Support vector machine (SVM) is one of the most studied paradigms in the realm of machine
learning for classification and regression problems. It relies on vectorized input data …

For gear fault diagnosis, it is often encountered that the input samples are naturally
constructed as two-dimensional feature matrices with rich structure information. Support …

In the industrial processes, the intelligent fault diagnosis related to signal analysis and
pattern recognition is an important step to ensure the health of mechanical equipment. A …

The reproductive performance of sows is an important indicator for evaluating the economic
efficiency and production level of pigs. In this paper, we design and propose a lightweight …

Background Incorporating the time-frequency localization properties of Gabor transform
(GT), the complexity understandings of convolutional neural network (CNN), and histogram …

Background and objective: The development of deep learning has led to significant
improvements in the decoding accuracy of Motor Imagery (MI) EEG signal classification …

The motor imagery (MI) paradigm has been wildly used in brain-computer interface (BCI),
but the difficulties in performing imagery tasks limit its application. Mechanical vibration …

The recent extraordinary success of deep neural networks for electroencephalogram (EEG)
decoding can be mainly attributed to the availability of large-scale labeled datasets …

Deep stacked networks (DSNs) have shown promising performance in
electroencephalogram (EEG) pattern decoding by recursively enhancing the separability of …

The emerging matrix learning methods have achieved promising performances in
electroencephalogram (EEG) classification by exploiting the structural information between …