Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increa...Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.展开更多
The increasingly complex and interconnected train control information network is vulnerable to a variety of malicious traffic attacks,and the existing malicious traffic detection methods mainly rely on machine learnin...The increasingly complex and interconnected train control information network is vulnerable to a variety of malicious traffic attacks,and the existing malicious traffic detection methods mainly rely on machine learning,such as poor robustness,weak generalization,and a lack of ability to learn common features.Therefore,this paper proposes a malicious traffic identification method based on stacked sparse denoising autoencoders combined with a regularized extreme learning machine through particle swarm optimization.Firstly,the simulation environment of the Chinese train control system-3,was constructed for data acquisition.Then Pearson coefficient and other methods are used for pre-processing,then a stacked sparse denoising autoencoder is used to achieve nonlinear dimensionality reduction of features,and finally regularization extreme learning machine optimized by particle swarm optimization is used to achieve classification.Experimental data show that the proposed method has good training performance,with an average accuracy of 97.57%and a false negative rate of 2.43%,which is better than other alternative methods.In addition,ablation experiments were performed to evaluate the contribution of each component,and the results showed that the combination of methods was superior to individual methods.To further evaluate the generalization ability of the model in different scenarios,publicly available data sets of industrial control system networks were used.The results show that the model has robust detection capability in various types of network attacks.展开更多
Floating wastes in rivers have specific characteristics such as small scale,low pixel density and complex backgrounds.These characteristics make it prone to false and missed detection during image analysis,thus result...Floating wastes in rivers have specific characteristics such as small scale,low pixel density and complex backgrounds.These characteristics make it prone to false and missed detection during image analysis,thus resulting in a degradation of detection performance.In order to tackle these challenges,a floating waste detection algorithm based on YOLOv7 is proposed,which combines the improved GFPN(Generalized Feature Pyramid Network)and a long-range attention mechanism.Firstly,we import the improved GFPN to replace the Neck of YOLOv7,thus providing more effective information transmission that can scale into deeper networks.Secondly,the convolution-based and hardware-friendly long-range attention mechanism is introduced,allowing the algorithm to rapidly generate an attention map with a global receptive field.Finally,the algorithm adopts the WiseIoU optimization loss function to achieve adaptive gradient gain allocation and alleviate the negative impact of low-quality samples on the gradient.The simulation results reveal that the proposed algorithm has achieved a favorable average accuracy of 86.3%in real-time scene detection tasks.This marks a significant enhancement of approximately 6.3%compared with the baseline,indicating the algorithm's good performance in floating waste detection.展开更多
Android malware has exploded in popularity in recent years,due to the platform’s dominance of the mobile market.With the advancement of deep learning technology,numerous deep learning-based works have been proposed f...Android malware has exploded in popularity in recent years,due to the platform’s dominance of the mobile market.With the advancement of deep learning technology,numerous deep learning-based works have been proposed for the classification of Android malware.Deep learning technology is designed to handle a large amount of raw and continuous data,such as image content data.However,it is incompatible with discrete features,i.e.,features gathered from multiple sources.Furthermore,if the feature set is already well-extracted and sparsely distributed,this technology is less effective than traditional machine learning.On the other hand,a wide learning model can expand the feature set to enhance the classification accuracy.To maximize the benefits of both methods,this study proposes combining the components of deep learning based on multi-branch CNNs(Convolutional Network Neural)with wide learning method.The feature set is evaluated and dynamically partitioned according to its meaning and generalizability to subsets when used as input to the model’s wide or deep component.The proposed model,partition,and feature set quality are all evaluated using the K-fold cross validation method on a composite dataset with three types of features:API,permission,and raw image.The accuracy with Wide and Deep CNN(WDCNN)model is 98.64%,improved by 1.38%compared to RNN(Recurrent Neural Network)model.展开更多
