Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Weidong Dang"'
Publikováno v:
IEEE Access, Vol 6, Pp 65796-65802 (2018)
Brain is the most complex organ of human, which serves as the center of controlling most activities. A novel methodology called complex network is capable of characterizing the functional connectivity of human brain by means of graph theoretical meas
Externí odkaz:
https://doaj.org/article/b9aae94f548b43a3a71dec3e398ebe95
Publikováno v:
New Journal of Physics, Vol 20, Iss 12, p 125005 (2018)
Unveiling complex dynamics of natural systems from a multivariate time series represents a research hotspot in a broad variety of areas. We develop a novel multilayer network analysis framework, i.e. multivariate time-frequency multilayer network (MT
Externí odkaz:
https://doaj.org/article/cd541a7abaa7405e872789976ead253e
Publikováno v:
IEEE Sensors Journal. 22:12036-12043
Publikováno v:
IEEE Transactions on Instrumentation and Measurement. 71:1-8
Publikováno v:
IEEE Transactions on Circuits and Systems II: Express Briefs. 69:244-248
Visual evoked potential (VEP)-related EEG signals have attracted widespread attention in the construction of brain-computer interface (BCI) systems. However, long-term use of the BCI system is prone to fatigue. Effectively coping with fatigue is the
Studying Multi-Frequency Multilayer Brain Network via Deep Learning for EEG-Based Epilepsy Detection
Publikováno v:
IEEE Sensors Journal. 21:27651-27658
Epilepsy makes the patients suffer great pain and has a very bad impact on daily life. In this paper, a novel method is proposed to implement electroencephalogram (EEG)-based epilepsy detection, in which multi-frequency multilayer brain network and d
Publikováno v:
IEEE Transactions on Systems, Man, and Cybernetics: Systems. 51:7143-7151
Brain–computer interface (BCI) systems based on electroencephalography (EEG) signals have been extensively used in medical practice. To enhance the BCI performance, improving the classification accuracy of EEG signals is the key, which has always b
Publikováno v:
IEEE Transactions on Industrial Informatics. 17:6329-6336
Gas–liquid two-phase flow is of great importance in various industrial processes. How to accurately measure the flow parameters in the gas–liquid two-phase flow remains a challenging problem. In this article, we develop a novel deep learning base
Publikováno v:
IEEE Sensors Journal. 21:18123-18131
Gas-liquid two-phase flow is attracting increasing attention in industrial areas. Precise measurement of total rate and void fraction, key parameters in two-phase flows, can significantly improve the industrial efficiency, which is still a challengin
Publikováno v:
IEEE Transactions on Circuits and Systems II: Express Briefs. 67:2179-2183
Gas-liquid two-phase flow widely exists in chemical industries and natural gas industries. But characterizing the nonlinear flow behaviors underlying such flow remains a challenge of great importance. In this brief, we first carry out the vertical ga