Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Deep reinforcement learning in smart manufacturing: A review and prospects
To facilitate the personalized smart manufacturing paradigm with cognitive automation
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by …
Deep reinforcement learning in production systems: A systematic literature review
Shortening product development cycles and fully customisable products pose major
challenges for production systems. These not only have to cope with an increased product …
challenges for production systems. These not only have to cope with an increased product …
Intrusion response systems for cyber-physical systems: A comprehensive survey
Abstract Cyberattacks on Cyber-Physical Systems (CPS) are on the rise due to CPS
increased networked connectivity and may cause costly environmental hazards as well as …
increased networked connectivity and may cause costly environmental hazards as well as …
Novel best path selection approach based on hybrid improved A* algorithm and reinforcement learning
X Liu, D Zhang, T Zhang, Y Cui, L Chen, S Liu - Applied Intelligence, 2021 - Springer
Path planning of intelligent driving vehicles in emergencies is a hot research issue, this
paper proposes a new method of the best path selection for the intelligent driving vehicles to …
paper proposes a new method of the best path selection for the intelligent driving vehicles to …
Zero knowledge clustering based adversarial mitigation in heterogeneous federated learning
The simultaneous development of deep learning techniques and Internet of Things
(IoT)/Cyber-physical Systems (CPS) technologies has afforded untold possibilities for …
(IoT)/Cyber-physical Systems (CPS) technologies has afforded untold possibilities for …
A survey for deep reinforcement learning in markovian cyber–physical systems: Common problems and solutions
Abstract Deep Reinforcement Learning (DRL) is increasingly applied in cyber–physical
systems for automation tasks. It is important to record the develo** trends in DRL's …
systems for automation tasks. It is important to record the develo** trends in DRL's …
Deep neural networks for spatial-temporal cyber-physical systems: A survey
Cyber-physical systems (CPS) refer to systems that integrate communication, control, and
computational elements into physical processes to facilitate the control of physical systems …
computational elements into physical processes to facilitate the control of physical systems …
Machine learning frameworks in IoT systems: A survey, case study, and future research directions
Abstract The Internet of Things (IoT) and the creation of cyber-physical systems (CPS) have
recently received much attention in both academia and industry due to the rapid growth and …
recently received much attention in both academia and industry due to the rapid growth and …
Deep reinforcement learning in production planning and control: A systematic literature review
Increasingly fast development cycles and individualized products pose major challenges for
today's smart production systems in times of industry 4.0. The systems must be flexible and …
today's smart production systems in times of industry 4.0. The systems must be flexible and …
Toward Deep Q-Network-Based Resource Allocation in Industrial Internet of Things
With the increasing adoption of Industrial Internet-of-Things (IIoT) devices, infrastructures,
and supporting applications, it is critical to design schemes to effectively allocate resources …
and supporting applications, it is critical to design schemes to effectively allocate resources …