In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training cos...In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training costs and long inference times, limiting their practical application in denoising tasks. This paper proposes a new dual convolutional denoising network with skip connections(DECDNet), which achieves an ideal balance between denoising effect and network complexity. The proposed DECDNet consists of a noise estimation network, a multi-scale feature extraction network, a dual convolutional neural network, and dual attention mechanisms. The noise estimation network is used to estimate the noise level map, and the multi-scale feature extraction network is combined to improve the model's flexibility in obtaining image features. The dual convolutional neural network branch design includes convolution and dilated convolution interactive connections, with the lower branch consisting of dilated convolution layers, and both branches using skip connections. Experiments show that compared with other models, the proposed DECDNet achieves superior PSNR and SSIM values at all compared noise levels, especially at higher noise levels, showing robustness to images with higher noise levels. It also demonstrates better visual effects, maintaining a balance between denoising and detail preservation.展开更多
Driving fatigue is one of the major contributors to traffic accidents and poses a serious threat to road safety.Traditional driving fatigue detection methods suffer from limitations such as low classification accuracy...Driving fatigue is one of the major contributors to traffic accidents and poses a serious threat to road safety.Traditional driving fatigue detection methods suffer from limitations such as low classification accuracy,insufficient generalization ab ility,and poor noise resistance.To address these issues,this study proposes a novel driving fatigue detection approach based on a n improved dense connection convolutional network.This method innovatively utilizes raw Electroencephalogram(EEG)signals as inp ut to the model without requiring any data preprocessing,thereby enabling end-to-end feature extraction and classification.The network enhances information flow within dense blocks to promote feature reuse,employs multi-scale convolutional layers for fe ature extraction,and integrates an attention mechanism to assign adaptive weights to multi-scale feature channels.After completing primary feature extraction through stacked dense blocks and pooling layers,a multi-class classification function is applie d to detect driving fatigue.Experimental results on the SEED-VIG driving fatigue dataset show that the proposed method achieves an accuracy of 97.32%,a precision of 96.43%,a recall of 95.78%,and an F1-score of 96.10%.Compared to traditional approaches such as Convolutional Neural Networks(CNN)and Long Short-Term Memory Networks(LSTM),the accuracy improves by 5.14%and 3.45%,respectively.This study demonstrates that the proposed method has significant practical value:on one hand,the end-to-end a rchitecture greatly simplifies the complex feature engineering required by traditional methods;on the other hand,the incorporation of feature reuse and attention mechanisms substantially enhances the model’s classification performance and generalization capability,providing a new technical perspective for intelligent driving safety monitoring.展开更多
The precision and quality of machining in computer numerical control(CNC)machines are significantly impacted by the state of the tool.Therefore,it is essential and crucial to monitor the tool’s condition in real time...The precision and quality of machining in computer numerical control(CNC)machines are significantly impacted by the state of the tool.Therefore,it is essential and crucial to monitor the tool’s condition in real time during operation.To improve the monitoring accuracy of tool wear values,a tool wear monitoring approach is developed in this work,which is based on an improved integrated model of densely connected convolutional network(DenseNet)and gated recurrent unit(GRU),which incorporates data preprocessing via wavelet packet transform(WPT).Firstly,wavelet packet decomposition(WPD)is used to extract time-frequency domain features from the original timeseries monitoring signals of the tool.Secondly,the multidimensional deep features are extracted from DenseNet containing asymmetric convolution kernels,and feature fusion is performed.A dilation scheme is employed to acquire more historical data by utilizing dilated convolutional kernels with different dilation rates.Finally,the GRU is utilized to extract temporal features from the extracted deep-level signal features,and the feature mapping of these temporal features is then carried out by a fully connected neural network,which ultimately achieves the monitoring of tool wear values.Comprehensive experiments conducted on reference datasets show that the proposed model performs better in terms of accuracy and generalization than other cutting-edge tool wear monitoring algorithms.展开更多
基金funded by National Nature Science Foundation of China,grant number 61302188。
文摘In recent years, deep convolutional neural networks have shown superior performance in image denoising. However, deep network structures often come with a large number of model parameters, leading to high training costs and long inference times, limiting their practical application in denoising tasks. This paper proposes a new dual convolutional denoising network with skip connections(DECDNet), which achieves an ideal balance between denoising effect and network complexity. The proposed DECDNet consists of a noise estimation network, a multi-scale feature extraction network, a dual convolutional neural network, and dual attention mechanisms. The noise estimation network is used to estimate the noise level map, and the multi-scale feature extraction network is combined to improve the model's flexibility in obtaining image features. The dual convolutional neural network branch design includes convolution and dilated convolution interactive connections, with the lower branch consisting of dilated convolution layers, and both branches using skip connections. Experiments show that compared with other models, the proposed DECDNet achieves superior PSNR and SSIM values at all compared noise levels, especially at higher noise levels, showing robustness to images with higher noise levels. It also demonstrates better visual effects, maintaining a balance between denoising and detail preservation.
