|
|
|
|
|
Junfang Fan, Denghui Dou and Yi Ji
In this study, two different impact-angle-constrained guidance and control strategies using deep reinforcement learning (DRL) are proposed. The proposed strategies are based on the dual-loop and integrated guidance and control types. To address comprehen...
ver más
|
|
|
|
|
|
|
|
|
Yan Zeng, Jiyang Wu, Jilin Zhang, Yongjian Ren and Yunquan Zhang
Deep learning, with increasingly large datasets and complex neural networks, is widely used in computer vision and natural language processing. A resulting trend is to split and train large-scale neural network models across multiple devices in parallel,...
ver más
|
|
|
|
|
|
|
|
|
Tengjie Yang, Lin Zuo, Xinduoji Yang and Nianbo Liu
In recent years, individual learning path planning has become prevalent in online learning systems, while few studies have focused on teaching path planning for traditional classroom teaching. This paper proposes a target-oriented teaching path optimizat...
ver más
|
|
|
|
|
|
|
|
|
Xiaozhou Zhou, Yu Jin, Lesong Jia and Chengqi Xue
In virtual reality, users? input and output interactions are carried out in a three-dimensional space, and bare-hand click interaction is one of the most common interaction methods. Apart from the limitations of the device, the movements of bare-hand cli...
ver más
|
|
|
|
|
|
|
|
|
Yang Chen, Masao Yamagishi and Isao Yamada
This paper proposes a new group-sparsity-inducing regularizer to approximate l2,0
l
2
,
0
pseudo-norm. The regularizer is nonconvex, which can be seen as a linearly involved generalized Moreau enhancement of l2,1
l
2
,
1
-norm. Moreover, the overall con...
ver más
|
|
|
|