A Modified Algorithm Based on Smoothed L0 Norm in Compressive Sensing Signal Reconstruction

Wang, Lin-Yu; Ye, Pengfei; Xiang, Jianhong

doi:10.1109/icip.2018.8451799

Cited by 11 publications

(3 citation statements)

References 7 publications

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“…In accordance with [31], sparse restrictions allow being introduced to the model loss function for the alleviation of feature homogeneity symptoms. Because of its nonzero numerical characteristic, the Gaussian function family [32] will be used for sparse regularization in this paper. The sparse regular term is be expressed as:…”

Section: Ms-dbn With Momentum Factormentioning

confidence: 99%

An optimized sparse deep belief network with momentum factor for fault diagnosis of radar transceivers

Shi,

Li,

Chen

2024

Meas. Sci. Technol.

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Transceiver is a crucial component of radar system that allows for the regulation of signal phase and amplitude as well as the amplification of both transmitted and received signals. Its operational efficiency has a significant impact on the whole dependability of the radar system. To ensure the safe and reliable operation of the radar system, an optimized sparse deep belief network with momentum factor is developed to diagnose potential faults of radar transceivers. Firstly, a momentum term is added into the parameter update to enhance the anti-oscillation ability of model parameters in training, while a sparse regular term is integrated into the deep belief network to prevent the model from overfitting. Secondly, to automatically configure the model hyper-parameters, a hybrid sine cosine algorithm with dynamic inertia weight and adaptive strategies is proposed. Thus, an effective diagnostic model named HSCA-MS-DBN is formed by combining sparse deep belief network with momentum factor and hybrid sine cosine algorithm. The efficiency of the proposed HSCA-MS-DBN model is confirmed using an actual-world radar transceiver dataset, and the findings from experiments reveal that this model surpasses multiple prominent intelligent models.

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Section: Ms-dbn With Momentum Factormentioning

confidence: 99%

An optimized sparse deep belief network with momentum factor for fault diagnosis of radar transceivers

Shi,

Li,

Chen

2024

Meas. Sci. Technol.

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“…CS is mainly used in the field of signal recovery [39–42]. Candés and Tao [15] proved that the CS problem can be solved by minimizing ℓ 0 norm model:

$$\begin{equation} {\begin{cases} \min \Vert {\mathbf {x}}\Vert _0 \\[3pt] \text{s.t.}

…”

Section: Related Workmentioning

confidence: 99%

“…CS is mainly used in the field of signal recovery [39][40][41][42]. Candés and Tao [15] proved that the CS problem can be solved by minimizing 𝓁 0 norm model:…”

Section: Compressive Sensingmentioning

confidence: 99%

SRI3D: Two‐stream inflated 3D ConvNet based on sparse regularization for action recognition

Yang

Zhang

et al. 2022

IET Image Processing

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Although most state‐of‐the‐art action recognition models have adopted a two‐stream 3D convolutional structure as a backbone network, few works have studied the impact of loss functions on action recognition models. In addition, sparsity is used as a key prior knowledge in many fields. However, as far as is known, no one has studied the influence of the sparsity of network output on the output of deep learning‐based action recognition models. Therefore, this paper proposes a novel two‐stream inflated 3D ConvNet based on the sparse regularization (SRI3D) model for action recognition. In order to allow the network to learn the sparsity of output, the ℓ1 norm is embedded in the loss function in regularization form in a plug‐and‐play manner. It can make the classification result after the fusion of the two‐stream network only be the category with the highest confidence in one of the streams and not the other cases. The proposed loss function based on sparse regularization makes the output vector of the neural network as sparse as possible so that the classification results will not be ambiguous. Experimental results show that compared with other state‐of‐the‐art models, this SRI3D has a competitive advantage on Kinetics‐400, Something‐Something V2, UCF‐101 and HMDB‐51.

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Circuit Design and Analysis of Smoothed $${l}_0$$ Norm Approximation for Sparse Signal Reconstruction

Che

Liu

2022

Circuits Syst Signal Process

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A Modified Algorithm Based on Smoothed L0 Norm in Compressive Sensing Signal Reconstruction

Cited by 11 publications

References 7 publications

An optimized sparse deep belief network with momentum factor for fault diagnosis of radar transceivers

An optimized sparse deep belief network with momentum factor for fault diagnosis of radar transceivers

SRI3D: Two‐stream inflated 3D ConvNet based on sparse regularization for action recognition

Circuit Design and Analysis of Smoothed $${l}_0$$ Norm Approximation for Sparse Signal Reconstruction

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