Probabilistic-constrained distributed fusion filtering for a class of time-varying systems over sensor networks: a torus-event-triggering mechanism

Qu, Fanrong; Zhao, Xuelong; Wang, Xinmeng; Tian, Engang

doi:10.1080/00207721.2021.1998721

Cited by 84 publications

(23 citation statements)

References 38 publications

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“…Future work can be summarized into the following five aspects: 1) designing a parameter selection strategy to automatically choose the parameters of the adaptive weighting function; 2) adjusting the mean and variance of the random noises adaptively for different evolutionary states; 3) applying the ASRPPSO to multi-objective optimization problems; and 4) analyzing the dynamical behavior of the ASRPPSO by using the Lyapunov-like stability theory and the filtering techniques [11], [18], [20], [30]; and 5) deploying the proposed outlier detection algorithm to some other data analysis applications [22], [29].…”

Section: Outlier Detection On Waam Data Setsmentioning

confidence: 99%

A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data Clustering in Wire Arc Additive Manufacturing

Fang

Wang

Liu

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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In this paper, a novel outlier detection method is proposed for industrial data analysis based on the fuzzy C-means (FCM) algorithm. An adaptive switching randomly perturbed particle swarm optimization algorithm (ASRPPSO) is put forward to optimize the initial cluster centroids of the FCM algorithm. The superiority of the proposed ASRPPSO is demonstrated over five existing PSO algorithms on a series of benchmark functions. To illustrate its application potential, the proposed ASRPPSO-based FCM algorithm is exploited in the outlier detection problem for analyzing the real-world industrial data collected from a wire arc additive manufacturing pilot line in Sweden. Experimental results demonstrate that the proposed ASRPPSO-based FCM algorithm outperforms the standard FCM algorithm in detecting outliers of real-world industrial data. Note to Practitioners-Electric arc (which is governed by the current and arc voltage) plays a significant role in monitoring the operating status of the wire arc additive manufacturing (WAAM) process. The nominal periodic current and voltage may occasionally change abruptly due to anomalies (such as arc instability, unstable metal transfer, geometrical deviations, and surface contaminations), which would affect the quality of the fabricated component. This paper focuses on detecting possible anomalies by analyzing the current and voltage during the WAAM process. A novel clustering-based outlier detection method is proposed for anomaly detection where abnormal and normal instances are categorized into two separate clusters.A new particle swarm optimization algorithm is put forward to optimize the initial cluster centroid so as to improve the detection accuracy. The proposed outlier detection method is applied to real-world data collected from a WAAM pilot line for detecting abnormal instances. Experimental results demonstrate the effectiveness of the proposed outlier detection method. The proposed outlier detection method can be applied to other industrial applications including electrical engineering, mechanical engineering and medical engineering. In the future, we aim to develop an online outlier detection system based on the proposed

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Section: Outlier Detection On Waam Data Setsmentioning

confidence: 99%

A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data Clustering in Wire Arc Additive Manufacturing

Fang

Wang

Liu

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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“…Generally speaking, the phenomenon of the PULs can be classified into three types, i.e. intermittent links [21], probabilistic links [11], [34] and dynamic links [24]. Each type results in its distinctive uncertainty on the influence diffusion, and such uncertainties complicate the community structure and bring in substantial difficulties in influences calculation.…”

Section: Introductionmentioning

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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“…Note that, when utilizing traditional NAS-based methods, we usually confront difficulty in searching a large space with unacceptable searching speed. Although some effort has been devoted to reducing the searching space [12,22,24,28,38], the layer information, i.e., the information of filters in one layer, has seldom been taken into consideration when it comes to the optimization of the pruning policy. Basically, most criterionbased pruning methods fail to take the correlation among layers into consideration while NAS-based methods usually ignore the information of individual filters in a layer.…”

Section: Introductionmentioning

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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Probabilistic-constrained distributed fusion filtering for a class of time-varying systems over sensor networks: a torus-event-triggering mechanism

Cited by 84 publications

References 38 publications

A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data Clustering in Wire Arc Additive Manufacturing

A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data Clustering in Wire Arc Additive Manufacturing

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

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