In this paper, the generalized feature concept is put forward according to concurrent engineering. An integrated product model is established based on the generalized feature according to STEP in order to provide enri...In this paper, the generalized feature concept is put forward according to concurrent engineering. An integrated product model is established based on the generalized feature according to STEP in order to provide enrichment information for product concurrent development process. The integration of the information and function of CAD/CAPP can be realized based on the integrated product model that supports concurrent engineering. IPM has been used successfully in product concurrent development.展开更多
Using machine learning to predict and design materials is an important mean of accelerating material development.One way to improve the accuracy of machine learning predictions is to introduce material structures as d...Using machine learning to predict and design materials is an important mean of accelerating material development.One way to improve the accuracy of machine learning predictions is to introduce material structures as descriptors.However,thecomplexity ofcomputing material structures limits the practical use of these models.To address this challenge and improve prediction accuracy in small data sets,we develop a generative network framework:Elemental Features enhanced and Transferring corrected data augmentation in Generative Adversarial Networks(EFTGAN).Combining the elemental convolution technique with Generative Adversarial Networks(GAN),EFTGAN provides a robust and efficient approach for generating data containing elemental and structural information that can be used not only for data augmentation to improve model accuracy,but also for prediction when the structures are unknown.Applying this framework to the FeNiCoCrMn/Pd high-entropy alloys,we successfully improve the prediction accuracy in a small data set and predict the concentrationdependent formation energies,lattices,and magnetic moments in quinary systems.This study provides a new algorithm to improve the performance and usability of deep learning with structures as inputs,which is effective and accurate for the prediction and development of materials for small data sets.展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R196)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Cardiovascular diseases(CVDs)continue to present a leading cause ofmortalityworldwide,emphasizing the importance of early and accurate prediction.Electrocardiogram(ECG)signals,central to cardiac monitoring,have increasingly been integratedwithDeep Learning(DL)for real-time prediction of CVDs.However,DL models are prone to performance degradation due to concept drift and to catastrophic forgetting.To address this issue,we propose a realtime CVDs prediction approach,referred to as ADWIN-GFR that combines Convolutional Neural Network(CNN)layers,for spatial feature extraction,with Gated Recurrent Units(GRU),for temporal modeling,alongside adaptive drift detection and mitigation mechanisms.The proposed approach integratesAdaptiveWindowing(ADWIN)for realtime concept drift detection,a fine-tuning strategy based on Generative Features Replay(GFR)to preserve previously acquired knowledge,and a dynamic replay buffer ensuring variance,diversity,and data distribution coverage.Extensive experiments conducted on the MIT-BIH arrhythmia dataset demonstrate that ADWIN-GFR outperforms standard fine-tuning techniques,achieving an average post-drift accuracy of 95.4%,amacro F1-score of 93.9%,and a remarkably low forgetting score of 0.9%.It also exhibits an average drift detection delay of 12 steps and achieves an adaptation gain of 17.2%.These findings underscore the potential of ADWIN-GFR for deployment in real-world cardiac monitoring systems,including wearable ECG devices and hospital-based patient monitoring platforms.
文摘The increasingly complex and interconnected train control information network is vulnerable to a variety of malicious traffic attacks,and the existing malicious traffic detection methods mainly rely on machine learning,such as poor robustness,weak generalization,and a lack of ability to learn common features.Therefore,this paper proposes a malicious traffic identification method based on stacked sparse denoising autoencoders combined with a regularized extreme learning machine through particle swarm optimization.Firstly,the simulation environment of the Chinese train control system-3,was constructed for data acquisition.Then Pearson coefficient and other methods are used for pre-processing,then a stacked sparse denoising autoencoder is used to achieve nonlinear dimensionality reduction of features,and finally regularization extreme learning machine optimized by particle swarm optimization is used to achieve classification.Experimental data show that the proposed method has good training performance,with an average accuracy of 97.57%and a false negative rate of 2.43%,which is better than other alternative methods.In addition,ablation experiments were performed to evaluate the contribution of each component,and the results showed that the combination of methods was superior to individual methods.To further evaluate the generalization ability of the model in different scenarios,publicly available data sets of industrial control system networks were used.The results show that the model has robust detection capability in various types of network attacks.
