Self-paced dynamic infinite mixture model for fatigue evaluation of pilots' brains
Current brain cognitive models are insufficient in handling outliers and dynamics of
electroencephalogram (EEG) signals. This article presents a novel self-paced dynamic …
electroencephalogram (EEG) signals. This article presents a novel self-paced dynamic …
SCA-MADRL: Multiagent deep reinforcement learning framework based on state classification and assignment for intelligent shield attitude control
J Xu, J Bu, N Qin, D Huang - Expert Systems with Applications, 2024 - Elsevier
With the wide application of the shield tunneling method in tunnel engineering, the untimely
and incorrect attitude control of shield systems has become an essential factor affecting the …
and incorrect attitude control of shield systems has become an essential factor affecting the …
A novel tensor learning model for joint relational triplet extraction
Z Wang, H Nie, W Zheng, Y Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The relational triplet is a format to represent relational facts in the real world, which consists
of two entities and a semantic relation between these two entities. Since the relational triplet …
of two entities and a semantic relation between these two entities. Since the relational triplet …
Novel multitask conditional neural-network surrogate models for expensive optimization
Multiple-related tasks can be learned simultaneously by sharing information among tasks to
avoid tabula rasa learning and to improve performance in the no transfer case (ie, when …
avoid tabula rasa learning and to improve performance in the no transfer case (ie, when …
Tensorized LSSVMS For Multitask Regression
Multitask learning (MTL) can utilize the relatedness between multiple tasks for performance
improvement. The advent of multimodal data allows tasks to be referenced by multiple …
improvement. The advent of multimodal data allows tasks to be referenced by multiple …
Multiscale anchor box and optimized classification with faster R‐CNN for object detection
SY Wang, Z Qu - IET Image Processing, 2023 - Wiley Online Library
For the two‐stage object detector as a faster region‐convolutional neural network (Faster R‐
CNN), upgrading the accuracy of object recognition depends on the proposal box, which is …
CNN), upgrading the accuracy of object recognition depends on the proposal box, which is …
MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces
Event-related potentials (ERPs) reflect neurophysiological changes of the brain in response
to external events and their associated underlying complex spatiotemporal feature …
to external events and their associated underlying complex spatiotemporal feature …
Multi-task Heterogeneous Ensemble Learning-based Cross-Subject EEG Classification under Stroke Patients
Robot-assisted motor training is applied for neurorehabilitation in stroke patients, using
motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real …
motor imagery (MI) as a representative paradigm of brain-computer interfaces to offer real …
An efficient semi-proximal ADMM algorithm for low-rank and sparse regularized matrix minimization problems with real-world applications
With the development of technology and the arrival of the era of big data, a large number of
complex data structures have been generated, which makes matrix minimization become …
complex data structures have been generated, which makes matrix minimization become …
Joint Feature Learning for Cell Segmentation Based on Multi-scale Convolutional U-Net
A major challenge in the analysis of tissue imaging data is cell segmentation, the task of
identifying precisely the boundary of each cell in a microscopic image. The cell …
identifying precisely the boundary of each cell in a microscopic image. The cell …