Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowad...Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.展开更多
Lightweight convolutional neural networks(CNNs)have simple structures but struggle to comprehensively and accurately extract important semantic information from images.While attention mechanisms can enhance CNNs by le...Lightweight convolutional neural networks(CNNs)have simple structures but struggle to comprehensively and accurately extract important semantic information from images.While attention mechanisms can enhance CNNs by learning distinctive representations,most existing spatial and hybrid attention methods focus on local regions with extensive parameters,making them unsuitable for lightweight CNNs.In this paper,we propose a self-attention mechanism tailored for lightweight networks,namely the brief self-attention module(BSAM).BSAM consists of the brief spatial attention(BSA)and advanced channel attention blocks.Unlike conventional self-attention methods with many parameters,our BSA block improves the performance of lightweight networks by effectively learning global semantic representations.Moreover,BSAM can be seamlessly integrated into lightweight CNNs for end-to-end training,maintaining the network’s lightweight and mobile characteristics.We validate the effectiveness of the proposed method on image classification tasks using the Food-101,Caltech-256,and Mini-ImageNet datasets.展开更多
A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumor...A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.展开更多
Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features i...Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology,as well as the existence of information redundancy in cross-modal data fusion,this paper proposes amultimodal fusion framework based on cross-modal collaboration and weighted attention mechanism.In terms of feature extraction,the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture,overcoming the limitations of traditional single-modality models in capturing either local or global information;in terms of fusion strategy,the framework innovatively designs a cross-modal dynamic fusion strategy,combining overlappingmulti-head self-attention modules with a bidirectional feature alignment mechanism,addressing the bottlenecks of low feature interaction efficiency and excessive attention fusion computations in traditional parallel fusion,and further introduces cross-domain local integration technology,which enhances the representation ability of the lesion area through pixel-level feature recalibration and optimizes the diagnostic robustness of complex cases.Experiments show that the framework exhibits excellent feature expression and generalization performance in cross-domain scenarios of ophthalmic medical images and natural images,providing a high-precision,low-redundancy fusion paradigm for multimodal medical image analysis,and promoting the upgrade of intelligent diagnosis and treatment fromsingle-modal static analysis to dynamic decision-making.展开更多
Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the pun...Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.展开更多
For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high com...For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high computational complexity,and long reconstruction time.An image compressed sensing reconstruction network based on self-attention mechanism(SAMNet)was proposed.For the compressed sampling,self-attention convolution was designed,which was conducive to capturing richer features,so that the compressed sensing measurement value retained more image structure information.For the reconstruction,a self-attention mechanism was introduced in the convolutional neural network.A reconstruction network including residual blocks,bottleneck transformer(BoTNet),and dense blocks was proposed,which strengthened the transfer of image features and reduced the amount of parameters dramatically.Under the Set5 dataset,when the measurement rates are 0.01,0.04,0.10,and 0.25,the average peak signal-to-noise ratio(PSNR)of SAMNet is improved by 1.27,1.23,0.50,and 0.15 dB,respectively,compared to the CSNet+.The running time of reconstructing a 256×256 image is reduced by 0.1473,0.1789,0.2310,and 0.2524 s compared to ReconNet.Experimental results showed that SAMNet improved the quality of reconstructed images and reduced the reconstruction time.展开更多
The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiment...The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.展开更多
As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additi...As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.展开更多
Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operati...Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.展开更多
With the legislative development,the organic and inorganic composition separation has become the primary requirement for sewer sediment disposal,however the relevant technology has been rarely reported and the driving...With the legislative development,the organic and inorganic composition separation has become the primary requirement for sewer sediment disposal,however the relevant technology has been rarely reported and the driving mechanism was still unclear.In this study,direct disintegration of biopolymers and indirect broken of connection point were investigated on the hydrolysis and component separation.Three typical sewer sediment treatment approaches,i.e.,alkaline,thermal and cation exchange treatments were proposed,which represented the hydrolysis-driving forces of chemical hydrolysis,physical hydrolysis and innovative cation bridging break-age.The results showed that the organic and inorganic separation rates of sewer sediment driven by alkaline,thermal and cation exchange treatments reached 21.26%,23.80%,and 19.56%-48.0%,respectively,compared to 4.43%in control.The secondary structure of proteins was disrupted,transitioning from𝛼α-helix to𝛽β-turn and random coil.Meanwhile,much biopolymers were released from solid to the liquid phase.From thermody-namic perspective,sewer sediment deposition was controlled by short-range interfacial interactions described by extended Derjaguin-Landau-Verwey-Overbeek theory.Additionally,the separation of organic and inorganic components was positively correlated with the thermodynamic parameters(Corr=0.87),highlighted the robust-ness of various driving forces.And the flocculation energy barriers were 2.40(alkaline),1.60 times(thermal),and 4.02–4.97 times(cation exchange)compared to control group.The findings revealed the contrition differ-ence of direct disintegration of gelatinous biopolymers and indirect breakage of composition connection sites in sediment composition separation,filling the critical gaps in understanding the specific mechanisms of sediment biopolymer disintegration and intermolecular connection breakage.展开更多
