[HTML][HTML] Progress in the proton exchange membrane fuel cells (PEMFCs) water/thermal management: From theory to the current challenges and real-time fault …
Abstract Proton Exchange Membrane Fuel Cells (PEMFCs) are known as a promising
alternative for internal combustion engines (ICE) to reduce pollution. Recent progress of …
alternative for internal combustion engines (ICE) to reduce pollution. Recent progress of …
The reactant starvation of the proton exchange membrane fuel cells for vehicular applications: A review
The short service life of fuel cell is a key problem that restricts the commercialization of fuel
cell vehicles. Many scholars have found that gas starvation is one of the most important …
cell vehicles. Many scholars have found that gas starvation is one of the most important …
TC-Net: A Transformer Capsule Network for EEG-based emotion recognition
Deep learning has recently achieved remarkable success in emotion recognition based on
Electroencephalogram (EEG), in which convolutional neural networks (CNNs) are the mostly …
Electroencephalogram (EEG), in which convolutional neural networks (CNNs) are the mostly …
Scalp EEG classification using deep Bi-LSTM network for seizure detection
Automatic seizure detection technology not only reduces workloads of neurologists for
epilepsy diagnosis but also is of great significance for treatments of epileptic patients. A …
epilepsy diagnosis but also is of great significance for treatments of epileptic patients. A …
HeartID: A multiresolution convolutional neural network for ECG-based biometric human identification in smart health applications
Q Zhang, D Zhou, X Zeng - Ieee Access, 2017 - ieeexplore.ieee.org
Body area networks, including smart sensors, are widely reshaping health applications in
the new era of smart cities. To meet increasing security and privacy requirements …
the new era of smart cities. To meet increasing security and privacy requirements …
EEG-based seizure prediction via Transformer guided CNN
Recently, most seizure prediction methods mainly utilize pure CNN or Transformer model,
which cannot extract local and global features simultaneously. To this end, we propose an …
which cannot extract local and global features simultaneously. To this end, we propose an …
DWT based detection of epileptic seizure from EEG signals using naive Bayes and k-NN classifiers
A Sharmila, P Geethanjali - Ieee Access, 2016 - ieeexplore.ieee.org
Electroencephalogram (EEG) comprises valuable details related to the different
physiological state of the brain. In this paper, a framework is offered for detecting the …
physiological state of the brain. In this paper, a framework is offered for detecting the …
Cognitive workload recognition using EEG signals and machine learning: A review
Machine learning and its subfield deep learning techniques provide opportunities for the
development of operator mental state monitoring, especially for cognitive workload …
development of operator mental state monitoring, especially for cognitive workload …
[图书][B] Wavelets in neuroscience
Alexander E. Hramov · Alexey A. Koronovskii · Valeri A. Makarov · Vladimir A. Maksimenko ·
Alexey N. Pavlov Page 1 Springer Series in Synergetics Alexander E. Hramov · Alexey A …
Alexey N. Pavlov Page 1 Springer Series in Synergetics Alexander E. Hramov · Alexey A …
CWT based transfer learning for motor imagery classification for brain computer interfaces
Background The processing of brain signals for Motor imagery (MI) classification to have
better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional …
better accuracy is a key issue in the Brain-Computer Interface (BCI). While conventional …