基金Supported by the Science Foundation of the Shaanxi Provincial Department of Science and Technology,General Program-Youth Program(2022JQ-695)the Scientific Research Program Funded by Education Department of Shaanxi Provincial Government(22JK0378)+1 种基金the Talent Program of Weinan Normal University(2021RC20)the Educational Reform Research Project(JG202342)。
文摘Floating wastes in rivers have specific characteristics such as small scale,low pixel density and complex backgrounds.These characteristics make it prone to false and missed detection during image analysis,thus resulting in a degradation of detection performance.In order to tackle these challenges,a floating waste detection algorithm based on YOLOv7 is proposed,which combines the improved GFPN(Generalized Feature Pyramid Network)and a long-range attention mechanism.Firstly,we import the improved GFPN to replace the Neck of YOLOv7,thus providing more effective information transmission that can scale into deeper networks.Secondly,the convolution-based and hardware-friendly long-range attention mechanism is introduced,allowing the algorithm to rapidly generate an attention map with a global receptive field.Finally,the algorithm adopts the WiseIoU optimization loss function to achieve adaptive gradient gain allocation and alleviate the negative impact of low-quality samples on the gradient.The simulation results reveal that the proposed algorithm has achieved a favorable average accuracy of 86.3%in real-time scene detection tasks.This marks a significant enhancement of approximately 6.3%compared with the baseline,indicating the algorithm's good performance in floating waste detection.
文摘Android malware has exploded in popularity in recent years,due to the platform’s dominance of the mobile market.With the advancement of deep learning technology,numerous deep learning-based works have been proposed for the classification of Android malware.Deep learning technology is designed to handle a large amount of raw and continuous data,such as image content data.However,it is incompatible with discrete features,i.e.,features gathered from multiple sources.Furthermore,if the feature set is already well-extracted and sparsely distributed,this technology is less effective than traditional machine learning.On the other hand,a wide learning model can expand the feature set to enhance the classification accuracy.To maximize the benefits of both methods,this study proposes combining the components of deep learning based on multi-branch CNNs(Convolutional Network Neural)with wide learning method.The feature set is evaluated and dynamically partitioned according to its meaning and generalizability to subsets when used as input to the model’s wide or deep component.The proposed model,partition,and feature set quality are all evaluated using the K-fold cross validation method on a composite dataset with three types of features:API,permission,and raw image.The accuracy with Wide and Deep CNN(WDCNN)model is 98.64%,improved by 1.38%compared to RNN(Recurrent Neural Network)model.
文摘In this paper, the generalized feature concept is put forward according to concurrent engineering. An integrated product model is established based on the generalized feature according to STEP in order to provide enrichment information for product concurrent development process. The integration of the information and function of CAD/CAPP can be realized based on the integrated product model that supports concurrent engineering. IPM has been used successfully in product concurrent development.
基金supported by the National Natural Science Foundation ofChina(grant no.92270104)partially by Grant-in-Aids for Scientific Research on innovative Areas on High Entropy Alloys through the grant number P18H05454 of JSPS,Japan.Authors acknowledge the Center of High Performance Computing,Tsinghua University and the Center for Computational Materials Science of the Institute for Materials Research,Tohoku University for the support of the supercomputing facilities.Figure 1 is drawn by FigDraw.
文摘Using machine learning to predict and design materials is an important mean of accelerating material development.One way to improve the accuracy of machine learning predictions is to introduce material structures as descriptors.However,thecomplexity ofcomputing material structures limits the practical use of these models.To address this challenge and improve prediction accuracy in small data sets,we develop a generative network framework:Elemental Features enhanced and Transferring corrected data augmentation in Generative Adversarial Networks(EFTGAN).Combining the elemental convolution technique with Generative Adversarial Networks(GAN),EFTGAN provides a robust and efficient approach for generating data containing elemental and structural information that can be used not only for data augmentation to improve model accuracy,but also for prediction when the structures are unknown.Applying this framework to the FeNiCoCrMn/Pd high-entropy alloys,we successfully improve the prediction accuracy in a small data set and predict the concentrationdependent formation energies,lattices,and magnetic moments in quinary systems.This study provides a new algorithm to improve the performance and usability of deep learning with structures as inputs,which is effective and accurate for the prediction and development of materials for small data sets.
