Robust neural dynamics with adaptive coefficient applied to solve the dynamic matrix square root

Jiang, Chengze; Wu, Chaomin; Xiao, Xiuchun; Lin, Cong

doi:10.1007/s40747-022-00954-9

Cited by 8 publications

(5 citation statements)

References 42 publications

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“…As can be seen from Figure 8, the addition of the attention module CBAM in the network results in a change in the model's loss function compared to the original network's loss function, indicating that the model's robustness (Jiang et al, 2022a) is affected. Replacing the network's loss function with SIoU can greatly improve the poor robustness introduced by the attention mechanism in the model.…”

Section: Comparison Of Experimental Resultsmentioning

confidence: 99%

A deep learning approach for object detection of rockfish in challenging underwater environments

Liu,

Jiang,

Hou

et al. 2023

Front. Mar. Sci.

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IntroductionPreserving the marine ecological environment and safeguarding marine species is a global priority. However, human overfishing has led to a drastic decline in fish species with longer growth cycles, disrupting the equilibrium of the marine ecosystem. To address this issue, researchers are turning to deep learning techniques and state-of-the-art underwater devices, such as underwater robots, to explore the aquatic environment and monitor the activities of endangered populations. This approach has emerged as a focal point of recent research in protecting the marine ecological environment. This study employs a deep learning-based object detection algorithm to identify fish species in complex underwater environments.MethodsThe algorithm is built upon the You Only Look Once version 7(YOLOv7) algorithm, with the addition of the attention mechanism Convolutional Block Attention Module (CBAM) in the network’s backbone. CBAM enhances the feature maps through the fusion of spatial attention and channel attention, ultimately improving the robustness and accuracy of the model’s inference by replacing the original loss function CIoU with SCYLLAIntersection over Union(SIoU). In this paper, the rockfish pictures in the dataset Label Fishes in the Wild published by the National Marine Fisheries Service are selected, and the underwater image enhancement model (UWCNN) is introduced to process the pictures.ResultThe experimental results show that the mean average precision (mAP) value of the improved model on the test set is 94.4%, which is 3.5% higher than the original YOLOv7 model, and the precision and recall rate are 99.1% and 99%, respectively. The detection performance of the algorithm in the field of complex underwater environment is improved.DiscussionThe underwater fish detection scheme proposed in this study holds significant practical value and significance in promoting the conservation of marine ecosystems and the protection of fish species.

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Section: Comparison Of Experimental Resultsmentioning

confidence: 99%

A deep learning approach for object detection of rockfish in challenging underwater environments

Liu,

Jiang,

Hou

et al. 2023

Front. Mar. Sci.

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“…However, the OZNN method cannot deal with the DQRF (1) more efficiently. In the noisy case, even non-convergence can occur [35]. Therefore, the BAFARNN model is proposed in the following part.…”

Section: Bafarnn Solutionmentioning

confidence: 99%

“…In [32], the varying-parameter ZNNs are shown to be better than the traditional ZNN model in solving the dynamic Lyapunov function and Stein matrix equation. In practical applications, considering the variations in algorithm parameter values in different domains, some researchers have proposed intelligent optimization algorithms incorporating adaptive coefficients [33][34][35][36]. Chen et al [37] add adaptive parameters to the controller design to update data adaptively and ensure the controller's stability.…”

Section: Introductionmentioning

confidence: 99%

Bounded Adaptive Function Activated Recurrent Neural Network for Solving the Dynamic QR Factorization

Yang

Xie

et al. 2023

Mathematics

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The orthogonal triangular factorization (QRF) method is a widespread tool to calculate eigenvalues and has been used for many practical applications. However, as an emerging topic, only a few works have been devoted to handling dynamic QR factorization (DQRF). Moreover, the traditional methods for dynamic problems suffer from lagging errors and are susceptible to noise, thereby being unable to satisfy the requirements of the real-time solution. In this paper, a bounded adaptive function activated recurrent neural network (BAFARNN) is proposed to solve the DQRF with a faster convergence speed and enhance existing solution methods’ robustness. Theoretical analysis shows that the model can achieve global convergence in different environments. The results of the systematic experiment show that the BAFARNN model outperforms both the original ZNN (OZNN) model and the noise-tolerant zeroing neural network (NTZNN) model in terms of accuracy and convergence speed. This is true for both single constants and time-varying noise disturbances.

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“…Deep learning is an efficient method for data analysis [6,7]. Jiang et al addressed the reliability concerns of zeroing neural networks using a robust neural dynamic model [8]. Wu et al developed an unsupervised generative adversarial network to effectively fuse panchromatic and multispectral images [9].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Sensor Data Preprocessing Method for OCT Fundus Image Watermarking Using an RCNN

Lin,

Chen

2024

CMES

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Watermarks can provide reliable and secure copyright protection for optical coherence tomography (OCT) fundus images. The effective image segmentation is helpful for promoting OCT image watermarking. However, OCT images have a large amount of low-quality data, which seriously affects the performance of segmentation methods. Therefore, this paper proposes an effective segmentation method for OCT fundus image watermarking using a rough convolutional neural network (RCNN). First, the rough-set-based feature discretization module is designed to preprocess the input data. Second, a dual attention mechanism for feature channels and spatial regions in the CNN is added to enable the model to adaptively select important information for fusion. Finally, the refinement module for enhancing the extraction power of multi-scale information is added to improve the edge accuracy in segmentation. RCNN is compared with CE-Net and MultiResUNet on 83 gold standard 3D retinal OCT data samples. The average dice similarly coefficient (DSC) obtained by RCNN is 6% higher than that of CE-Net. The average 95 percent Hausdorff distance (95HD) and average symmetric surface distance (ASD) obtained by RCNN are 32.4% and 33.3% lower than those of MultiResUNet, respectively. We also evaluate the effect of feature discretization, as well as analyze the initial learning rate of RCNN and conduct ablation experiments with the four different models. The experimental results indicate that our method can improve the segmentation accuracy of OCT fundus images, providing strong support for its application in medical image watermarking.

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Robust neural dynamics with adaptive coefficient applied to solve the dynamic matrix square root

Cited by 8 publications

References 42 publications

A deep learning approach for object detection of rockfish in challenging underwater environments

A deep learning approach for object detection of rockfish in challenging underwater environments

Bounded Adaptive Function Activated Recurrent Neural Network for Solving the Dynamic QR Factorization

An Intelligent Sensor Data Preprocessing Method for OCT Fundus Image Watermarking Using an RCNN

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