Distributed filtering for delayed nonlinear system with random sensor saturation: a dynamic event-triggered approach

Li, Zehao; Hu, Jun; Li, Jiaxing

doi:10.1080/21642583.2021.1919935

Cited by 24 publications

(7 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firstly, the ability of extracting highly discriminative features can be enhanced by diverse convolution variants and multi-scale feature fusion methods [36] , [37] . Secondly, common spatial pattern [38] and multi-agent systems [39] can be applied to optimize the training process, and aiming at the training data, some filtering techniques can be adopted to alleviate the interference of noises [40] , [41] , [42] , [43] , [44] In addition, some data enhancement techniques can be considered as well [45] . Moreover, a number of optimization algorithms and systems can be used to realize potential better structural configurations so that the established network can exhibit superior performance [46] , [47] , [48] , [49] , [50] .…”

Section: Resultsmentioning

confidence: 99%

Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis

Xie

Wang

et al. 2023

Computers in Biology and Medicine

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis

Xie

Wang

et al. 2023

Computers in Biology and Medicine

View full text Add to dashboard Cite

“…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.

View full text Add to dashboard Cite

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.

show abstract

“…The resulted quantization errors, if not properly settled, might significantly decrease the system performance. In this regard, the quantization issues have gained considerable attention in recent years, see for example, References 43‐53. For example, a recursive finite‐horizon filter has been proposed in Reference 54 for a class of nonlinear time‐varying systems subject to logarithmic quantization effects.…”

Section: Introductionmentioning

confidence: 99%

Distributed estimation for multi‐agent systems with relative measurements and quantized communication: A feedback quadratic framework

Liu

Zheng

Wang

et al. 2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this paper, the feedback quadratic distributed state estimation problem is investigated for a class of linear discrete time-varying multi-agent systems subject to non-Gaussian noises. For the multi-agent systems, each agent has access to not only the local measurements but the relative states to its adjacent agents as well. Due to the bandwidth constraints of the digital communication networks, the signals are quantized and then exchanged between two adjacent agents, where the probabilistic uniform quantizations are taken into account. The purpose of the addressed problem is to design a novel quadratic distributed estimator for each agent based on the local information and the predictions received from the neighbors. In particular, an output injection term is introduced to handle the unstable systems. By means of the state/measurement augmentation approach, the underlying system is transformed into an augmented one, which aggregates the original vector and its second-order Kronecker power. Accordingly, a distributed estimator is constructed such that an upper bound is ensured for the estimation error covariance and the suboptimal estimator parameters, which minimize such an upper bound, are subsequently calculated in terms of the solutions to certain matrix difference equations. Finally, an illustrative example is provided to verify the effectiveness and superiority of the proposed quadratic estimation scheme.

show abstract

Distributed filtering for delayed nonlinear system with random sensor saturation: a dynamic event-triggered approach

Cited by 24 publications

References 37 publications

Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis

Progressive attention integration-based multi-scale efficient network for medical imaging analysis with application to COVID-19 diagnosis

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

Distributed estimation for multi‐agent systems with relative measurements and quantized communication: A feedback quadratic framework

Contact Info

Product

Resources

About