Zn-I_(2) batteries have emerged as promising next-generation energy storage systems owing to their inherent safety,environmental compatibility,rapid reaction kinetics,and small voltage hysteresis.Nevertheless,two crit...Zn-I_(2) batteries have emerged as promising next-generation energy storage systems owing to their inherent safety,environmental compatibility,rapid reaction kinetics,and small voltage hysteresis.Nevertheless,two critical challenges,i.e.,zinc dendrite growth and polyiodide shuttle effect,severely impede their commercial viability.To conquer these limitations,this study develops a multifunctional separator fabricated from straw-derived carboxylated nanocellulose,with its negative charge density further reinforced by anionic polyacrylamide incorporation.This modification simultaneously improves the separator’s mechanical properties,ionic conductivity,and Zn^(2+)ion transfer number.Remarkably,despite its ultrathin 20μm profile,the engineered separator demonstrates exceptional dendrite suppression and parasitic reaction inhibition,enabling Zn//Zn symmetric cells to achieve impressive cycle life(>1800 h at 2 m A cm^(-2)/2 m Ah cm^(-2))while maintaining robust performance even at ultrahigh areal capacities(25 m Ah cm^(-2)).Additionally,the separator’s anionic characteristic effectively blocks polyiodide migration through electrostatic repulsion,yielding Zn-I_(2) batteries with outstanding rate capability(120.7 m Ah g^(-1)at 5 A g^(-1))and excellent cyclability(94.2%capacity retention after 10,000 cycles).And superior cycling stability can still be achieved under zinc-deficient condition and pouch cell configuration.This work establishes a new paradigm for designing high-performance zinc-based energy storage systems through rational separator engineering.展开更多
Magnetically separable mesoporous activated carbon was prepared from brown coal in the presence of Fe3O4 as a bi-functional additive.Magnetic activated carbon(MAC)was characterized by lowtemperature nitrogen adsorptio...Magnetically separable mesoporous activated carbon was prepared from brown coal in the presence of Fe3O4 as a bi-functional additive.Magnetic activated carbon(MAC)was characterized by lowtemperature nitrogen adsorption,scanning electron microscopy(SEM),transmission electron microscopy(TEM),X-ray diffraction(XRD),X-ray photoelectron spectroscopy(XPS)and vibrating sample magnetometry(VSM).The evolution behaviors and transition mechanism of Fe3O4 during the preparation of MAC were investigated.The results show that prepared MAC with 6 wt%Fe3O4 addition having a specific surface area and mesopore ratio of 370 m^2·g^-1 and 55.7%,which meet the requirements of adsorption application and magnetic recovery.Highly dispersed iron-containing aggregates with the size of 0.1 lm in the MAC were observed.During the preparation of MAC,Fe3O4 could enhance the escape of volatiles during the carbonization.Fe3O4 could also accelerate burning off the carbon wall during activation,which leads to enlarging micropore size,then resulting in the generation of mesopore and macropore.As a result,a part of Fe3O4 converted into FeO,FeOOH,a-Fe,c-Fe,Fe2SiO4 and compound of Aluminum-iron-silicon.The prepared activated carbon,which was magnetized by both of residual Fe3O4,reduced a-Fe and c-Fe,can be easily separated from the original solution by external magnetic field.展开更多
An approximation for the one-way wave operator takes the form of separated space and wave-number variables and makes it possible to use the FFT, which results in a great improvement in the computational efficiency. Fr...An approximation for the one-way wave operator takes the form of separated space and wave-number variables and makes it possible to use the FFT, which results in a great improvement in the computational efficiency. From the function approximation perspective, the OSA method shares the same separable approximation format to the one-way wave operator as other separable approximation methods but it is the only global function approximation among these methods. This leads to a difference in the phase error curve, impulse response, and migration result from other separable approximation methods. The difference is that the OSA method has higher accuracy, and the sensitivity to the velocity variation declines with increasing order.展开更多
Let H be a finite dimensional Hopf algebra over a field and A an H-module algebra. The H induces an action on the CA#H(A) by adjoint and CA#H(A)H= Z(A # H) = C,where CA#H(A) denotes the centralizer which algebra A in ...Let H be a finite dimensional Hopf algebra over a field and A an H-module algebra. The H induces an action on the CA#H(A) by adjoint and CA#H(A)H= Z(A # H) = C,where CA#H(A) denotes the centralizer which algebra A in A # H and Z(A # H) the center of A # H.The aim of this paper is to discuss ,the Galois conditions on the centralizer CA# H(A).We prove that CA# H(A)/ZA # H is H* -Galois if and only if CA# H(A)# H/CA# H(A) is H-separable). Furthermore , if H is a finite dimensional semisimple Hopf algebra and CA# H(A)# H is an Azumaya C-algebra or A # H/A is H-separable, CA# H(A) satisfies the double centralizer property in CA# H(A)# H, CA# H(A)/C is separable and there exists a cocommutative left integral t ∈∫1H,then CA# H(A)/C is H*-Galois.展开更多
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
基金supported by the National Key Research and Development Program of China(No.2021YFA0715900).
