The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical m...The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.展开更多
In practical engineering construction,multi-layered barriers containing geomembranes are extensively applied to retard the migration of pollutants.However,the associated analytical theory on pollutants diffusion still...In practical engineering construction,multi-layered barriers containing geomembranes are extensively applied to retard the migration of pollutants.However,the associated analytical theory on pollutants diffusion still needs to be further improved.In this work,general analytical solutions are derived for one-dimensional diffusion of degradable organic contaminant(DOC)in the multi-layered media containing geomembranes under a time-varying concentration boundary condition,where the variable substitution and separated variable approaches are employed.These analytical solutions with clear expressions can be used not only to study the diffusion behaviors of DOC in bottom and vertical composite barrier systems,but also to verify other complex numerical models.The proposed general analytical solutions are then fully validated via three comparative analyses,including comparisons with the experimental measurements,an existing analytical solution,and a finite-difference solution.Ultimately,the influences of different factors on the composite cutoff wall’s(CCW,which consists of two soil-bentonite layers and a geomembrane)service performance are investigated through a composite vertical barrier system as the application example.The findings obtained from this investigation can provide scientific guidance for the barrier performance evaluation and the engineering design of CCWs.This application example also exhibits the necessity and effectiveness of the developed analytical solutions.展开更多
At present,the emerging solid-phase friction-based additive manufacturing technology,including friction rolling additive man-ufacturing(FRAM),can only manufacture simple single-pass components.In this study,multi-laye...At present,the emerging solid-phase friction-based additive manufacturing technology,including friction rolling additive man-ufacturing(FRAM),can only manufacture simple single-pass components.In this study,multi-layer multi-pass FRAM-deposited alumin-um alloy samples were successfully prepared using a non-shoulder tool head.The material flow behavior and microstructure of the over-lapped zone between adjacent layers and passes during multi-layer multi-pass FRAM deposition were studied using the hybrid 6061 and 5052 aluminum alloys.The results showed that a mechanical interlocking structure was formed between the adjacent layers and the adja-cent passes in the overlapped center area.Repeated friction and rolling of the tool head led to different degrees of lateral flow and plastic deformation of the materials in the overlapped zone,which made the recrystallization degree in the left and right edge zones of the over-lapped zone the highest,followed by the overlapped center zone and the non-overlapped zone.The tensile strength of the overlapped zone exceeded 90%of that of the single-pass deposition sample.It is proved that although there are uneven grooves on the surface of the over-lapping area during multi-layer and multi-pass deposition,they can be filled by the flow of materials during the deposition of the next lay-er,thus ensuring the dense microstructure and excellent mechanical properties of the overlapping area.The multi-layer multi-pass FRAM deposition overcomes the limitation of deposition width and lays the foundation for the future deposition of large-scale high-performance components.展开更多
Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems...Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems,such as the choice of sensors and fusion methods.To solve these issues,we proposed a machine learning-based fusion sensing system that uses a camera and radar,and that can be used in intelligent vehicles.First,the object detection algorithm is used to detect the image obtained by the camera;in sequence,the radar data is preprocessed,coordinate transformation is performed,and a multi-layer perceptron model for correlating the camera detection results with the radar data is proposed.The proposed fusion sensing system was verified by comparative experiments in a real-world environment.The experimental results show that the system can effectively integrate camera and radar data results,and obtain accurate and comprehensive object information in front of intelligent vehicles.展开更多
Human Action Recognition(HAR)is an active research topic in machine learning for the last few decades.Visual surveillance,robotics,and pedestrian detection are the main applications for action recognition.Computer vis...Human Action Recognition(HAR)is an active research topic in machine learning for the last few decades.Visual surveillance,robotics,and pedestrian detection are the main applications for action recognition.Computer vision researchers have introduced many HAR techniques,but they still face challenges such as redundant features and the cost of computing.In this article,we proposed a new method for the use of deep learning for HAR.In the proposed method,video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning.The Resnet-50 Pre-Trained Model is used as a deep learning model in this work.Features are extracted from two layers:Global Average Pool(GAP)and Fully Connected(FC).The features of both layers are fused by the Canonical Correlation Analysis(CCA).Then features are selected using the Shanon Entropy-based threshold function.The selected features are finally passed to multiple classifiers for final classification.Experiments are conducted on five publicly available datasets as IXMAS,UCF Sports,YouTube,UT-Interaction,and KTH.The accuracy of these data sets was 89.6%,99.7%,100%,96.7%and 96.6%,respectively.Comparison with existing techniques has shown that the proposed method provides improved accuracy for HAR.Also,the proposed method is computationally fast based on the time of execution.展开更多
