Factor Graph Model Based User Profile Matching Across Social Networks

Wang, Lidong; Hu, Keyong; Zhang, Yun; Cao, Shixun

doi:10.1109/access.2019.2948073

Cited by 11 publications

(5 citation statements)

References 29 publications

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“…But previous research reports different findings in terms of the alpha band. Some research found an increasing trend between the alpha band and fatigue [57]. However, fatigue does not lead to a noticeable decrease or increase in beta band activity.…”

Section: Discussionmentioning

confidence: 98%

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Xu,

Li,

Feng

2023

Sensors

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Fatigue of miners is caused by intensive workloads, long working hours, and shift-work schedules. It is one of the major factors increasing the risk of safety problems and work mistakes. Examining the detection of miner fatigue is important because it can potentially prevent work accidents and improve working efficiency in underground coal mines. Many previous studies have introduced feature-based machine-learning methods to estimate miner fatigue. This work proposes a method that uses electroencephalogram (EEG) signals to generate topographic maps containing frequency and spatial information. It utilizes a convolutional neural network (CNN) to classify the normal state, critical state, and fatigue state of miners. The topographic maps are generated from the EEG signals and contrasted using power spectral density (PSD) and relative power spectral density (RPSD). These two feature extraction methods were applied to feature recognition and four representative deep-learning methods. The results showthat RPSD achieves better performance than PSD in classification accuracy with all deep-learning methods. The CNN achieved superior results to the other deep-learning methods, with an accuracy of 94.5%, precision of 97.0%, sensitivity of 94.8%, and F1 score of 96.3%. Our results also show that the RPSD–CNN method outperforms the current state of the art. Thus, this method might be a useful and effective miner fatigue detection tool for coal companies in the near future.

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Section: Discussionmentioning

confidence: 98%

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Xu,

Li,

Feng

2023

Sensors

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“…Wang et al [180], Ahmad et al [181] 4. online groups joined by a user on SNs exploiting the group membership information of a user that he/she subscribe/join for information seeking or sharing and is available on the SN sites.…”

Section: B De-anonymization Methods Employed By the Adversaries To Jmentioning

confidence: 99%

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

2021

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“…Thirdly, it provides more accurate and reliable predictions than traditional methods. Fourthly, it enables the optimization of energy storage device scheduling, leading to peak demand reduction and providing backup power (Wang et al, 2019).…”

Section: Gru Modelmentioning

confidence: 99%

Smart grid energy storage capacity planning and scheduling optimization through PSO-GRU and multihead-attention

Xiao,

Zhang

2023

Front. Energy Res.

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The energy problem in today’s society is becoming increasingly prominent, and the smart grid has become one of the important ways to solve the energy problem. Smart grid energy storage capacity planning and scheduling optimization is an important issue in the smart grid, which can make the grid more efficient, reliable, and sustainable to meet energy demand better and protect the environment. The core of smart grid energy storage capacity planning and scheduling optimization is maximizing the use of energy storage devices to balance the difference between power supply and demand to ensure the grid operation’s stability. Traditional planning methods are usually based on experience and rules, have low precision, and cannot adapt to the dynamic changes in the long-term development of the power grid. Therefore, this paper proposes a method that combines PSO-GRU (particle swarm Optimization (PSO)-gated recurrent unit (GRU)) and Multihead-Attention to realize smart grid energy storage capacity planning. And scheduling optimization. First, PSO-GRU models and predicts power grid data by searching for the optimal GRU model parameters; second, Multihead-Attention improves the model’s performance through the self-attention mechanism. Finally, we use the method to determine the optimal energy storage capacity and dispatching scheme for the efficient operation of smart grids. Our experiments use real power grid datasets and compare them with other common methods. Experimental results show that our proposed method has higher accuracy and stability than other methods and can better adapt to the dynamic changes of the power grid. This indicates that our method has good feasibility and applicability in practical applications and is significant for realizing the efficient operation of smart grids and energy saving and emission reduction.

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Factor Graph Model Based User Profile Matching Across Social Networks

Cited by 11 publications

References 29 publications

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Smart grid energy storage capacity planning and scheduling optimization through PSO-GRU and multihead-attention

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