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A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems
L Cheng, T Yu - International Journal of Energy Research, 2019 - Wiley Online Library
The new generation of artificial intelligence (AI), called AI 2.0, has recently become a
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …
Research progress of automated visual surface defect detection for industrial metal planar materials
X Fang, Q Luo, B Zhou, C Li, L Tian - Sensors, 2020 - mdpi.com
The computer-vision-based surface defect detection of metal planar materials is a research
hotspot in the field of metallurgical industry. The high standard of planar surface quality in …
hotspot in the field of metallurgical industry. The high standard of planar surface quality in …
Short-term traffic flow prediction with LSTM recurrent neural network
Accurate and timely short-term traffic flow prediction plays an important role in intelligent
transportation management and control. Traffic flow prediction has a long history and is still …
transportation management and control. Traffic flow prediction has a long history and is still …
T-LSTM: A long short-term memory neural network enhanced by temporal information for traffic flow prediction
Short-term traffic flow prediction is one of the most important issues in the field of intelligent
transportation systems. It plays an important role in traffic information service and traffic …
transportation systems. It plays an important role in traffic information service and traffic …
[PDF][PDF] Dioxin emission concentration measurement approaches for municipal solid wastes incineration process: a survey
Q Jun-Fei, G Zi-Hao, T Jian - Acta Automatica Sinica, 2020 - aas.net.cn
Incineration has significant advantages in the harmless, reduction and recycling treatment of
municipal solid waste (MSW). Dioxins (DXN), a highly toxic and persistent pollutant that is a …
municipal solid waste (MSW). Dioxins (DXN), a highly toxic and persistent pollutant that is a …
PDP: parallel dynamic programming
Deep reinforcement learning is a focus research area in artificial intelligence. The principle
of optimality in dynamic programming is a key to the success of reinforcement learning …
of optimality in dynamic programming is a key to the success of reinforcement learning …
A robust completed local binary pattern (rclbp) for surface defect detection
In this paper, we present a Robust Completed Local Binary Pattern (RCLBP) framework for a
surface defect detection task. Our approach uses a combination of Non-Local (NL) means …
surface defect detection task. Our approach uses a combination of Non-Local (NL) means …
Deep convolutional neural network based fractional-order terminal sliding-mode control for robotic manipulators
This paper proposes a deep convolutional neural network (DCNN) based fractional-order
terminal sliding-mode (FOTSM) control strategy for tracking control of rigid robotic …
terminal sliding-mode (FOTSM) control strategy for tracking control of rigid robotic …
The vehicle testing based on digital twins theory for autonomous vehicles
Z Meng, S Zhao, H Chen, M Hu… - IEEE Journal of Radio …, 2022 - ieeexplore.ieee.org
Digital twins theory can provide an effective solution for the complex whole vehicle testing,
which includes the dynamics and scenarios. The parallel system is the most advanced …
which includes the dynamics and scenarios. The parallel system is the most advanced …
Deep reinforcement learning–based online one-to-multiple charging scheme in wireless rechargeable sensor network
Z Gong, H Wu, Y Feng, N Liu - Sensors, 2023 - mdpi.com
Wireless rechargeable sensor networks (WRSN) have been emerging as an effective
solution to the energy constraint problem of wireless sensor networks (WSN). However, most …
solution to the energy constraint problem of wireless sensor networks (WSN). However, most …