The random noises of multi-sensor and the environment make observations uncertain and correlative, so the performance of fusion algorithms is reduced by using observations directly. To solve this problem, a multi-laye...The random noises of multi-sensor and the environment make observations uncertain and correlative, so the performance of fusion algorithms is reduced by using observations directly. To solve this problem, a multi-layer track fusion algorithm based on supporting degree matrix is proposed. Combined with the track fusion algorithm based on filtering step by step, it uses multi-sensor observations to establish supporting degree matrix and realize multi-layer fusion. Simulation results show its estimation precision is higher than the original algorithm and is increased by 20% around. Therefore, it solves the problem of target tracking further in the distributed track fusion system.展开更多
The huge impact kinetic energy cannot be quickly dissipated by the energy-absorbing structure and transferred to the other vehicle through the car body structure,which will cause structural damage and threaten the liv...The huge impact kinetic energy cannot be quickly dissipated by the energy-absorbing structure and transferred to the other vehicle through the car body structure,which will cause structural damage and threaten the lives of the occupants.Therefore,it is necessary to understand the laws of energy conversion,dissipation and transfer during train collisions.This study proposes a multi-layer progressive analysis method of energy flow during train collisions,considering the characteristics of the train.In this method,the train collision system is divided into conversion,dissipation,and transfer layers from the perspective of the train,collision interface,and car body structure to analyze the energy conversion,dissipation and transfer characteristics.Taking the collision process of a rail train as an example,a train collision energy transfer path analysis model was established based on power flow theory.The results show that when the maximum mean acceleration of the vehicle meets the standard requirements,the jerk may exceed the allowable limit of the human body,and there is a risk of injury to the occupants of a secondary collision.The decay rate of the collision energy along the direction of train operation reaches 79%.As the collision progresses,the collision energy gradually converges in the structure with holes,and the structure deforms when the gathered energy is greater than the maximum energy the structure can withstand.The proposed method helps to understand the train collision energy flow law and provides theoretical support for the train crashworthiness design in the future.展开更多
The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging at...The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.展开更多
This study proposes a general imperfect thermal contact model to predict the thermal contact resistance at the interface among multi-layered composite structures.Based on the Green-Lindsay(GL)thermoelastic theory,semi...This study proposes a general imperfect thermal contact model to predict the thermal contact resistance at the interface among multi-layered composite structures.Based on the Green-Lindsay(GL)thermoelastic theory,semi analytical solutions of temperature increment and displacement of multi-layered composite structures are obtained by using the Laplace transform method,upon which the effects of thermal resistance coefficient,partition coefficient,thermal conductivity ratio and heat capacity ratio on the responses are studied.The results show that the generalized imperfect thermal contact model can realistically describe the imperfect thermal contact problem.Accordingly,it may degenerate into other thermal contact models by adjusting the thermal resistance coefficient and partition coefficient.展开更多
Transportation structures such as composite pavements and railway foundations typically consist of multi-layered media designed to withstand high bearing capacity.A theoretical understanding of load transfer mechanism...Transportation structures such as composite pavements and railway foundations typically consist of multi-layered media designed to withstand high bearing capacity.A theoretical understanding of load transfer mechanisms in these multi-layer composites is essential,as it offers intuitive insights into parametric influences and facilitates enhanced structural performance.This paper employs an improved transfer matrix method to address the limitations of existing theoretical approaches for analyzing multi-layer composite structures.By establishing a twodimensional composite pavement model,it investigates load transfer characteristics and validates the accuracy through finite element simulation.The proposed method offers a straightforward analytical approach for examining internal interactions between structural layers.Case studies indicate that the concrete surface layer is the main load-bearing layer for most vertical normal and shear stresses.The soil base layer reduces the overall mechanical response of the substructure,while horizontal actions increase the risk of interfacial slip and cracking.Structural optimization analysis demonstrates that increasing the thickness of the concrete surface layer,enhancing the thickness and stiffness of the soil base layer,or incorporating gradient layers can significantly mitigate these risks of interfacial slip and cracking.The findings of this study can guide the optimization design,parameter analysis,and damage prevention of multi-layer composite structures.展开更多
Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed ...Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.展开更多
Stab-resistant textiles play a critical role in personal protection,necessitating a deeper understanding of how structural and layering factors influence their performance.The current study experimentally examines the...Stab-resistant textiles play a critical role in personal protection,necessitating a deeper understanding of how structural and layering factors influence their performance.The current study experimentally examines the effects of textile structure,layering,and ply orientation on the stab resistance of multi-layer textiles.Three 3D warp interlock(3DWI)structures({f1},{f2},{f3})and a 2D woven fabric({f4}),all made of high-performance p-aramid yarns,were engineered and manufactured.Multi-layer specimens were prepared and subjected to drop-weight stabbing tests following HOSBD standards.Stabbing performance metrics,including Depth of Trauma(DoT),Depth of Penetration(DoP),and trauma deformation(Ymax,Xmax),were investigated and analyzed.Statistical analyses(Two-and One-Way ANOVA)indicated that fabric type and layer number significantly impacted DoP(P<0.05),while ply orientation significantly affected DoP(P<0.05)but not DoT(P>0.05).Further detailed analysis revealed that 2D woven fabrics exhibited greater trauma deformation than 3D WIF structures.Increasing the number of layers reduced both DoP and DoT across all fabric structures,with f3 demonstrating the best performance in multi-layer configurations.Aligned ply orientations also enhanced stab resistance,underscoring the importance of alignment in dissipating impact energy.展开更多
Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting...Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting times and subjective interpretations,make this task difficult.The recent advancement of deep learning in healthcare has shownmuch success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics.Deep learning improves the speed and precision of skin cancer diagnosis,leading to earlier prediction and treatment.In this work,we proposed a novel deep architecture for skin cancer classification in innovative healthcare.The proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected dataset.The proposed architecture is based on two customized,innovative Convolutional neural network(CNN)models based on small depth and filter sizes.In the first model,four residual blocks are added in a squeezed fashion with a small filter size.In the second model,five residual blocks are added with smaller depth and more useful weight information of the lesion region.To make models more useful,we selected the hyperparameters through Bayesian Optimization,in which the learning rate is selected.After training the proposed models,deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance technique.The final features are passed on to the classifiers,and classification results are obtained.Also,the proposed trained model is interpreted through LIME-based localization on the HAM10000 dataset.The experimental process of the proposed architecture is performed on two dermoscopic datasets,HAM10000 and ISIC2019.We obtained an improved accuracy of 90.8%and 99.3%on these datasets,respectively.Also,the proposed architecture returned 91.6%for the cancer localization.In conclusion,the proposed architecture accuracy is compared with several pre-trained and state-of-the-art(SOTA)techniques and shows improved performance.展开更多
Silicon-based carbon composites are believed as promising anodes in the near future due to their outstanding specific capacity and relatively lower volume effect compared to pure silicon anodes.Herein,a multilayer sph...Silicon-based carbon composites are believed as promising anodes in the near future due to their outstanding specific capacity and relatively lower volume effect compared to pure silicon anodes.Herein,a multilayer spherical core-shell(M-SCS)electrode with a graphite framework prepared with Si@O-MCMB/C nanoparticles is developed,which aims to realize chemically/mechanically stability during the lithiation/delithiation process with high specific capacity.An electrochemical-/mechanical-coupling model for the M-SCS structure is established with various chemical/mechanical boundary conditions.The simulation of finite difference method(FDM)has been conducted based on the proposed coupling model,by which the diffusion-induced stress along both the radial and the circumferential directions is determined.Moreover,factors that influence the diffusion-induced stress of the M-SCS structure have been discussed and analyzed in detail.展开更多
