Results 51 to 60 of about 104,321 (158)

Real-Time Small-Signal Security Assessment of Power Systems Using an Edge-Enhanced Graph Attention Network

Applied Sciences
Conventional small-signal security assessment requires repeated power flow calculations, system linearization, and eigenvalue analysis, resulting in considerable computational burden for online applications.
Weiran Jiao, Mingyuan Wang, Qian Sun, Kangli Liu, Jianfeng Zhao   +4 more
doaj   +1 more source

LLM-Guided Graph Structure Learning for Alert Convergence in AIOps

Computers
In modern cloud-native systems, a single root cause can trigger cascading anomalies across multiple entities (e.g., microservices, databases, and hosts), generating alert storms with hundreds or thousands of heterogeneous alerts.
Haodong Zou, Yichen Zhao, Xin Chen, Ling Wang, Jinghang Yu, Long Yuan, Luokai Jiang   +6 more
doaj   +1 more source

D3GNN: Double dual dynamic graph neural network for multisource remote sensing data classification

International Journal of Applied Earth Observations and Geoinformation
Convolutional Neural Network (CNN) has garnered attention due to its outstanding performance in multisource remote sensing (RS) image classification.
Teng Yang, Song Xiao, Jiahui Qu
doaj   +1 more source

Graph Split Federated Learning for Distributed Large-Scale AIoT in Smart Cities

IEEE Open Journal of the Computer Society
The rise of smart cities has leveraged the power of Internet of Things devices to transform urban services. A key element of this transformation is the widespread deployment of IoT devices for data collection, which feeds into machine learning algorithms
Hanyue Xu, Kah Phooi Seng, Li-Minn Ang, Wei Wang, Jeremy Smith   +4 more
doaj   +1 more source

Joint Resource Allocation and Trajectory Control for UAV-Enabled Vehicular Communications

IEEE Access, 2019
In this paper, aiming at the emergency coverage for vehicular network, we consider the problem of resource allocation for unmanned-aerial-vehicle (UAV) enabled vehicular communications, where UAV work as a temporary cellular base station.
Lijun Deng, Gang Wu, Jingwei Fu, Yizhong Zhang, Yifu Yang   +4 more
doaj   +1 more source

Towards optimized scheduling and allocation of heterogeneous resource via graph-enhanced EPSO algorithm

Journal of Cloud Computing: Advances, Systems and Applications
Efficient allocation of tasks and resources is crucial for the performance of heterogeneous cloud computing platforms. To achieve harmony between task completion time, device power consumption, and load balance, we propose a Graph neural network-enhanced
Zhen Zhang, Chen Xu, Shaohua Xu, Long Huang, Jinyu Zhang   +4 more
doaj   +1 more source

Optimizing load frequency control in isolated island city microgrids: a deep graph reinforcement learning approach with data enhancement across extensive scenarios

Frontiers in Energy Research
This study presents a Data-Enhanced Optimum Load Frequency Control (DEO-LFC) strategy for microgrids, targeting an optimal balance between generation costs and frequency stability amidst high renewable energy integration.
Min Wu, Dakui Ma, Kaiqing Xiong, Linkun Yuan   +3 more
doaj   +1 more source

Enhanced Subspace Clustering With Limited Prior Knowledge Using Tensor Low-Rank Representation

IEEE Access
In this paper, we introduce a new subspace clustering model based on tensor low-rank representation to improve clustering performance under the condition of limited prior knowledge.
Junjie Yang, Wei Yan, Yi Guo, Senquan Yang   +3 more
doaj   +1 more source

Graph Neural Network- Enhanced Power Flow Adjustment

International Journal of Inventive Engineering and Sciences
Modern power grids are facing unprecedented operational complexity due to the surge in distributed energy resources (DERs), intermittent renewables, and electric vehicle (EV) charging demands. While traditional methods like Newton Raphson are computationally precise, their iterative nature often fails to meet the sub-second latency requirements of ...
Gowtham I, Dr. Perumal B
openaire   +1 more source

Residual-aware health prediction of power transformers via spatiotemporal graph neural networks.

PLoS ONE
Accurate health state prediction and timely fault detection of power transformers are critical for ensuring the reliability and resilience of modern power systems. This paper proposes a residual-aware spatiotemporal graph neural network (STGNN) framework
Peng Zeng, Gong Chu
doaj   +1 more source