文摘Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.
文摘Lightweight convolutional neural networks(CNNs)have simple structures but struggle to comprehensively and accurately extract important semantic information from images.While attention mechanisms can enhance CNNs by learning distinctive representations,most existing spatial and hybrid attention methods focus on local regions with extensive parameters,making them unsuitable for lightweight CNNs.In this paper,we propose a self-attention mechanism tailored for lightweight networks,namely the brief self-attention module(BSAM).BSAM consists of the brief spatial attention(BSA)and advanced channel attention blocks.Unlike conventional self-attention methods with many parameters,our BSA block improves the performance of lightweight networks by effectively learning global semantic representations.Moreover,BSAM can be seamlessly integrated into lightweight CNNs for end-to-end training,maintaining the network’s lightweight and mobile characteristics.We validate the effectiveness of the proposed method on image classification tasks using the Food-101,Caltech-256,and Mini-ImageNet datasets.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:1055-829-2024).
文摘A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.
基金funded by the Ongoing Research Funding Program(ORF-2025-102),King Saud University,Riyadh,Saudi Arabiaby the Science and Technology Research Programof Chongqing Municipal Education Commission(Grant No.KJQN202400813)by the Graduate Research Innovation Project(Grant Nos.yjscxx2025-269-193 and CYS25618).
文摘Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology,as well as the existence of information redundancy in cross-modal data fusion,this paper proposes amultimodal fusion framework based on cross-modal collaboration and weighted attention mechanism.In terms of feature extraction,the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture,overcoming the limitations of traditional single-modality models in capturing either local or global information;in terms of fusion strategy,the framework innovatively designs a cross-modal dynamic fusion strategy,combining overlappingmulti-head self-attention modules with a bidirectional feature alignment mechanism,addressing the bottlenecks of low feature interaction efficiency and excessive attention fusion computations in traditional parallel fusion,and further introduces cross-domain local integration technology,which enhances the representation ability of the lesion area through pixel-level feature recalibration and optimizes the diagnostic robustness of complex cases.Experiments show that the framework exhibits excellent feature expression and generalization performance in cross-domain scenarios of ophthalmic medical images and natural images,providing a high-precision,low-redundancy fusion paradigm for multimodal medical image analysis,and promoting the upgrade of intelligent diagnosis and treatment fromsingle-modal static analysis to dynamic decision-making.
文摘Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.
基金supported by National Natural Science Foundation of China(Nos.61261016,61661025)Science and Technology Plan of Gansu Province(No.20JR10RA273).
文摘For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high computational complexity,and long reconstruction time.An image compressed sensing reconstruction network based on self-attention mechanism(SAMNet)was proposed.For the compressed sampling,self-attention convolution was designed,which was conducive to capturing richer features,so that the compressed sensing measurement value retained more image structure information.For the reconstruction,a self-attention mechanism was introduced in the convolutional neural network.A reconstruction network including residual blocks,bottleneck transformer(BoTNet),and dense blocks was proposed,which strengthened the transfer of image features and reduced the amount of parameters dramatically.Under the Set5 dataset,when the measurement rates are 0.01,0.04,0.10,and 0.25,the average peak signal-to-noise ratio(PSNR)of SAMNet is improved by 1.27,1.23,0.50,and 0.15 dB,respectively,compared to the CSNet+.The running time of reconstructing a 256×256 image is reduced by 0.1473,0.1789,0.2310,and 0.2524 s compared to ReconNet.Experimental results showed that SAMNet improved the quality of reconstructed images and reduced the reconstruction time.
