefrontal Cortex and Striatum in Monkey. Delgado-García, J.M.; Zhang, Jianhua (Ed.). Advances in Cognitive Neurodynamics (VI). Chapter 44. p. 351-358. Springer Nature.
Wen, Zaizhi; Zhang, Jianhua; Pan, Xiaochuan (2018). A Comparison of Reward Values Encoding Function Between the Prefrontal Cortex and Striatum in Monkey. Delgado-García, J.M.; Zhang, Jianhua (Ed.). Advances in Cognitive Neurodynamics (VI). Chapter 4. p. 27-34. Springer Nature.
