Search Results for author:
Found 8 papers, 0 papers with code
DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic Signal Control
Then, to efficiently utilize the historical state information of the intersection, we design a sequence model with the temporal convolutional network (TCN) to capture the historical information and further merge it with the spatial information to improve its performance.
Progressive extension of reinforcement learning action dimension for asymmetric assembly tasks
Reinforcement learning (RL) is always the preferred embodiment to construct the control strategy of complex tasks, like asymmetric assembly tasks.
Feature-Based Compliance Control for Peg-in-Hole Assembly with Clearance or Interference Fit
This paper aims at solving mass precise peg-in-hole assembly.
Performance Comparison between Reconfigurable Intelligent Surface and Relays: Theoretical Methods and a Perspective from Operator
On the other hand, a relay node in a traditional relay network has to be active, which indicates that it will consume energy when it is relaying the signal or information between the source and destination nodes.
Information Theory Information Theory
Compressive Circular Polarization Snapshot Spectral Imaging
A compressive sensing based circular polarization snapshot spectral imaging system is proposed in this paper to acquire two-dimensional spatial, one-dimensional circular polarization (the right and left circular polarization), and one-dimensional spectral information, simultaneously.
Compressive Sensing
Optics
Image and Video Processing
78A10 (Primary)
I.4.2; I.4.4; I.4.5
Discriminatively Constrained Semi-supervised Multi-view Nonnegative Matrix Factorization with Graph Regularization
In recent years, semi-supervised multi-view nonnegative matrix factorization (MVNMF) algorithms have achieved promising performances for multi-view clustering.
Kernel principal component analysis network for image classification
In order to classify the nonlinear feature with linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network (KPCANet) is proposed.
Conditional Random Fields with High-Order Features for Sequence Labeling
Dependencies among neighbouring labels in a sequence is an important source of information for sequence labeling problems.