文摘The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.
基金supported by the National Key Research and Development Program of China(2020YFC2200901)。
文摘As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.
基金supported by the National Key R&D Program of China(No.2022YFB4301102).
文摘Currently,most trains are equipped with dedicated cameras for capturing pantograph videos.Pantographs are core to the high-speed-railway pantograph-catenary system,and their failure directly affects the normal operation of high-speed trains.However,given the complex and variable real-world operational conditions of high-speed railways,there is no real-time and robust pantograph fault-detection method capable of handling large volumes of surveillance video.Hence,it is of paramount importance to maintain real-time monitoring and analysis of pantographs.Our study presents a real-time intelligent detection technology for identifying faults in high-speed railway pantographs,utilizing a fusion of self-attention and convolution features.We delved into lightweight multi-scale feature-extraction and fault-detection models based on deep learning to detect pantograph anomalies.Compared with traditional methods,this approach achieves high recall and accuracy in pantograph recognition,accurately pinpointing issues like discharge sparks,pantograph horns,and carbon pantograph-slide malfunctions.After experimentation and validation with actual surveillance videos of electric multiple-unit train,our algorithmic model demonstrates real-time,high-accuracy performance even under complex operational conditions.
基金supported by Shaanxi Key Research and Development Program(No.2024SF-YBXM-546)the National Natural Science Foundation of China(No.52470161)the State Key Laboratory of Pollution Control and Resource Reuse Foundation(No.PCRRF21007).
文摘With the legislative development,the organic and inorganic composition separation has become the primary requirement for sewer sediment disposal,however the relevant technology has been rarely reported and the driving mechanism was still unclear.In this study,direct disintegration of biopolymers and indirect broken of connection point were investigated on the hydrolysis and component separation.Three typical sewer sediment treatment approaches,i.e.,alkaline,thermal and cation exchange treatments were proposed,which represented the hydrolysis-driving forces of chemical hydrolysis,physical hydrolysis and innovative cation bridging break-age.The results showed that the organic and inorganic separation rates of sewer sediment driven by alkaline,thermal and cation exchange treatments reached 21.26%,23.80%,and 19.56%-48.0%,respectively,compared to 4.43%in control.The secondary structure of proteins was disrupted,transitioning from𝛼α-helix to𝛽β-turn and random coil.Meanwhile,much biopolymers were released from solid to the liquid phase.From thermody-namic perspective,sewer sediment deposition was controlled by short-range interfacial interactions described by extended Derjaguin-Landau-Verwey-Overbeek theory.Additionally,the separation of organic and inorganic components was positively correlated with the thermodynamic parameters(Corr=0.87),highlighted the robust-ness of various driving forces.And the flocculation energy barriers were 2.40(alkaline),1.60 times(thermal),and 4.02–4.97 times(cation exchange)compared to control group.The findings revealed the contrition differ-ence of direct disintegration of gelatinous biopolymers and indirect breakage of composition connection sites in sediment composition separation,filling the critical gaps in understanding the specific mechanisms of sediment biopolymer disintegration and intermolecular connection breakage.
基金the financial support from the Natural Science Foundation of Jiangsu Province(BK20231292)the Jiangsu Agricultural Science and Technology Innovation Fund(CX(24)3091)+6 种基金the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX25_1429)the National Key R&D Program of China(2024YFE0109200)the Fundamental Research Funds for the Central Universities(No.2024300440)Guangdong Basic and Applied Basic Research Foundation(2025A1515011098)the National Natural Science Foundation of China(12464032)the Natural Science Foundation of Jiangxi Province(20232BAB201032)Ji'an Science and Technology Plan Project(2024H-100301)。
文摘Zn-I_(2) batteries have emerged as promising next-generation energy storage systems owing to their inherent safety,environmental compatibility,rapid reaction kinetics,and small voltage hysteresis.Nevertheless,two critical challenges,i.e.,zinc dendrite growth and polyiodide shuttle effect,severely impede their commercial viability.To conquer these limitations,this study develops a multifunctional separator fabricated from straw-derived carboxylated nanocellulose,with its negative charge density further reinforced by anionic polyacrylamide incorporation.This modification simultaneously improves the separator’s mechanical properties,ionic conductivity,and Zn^(2+)ion transfer number.Remarkably,despite its ultrathin 20μm profile,the engineered separator demonstrates exceptional dendrite suppression and parasitic reaction inhibition,enabling Zn//Zn symmetric cells to achieve impressive cycle life(>1800 h at 2 m A cm^(-2)/2 m Ah cm^(-2))while maintaining robust performance even at ultrahigh areal capacities(25 m Ah cm^(-2)).Additionally,the separator’s anionic characteristic effectively blocks polyiodide migration through electrostatic repulsion,yielding Zn-I_(2) batteries with outstanding rate capability(120.7 m Ah g^(-1)at 5 A g^(-1))and excellent cyclability(94.2%capacity retention after 10,000 cycles).And superior cycling stability can still be achieved under zinc-deficient condition and pouch cell configuration.This work establishes a new paradigm for designing high-performance zinc-based energy storage systems through rational separator engineering.