文摘Driving fatigue is one of the major contributors to traffic accidents and poses a serious threat to road safety.Traditional driving fatigue detection methods suffer from limitations such as low classification accuracy,insufficient generalization ab ility,and poor noise resistance.To address these issues,this study proposes a novel driving fatigue detection approach based on a n improved dense connection convolutional network.This method innovatively utilizes raw Electroencephalogram(EEG)signals as inp ut to the model without requiring any data preprocessing,thereby enabling end-to-end feature extraction and classification.The network enhances information flow within dense blocks to promote feature reuse,employs multi-scale convolutional layers for fe ature extraction,and integrates an attention mechanism to assign adaptive weights to multi-scale feature channels.After completing primary feature extraction through stacked dense blocks and pooling layers,a multi-class classification function is applie d to detect driving fatigue.Experimental results on the SEED-VIG driving fatigue dataset show that the proposed method achieves an accuracy of 97.32%,a precision of 96.43%,a recall of 95.78%,and an F1-score of 96.10%.Compared to traditional approaches such as Convolutional Neural Networks(CNN)and Long Short-Term Memory Networks(LSTM),the accuracy improves by 5.14%and 3.45%,respectively.This study demonstrates that the proposed method has significant practical value:on one hand,the end-to-end a rchitecture greatly simplifies the complex feature engineering required by traditional methods;on the other hand,the incorporation of feature reuse and attention mechanisms substantially enhances the model’s classification performance and generalization capability,providing a new technical perspective for intelligent driving safety monitoring.
基金supported by the National Natural Science Foundation of China(62020106003,62273177,62233009)the Natural Science Foundation of Jiangsu Province of China(BK20222012)+2 种基金the Programme of Introducing Talents of Discipline to Universities of China(B20007)the Fundamental Research Funds for the Central Universities(NI2024001)the National Key Laboratory of Space Intelligent Control(HTKJ2023KL502006).
文摘The precision and quality of machining in computer numerical control(CNC)machines are significantly impacted by the state of the tool.Therefore,it is essential and crucial to monitor the tool’s condition in real time during operation.To improve the monitoring accuracy of tool wear values,a tool wear monitoring approach is developed in this work,which is based on an improved integrated model of densely connected convolutional network(DenseNet)and gated recurrent unit(GRU),which incorporates data preprocessing via wavelet packet transform(WPT).Firstly,wavelet packet decomposition(WPD)is used to extract time-frequency domain features from the original timeseries monitoring signals of the tool.Secondly,the multidimensional deep features are extracted from DenseNet containing asymmetric convolution kernels,and feature fusion is performed.A dilation scheme is employed to acquire more historical data by utilizing dilated convolutional kernels with different dilation rates.Finally,the GRU is utilized to extract temporal features from the extracted deep-level signal features,and the feature mapping of these temporal features is then carried out by a fully connected neural network,which ultimately achieves the monitoring of tool wear values.Comprehensive experiments conducted on reference datasets show that the proposed model performs better in terms of accuracy and generalization than other cutting-edge tool wear monitoring algorithms.
