6G-ADM: knowledge based 6G network management and control architecture [PDF]
, 2022 Objectives: With the consensus reached on the 6G vision of three-dimensional coverage, extreme performance, virtual real integration and ubiquitous intelligence, problems such as personalized service customization, proliferation of network element types ...
Haifeng SUN +4 more
core +1 more source
Zhejiang Daxue xuebao. Lixue ban, 2019 针对目前大部分PM2.5 预测模型预测效果不稳定、泛化能力不强的现状，以记忆能力较强的循环神经网络（RNN） 和特征表达能力较强的卷积神经网络（CNN） 为基础，采取Stacking 集成策略对两者进行融合，提出了RNN-CNN 集成深度学习预测模型。该模型不仅充分利用时间轴上的前后关联信息去预测未来的浓度，而且在不同层次上将自动提取的高维时序数据通用特征用于预测，以保证预测结果的稳定性。最后，对集成之前的 RNN、CNN 和集成之后的RNN-CNN 模型，以2016 年中国大陆地区1 466 ...
HUANGJie(黄婕) +4 more
doaj +1 more source
Dynamic Blockchain Sharding for 6G Internet of Things Devices Collaboration [PDF]
With the massive deployment of Internet of Things (IoT) applications, numerous devices work together to generate massive, high-value data. If the security of these data is not guaranteed, they are vulnerable to threats such as data abuse, privacy leakage,
Ziyue CAI, Beihai TAN, Rong YU, Xumin HUANG, Siming WANG
core +1 more source
Voltage Hierarchical Control Strategy of Active Distribution Network Based on Deep Reinforcement Learning [PDF]
ObjectivesThe randomness and volatility of distributed power generation poses significant challenges for the voltage control in active distribution network (AND).
DU Wanlin +6 more
core +1 more source
Advantage estimator based on importance sampling [PDF]
, 2019 In continuous action tasks,deep reinforcement learning usually uses Gaussian distribution as a policy function.Aiming at the problem that the Gaussian distribution policy function slows down due to the clipped action,an importance sampling advantage ...
Quan LIU, Yubin JIANG, Zhihui HU
core +1 more source
Review of Application on Optimization Strategies for New-Type Power System Based on Reinforcement Learning [PDF]
ObjectivesAs power systems evolve toward higher levels of intelligence and automation, reinforcement learning (RL), a key technology in artificial intelligence, shows great potential in the intelligent development of the power sector.
WANG Kai, YAN Zhengyi, ZHAO Kang
core +1 more source
Distributed interference coordination based on multi-agent deep reinforcement learning [PDF]
, 2020 A distributed interference coordination strategy based on multi-agent deep reinforcement learning was investigated to meet the requirements of file downloading traffic in interference networks.By the proposed strategy transmission scheme could be ...
Chenyang YANG +2 more
core +1 more source
Intelligent anti-jamming decision algorithm based on proximal policy optimization [PDF]
The existing intelligent anti-jamming methods based on deep reinforcement learning are applied to space-ground TT&C and communication links, in which the deep neural network used for decision-making has a complex structure, and the resources of ...
HUANG Wei +4 more
core +1 more source
Research on Decision-Making at Intersection Without Traffic Lights Based on Deep Reinforcement Learning [PDF]
Left-turn intersections without signal lights are among the most dangerous scenes in autonomous driving, and achieving efficient and safe left-turn decision-making is highly challenging in autonomous driving.
FU Mingjian, GUO Fuqiang
core +1 more source
Digital Storytelling on Social Media for Language Learning: Students’ Experiences and Perceptions
Journal of Linguistics and Language TeachingThis study investigates the efficacy of digital storytelling, inspired by social media celebrity practices, as a pedagogical approach for language acquisition and intercultural engagement.
Minjie Xing, Amily Guenier
doaj +1 more source