文摘随着手机短信成为人们日常生活交往的重要手段,垃圾短信的识别具有重要的现实意义.针对此提出一种结合TFIDF的self-attention-based Bi-LSTM的神经网络模型.该模型首先将短信文本以词向量的方式输入到Bi-LSTM层,经过特征提取并结合TFIDF和self-attention层的信息聚焦获得最后的特征向量,最后将特征向量通过Softmax分类器进行分类得到短信文本分类结果.实验结果表明,结合TFIDF的self-attention-based Bi-LSTM模型相比于传统分类模型的短信文本识别准确率提高了2.1%–4.6%,运行时间减少了0.6 s–10.2 s.
基金supported by the Fund of 863 High-Tech Research and Development Program of China and the Poten research project No. YA-2016-003
文摘Magnetically separable mesoporous activated carbon was prepared from brown coal in the presence of Fe3O4 as a bi-functional additive.Magnetic activated carbon(MAC)was characterized by lowtemperature nitrogen adsorption,scanning electron microscopy(SEM),transmission electron microscopy(TEM),X-ray diffraction(XRD),X-ray photoelectron spectroscopy(XPS)and vibrating sample magnetometry(VSM).The evolution behaviors and transition mechanism of Fe3O4 during the preparation of MAC were investigated.The results show that prepared MAC with 6 wt%Fe3O4 addition having a specific surface area and mesopore ratio of 370 m^2·g^-1 and 55.7%,which meet the requirements of adsorption application and magnetic recovery.Highly dispersed iron-containing aggregates with the size of 0.1 lm in the MAC were observed.During the preparation of MAC,Fe3O4 could enhance the escape of volatiles during the carbonization.Fe3O4 could also accelerate burning off the carbon wall during activation,which leads to enlarging micropore size,then resulting in the generation of mesopore and macropore.As a result,a part of Fe3O4 converted into FeO,FeOOH,a-Fe,c-Fe,Fe2SiO4 and compound of Aluminum-iron-silicon.The prepared activated carbon,which was magnetized by both of residual Fe3O4,reduced a-Fe and c-Fe,can be easily separated from the original solution by external magnetic field.
基金sponsored by the National Natural Science Foundation of China (Nos. 40774069 and 40974074)the State Key Program of National Natural Science of China (No. 40830424)the National 973program (No. 007209603)
文摘An approximation for the one-way wave operator takes the form of separated space and wave-number variables and makes it possible to use the FFT, which results in a great improvement in the computational efficiency. From the function approximation perspective, the OSA method shares the same separable approximation format to the one-way wave operator as other separable approximation methods but it is the only global function approximation among these methods. This leads to a difference in the phase error curve, impulse response, and migration result from other separable approximation methods. The difference is that the OSA method has higher accuracy, and the sensitivity to the velocity variation declines with increasing order.
文摘Let H be a finite dimensional Hopf algebra over a field and A an H-module algebra. The H induces an action on the CA#H(A) by adjoint and CA#H(A)H= Z(A # H) = C,where CA#H(A) denotes the centralizer which algebra A in A # H and Z(A # H) the center of A # H.The aim of this paper is to discuss ,the Galois conditions on the centralizer CA# H(A).We prove that CA# H(A)/ZA # H is H* -Galois if and only if CA# H(A)# H/CA# H(A) is H-separable). Furthermore , if H is a finite dimensional semisimple Hopf algebra and CA# H(A)# H is an Azumaya C-algebra or A # H/A is H-separable, CA# H(A) satisfies the double centralizer property in CA# H(A)# H, CA# H(A)/C is separable and there exists a cocommutative left integral t ∈∫1H,then CA# H(A)/C is H*-Galois.
