A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security

Lv, Lingling; Wu, Zhenyu; Zhang, Jinhua; Tan, Zhiyuan; Zhang, Lei; Tian, Zhihong

doi:10.1109/tii.2021.3130237

Cited by 130 publications

(46 citation statements)

References 22 publications

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“…How to infer and prove the results in the contests involved many asymmetric groups is a possible extension. In addition, with the rapid development of artificial intelligence [33,34,48], the next work to be carried out would be combining the model in this paper with deep learning theory [35,49,51] and applying it to engineering practice.…”

Section: Discussionmentioning

confidence: 99%

An analysis of group contests with the possibility of a draw

Chang

Wang

2023

JIMO

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<p style='text-indent:20px;'>This paper investigates Tullock group contests with the possibility of a draw in which the winner, if any, shares the prize with other players in his group, and each group's sharing rule is private. Considering the draw is exogenously determined, we investigate the strategic choice of the sharing rules and individual effort. We find that introducing the draw into the contest has negative effects on individual payoff, while players would increase the sharing rule. When groups are symmetric, the possibility of a draw decreases all group's individual effort. In the model with asymmetric groups, the draw has a non-monotonic effect on the aggregate effort and individual effort of the strong group with larger value for the prize. Next, considering the draw is endogenously given, we investigate the strategy for maximizing aggregate effort. In the asymmetric model, when groups' values of the prize are dispersed enough or the organizer cares much more about the strong group, introducing a draw into the contest would increase the aggregate effort. However, no draw is most beneficial to the aggregate effort if the organizer puts more weight on the weak group. Finally, a numerical example is given to analyze and illustrate the results.</p>

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Section: Discussionmentioning

confidence: 99%

An analysis of group contests with the possibility of a draw

Chang

Wang

2023

JIMO

Self Cite

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“…This paper proposes a dual-attention-based multiscale feature fusion residual network for retinal vessel image segmentation [34][35][36][37]. The paper first designs a feature fusion residual module including ECA-Net, which effectively extracts image details and solves problems such as gradient dispersion and network degradation; then uses SA and ECA and modules such as feature fusion; adaptively aggregate features that are effective for segmentation; enhance network feature representation; and finally, effectively aggregate features at different stages to improve the segmentation performance of the network.…”

Section: Discussionmentioning

confidence: 99%

Image Segmentation of Retinal Blood Vessels Based on Dual-Attention Multiscale Feature Fusion

Gao

Huang

Gao

et al. 2022

Computational and Mathematical Methods in Medicine

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Aiming at the problem of insufficient details of retinal blood vessel segmentation in current research methods, this paper proposes a multiscale feature fusion residual network based on dual attention. Specifically, a feature fusion residual module with adaptive calibration weight features is designed, which avoids gradient dispersion and network degradation while effectively extracting image details. The SA module and ECA module are used many times in the backbone feature extraction network to adaptively select the focus position to generate more discriminative feature representations; at the same time, the information of different levels of the network is fused, and long-range and short-range features are used. This method aggregates low-level and high-level feature information, which effectively improves the segmentation performance. The experimental results show that the method in this paper achieves the classification accuracy of 0.9795 and 0.9785 on the STARE and DRIVE datasets, respectively, and the classification performance is better than the current mainstream methods.

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“…The attention mechanism has gradually become a research hotspot and an important part of the neural network structure in recent years. Initially applied to machine translation, now it has been used in image processing and recommendation systems 26 , 27 . The attention mechanism comes from the human visual attention mechanism.…”

Section: The Modelmentioning

confidence: 99%

A method of radar echo extrapolation based on dilated convolution and attention convolution

Shen

Meng

Zhang

et al. 2022

Sci Rep

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The neural network method can obtain a higher precision of radar echo extrapolation than the traditional method. However, its application in radar echo extrapolation is still in the initial stage of exploration, and there is still much room for improvement in the extrapolation accuracy. To improve the utilization of radar echo information and extrapolation accuracy, this paper proposes a radar echo extrapolation model (ADC_Net) based on dilated convolution and attention convolution. In this model, dilated convolution, instead of the pooling operation, is used to downsample the feature matrix obtained after the standard convolution operation. In doing so, the internal data structure of the feature matrix is retained, and the spatial features of radar echo data from different scales are extracted as well. Besides, the attention convolution module is integrated in the ADC_Net model to improve its sensitivity to the target features in the feature matrix and suppress the interference information. The proposed model is tested in the extrapolation of radar echo images in the next 90 min from five aspects—extrapolated image, POD index, CSI index, FAR index, and HSS index. The experimental results show that the model can effectively improve the accuracy of radar echo extrapolation.

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A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security

Cited by 130 publications

References 22 publications

An analysis of group contests with the possibility of a draw

An analysis of group contests with the possibility of a draw

Image Segmentation of Retinal Blood Vessels Based on Dual-Attention Multiscale Feature Fusion

A method of radar echo extrapolation based on dilated convolution and attention convolution

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