Resilient energy-to-peak filtering for linear parameter-varying systems under random access protocol

Yu, Haoyang; Hu, Jun; Song, Baoye; Liu, Hongjian; Yi, Xiaojian

doi:10.1080/00207721.2022.2053232

Cited by 27 publications

(12 citation statements)

References 49 publications

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“…Accurate and efficient solutions can be brought out for realistic problems by combining the powerful simulation capacity of MASs, the node classification and the link prediction of graph embedding algorithms. Thus, in the future, our next study direction is to apply novel graph embedding ideas (based on the MAS model) to the realistic problem so as to look into the structures of society or rules of human activities [25], [27], [42], [52], [53]. It should be mentioned that the study for graph embedding on the dynamic graph can be a new challenge for MAS simulations on the graph evolution of the real network [5], [54].…”

Section: Discussionmentioning

confidence: 99%

Influence Maximization in Multiagent Systems by a Graph Embedding Method: Dealing With Probabilistically Unstable Links

Wang

Han

et al. 2023

IEEE Trans. Cybern.

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This paper is concerned with the influence maximization problem under a network with probabilistically unstable links (PULs) via graph embedding for multi-agent systems (MASs). First, two diffusion models, the unstable-link independent cascade (UIC) model and the unstable-link linear threshold (ULT) model, are designed for the influence maximization problem under the network with PULs. Second, the MAS model for the influence maximization problem with PULs is established and a series of interaction rules among agents are built for the MAS model. Third, the similarity of the unstable structure of the nodes is defined and a novel graph embedding method, termed the unstable-similarity2vec (US2vec) approach, is proposed to tackle the influence maximization problem under the network with PULs. According to the embedding results of the US2vec approach, the seed set is figured out by the developed algorithm. Finally, extensive experiments are conducted to 1) verify the validity of the proposed model and the developed algorithms, and 2) illustrate the optimal solution for influence maximization under different scenarios with PULs.

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Section: Discussionmentioning

confidence: 99%

Influence Maximization in Multiagent Systems by a Graph Embedding Method: Dealing With Probabilistically Unstable Links

Wang

Han

et al. 2023

IEEE Trans. Cybern.

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“…Moreover, DCP-A can be extended into multi-task pruning. In the future, we will 1) consider more different guidance mechanisms with layer information [32,46,47,49,56,57,66], 2) introduce control strategies to enhance the model robustness [3,27,33,50,51,58], and 3) extend our approach to other complicated multi-task learning problems [1,23,34,59,65,67].…”

Section: Discussionmentioning

confidence: 99%

Differentiable channel pruning guided via attention mechanism: a novel neural network pruning approach

Cheng

Wang²,

Ma³

et al. 2023

Complex Intell. Syst.

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Neural network pruning offers great prospects for facilitating the deployment of deep neural networks on computational resource limited devices. Neural architecture search (NAS) provides an efficient way to automatically seek appropriate neural architecture design for compressed model. It is observed that, for existing NAS-based pruning methods, there is usually a lack of layer information when searching the optimal neural architecture. In this paper, we propose a new NAS approach, namely, differentiable channel pruning method guided via attention mechanism (DCP-A), where the adopted attention mechanism is able to provide layer information to guide the optimization of the pruning policy. The training process is differentiable with Gumbel-softmax sampling, while parameters are optimized under a two-stage training procedure. The neural network block with the shortcut is dedicatedly designed, which is of help to prune the network not only on its width but also on its depth. Extensive experiments are performed to verify the applicability and superiority of the proposed method. Detailed analysis with visualization of the pruned model architecture shows that our proposed DCP-A learns explainable pruning policies.

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“…4. New research requires further efforts to compress the many power quality data generated by the continuous operation of the grid [171][172][173][174][175] to ensure real-time monitoring of power quality.…”

Section: Future Possible Research Hotspots Of Pqd Detectionmentioning

confidence: 99%

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yan¹,

Chen²,

Geng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field, where specific scenarios and events for which each technique is applicable are also clearly presented. Finally, comments on the future evolution of PQD detection techniques are given. Unlike the published review articles, this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

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Resilient energy-to-peak filtering for linear parameter-varying systems under random access protocol

Cited by 27 publications

References 49 publications

Influence Maximization in Multiagent Systems by a Graph Embedding Method: Dealing With Probabilistically Unstable Links

Influence Maximization in Multiagent Systems by a Graph Embedding Method: Dealing With Probabilistically Unstable Links

Differentiable channel pruning guided via attention mechanism: a novel neural network pruning approach

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

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