A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5w...A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5wt%Er-1wt%TiB_(2)/Al-Mn-Mg-Sc-Zr nanocomposite were prepared using vacuum homogenization technique,and the density of samples prepared through the LPBF process reached 99.8%.The strengthening and toughening mechanisms of Er-TiB_(2)were investigated.The results show that Al_(3)Er diffraction peaks are detected by X-ray diffraction analysis,and texture strength decreases according to electron backscatter diffraction results.The added Er and TiB_(2)nano-reinforcing phases act as heterogeneous nucleation sites during the LPBF forming process,hindering grain growth and effectively refining the grains.After incorporating the Er-TiB_(2)dual-phase nano-reinforcing phases,the tensile strength and elongation at break of the LPBF-deposited samples reach 550 MPa and 18.7%,which are 13.4%and 26.4%higher than those of the matrix material,respectively.展开更多
Recently,the Cooperative Training Algorithm(CTA),a well-known Semi-Supervised Learning(SSL)technique,has garnered significant attention in the field of image classification.However,traditional CTA approaches face chal...Recently,the Cooperative Training Algorithm(CTA),a well-known Semi-Supervised Learning(SSL)technique,has garnered significant attention in the field of image classification.However,traditional CTA approaches face challenges such as high computational complexity and low classification accuracy.To overcome these limitations,we present a novel approach called Weighted fusion based Cooperative Training Algorithm(W-CTA),which leverages the cooperative training technique and unlabeled data to enhance classification performance.Moreover,we introduce the K-means Cooperative Training Algorithm(km-CTA)to prevent the occurrence of local optima during the training phase.Finally,we conduct various experiments to verify the performance of the proposed methods.Experimental results show that W-CTA and km-CTA are effective and efficient on CIFAR-10 dataset.展开更多
Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collect...Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.展开更多
BACKGROUND Lumbar interbody fusion(LIF)is the primary treatment for lumbar degenerative diseases.Elderly patients are prone to anxiety and depression after undergoing surgery,which affects their postoperative recovery...BACKGROUND Lumbar interbody fusion(LIF)is the primary treatment for lumbar degenerative diseases.Elderly patients are prone to anxiety and depression after undergoing surgery,which affects their postoperative recovery speed and quality of life.Effective prevention of anxiety and depression in elderly patients has become an urgent problem.AIM To investigate the trajectory of anxiety and depression levels in elderly patients after LIF,and the influencing factors.METHODS Random sampling was used to select 239 elderly patients who underwent LIF from January 2020 to December 2024 in Shenzhen Pingle Orthopedic Hospital.General information and surgery-related indices were recorded,and participants completed measures of psychological status,lumbar spine dysfunction,and quality of life.A latent class growth model was used to analyze the post-LIF trajectory of anxiety and depression levels,and unordered multi-categorical logistic regression was used to analyze the influencing factors.RESULTS Three trajectories of change in anxiety level were identified:Increasing anxiety(n=26,10.88%),decreasing anxiety(n=27,11.30%),and stable anxiety(n=186,77.82%).Likewise,three trajectories of change in depression level were identified:Increasing depression(n=30,12.55%),decreasing depression(n=26,10.88%),and stable depression(n=183,76.57%).Regression analysis showed that having no partner,female sex,elevated Oswestry dysfunction index(ODI)scores,and reduced 36-Item Short Form Health Survey scores all contributed to increased anxiety levels,whereas female sex,postoperative opioid use,and elevated ODI scores all contributed to increased depression levels.CONCLUSION During clinical observation,combining factors to predict anxiety and depression in post-LIF elderly patients enables timely intervention,quickens recovery,and enhances quality of life.展开更多
BACKGROUND Salvage of the infected long stem revision total knee arthroplasty is challenging due to the presence of well-fixed ingrown or cemented stems.Reconstructive options are limited.Above knee amputation(AKA)is ...BACKGROUND Salvage of the infected long stem revision total knee arthroplasty is challenging due to the presence of well-fixed ingrown or cemented stems.Reconstructive options are limited.Above knee amputation(AKA)is often recommended.We present a surgical technique that was successfully used on four such patients to convert them to a knee fusion(KF)using a cephalomedullary nail.CASE SUMMARY Four patients with infected long stem revision knee replacements that refused AKA had a single stage removal of their infected revision total knee followed by a KF.They were all treated with a statically locked antegrade cephalomedullary fusion nail,augmented with antibiotic impregnated bone cement.All patients had successful limb salvage and were ambulatory with assistive devices at the time of last follow-up.All were infection free at an average follow-up of 25.5 months(range 16-31).CONCLUSION Single stage cephalomedullary nailing can result in a successful KF in patients with infected long stem revision total knees.展开更多
基金National Natural Science Foundation of China(52175237)。
文摘The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.
基金Project(2023YFC3707800)supported by the National Key Research and Development Program of China。
文摘In practical engineering construction,multi-layered barriers containing geomembranes are extensively applied to retard the migration of pollutants.However,the associated analytical theory on pollutants diffusion still needs to be further improved.In this work,general analytical solutions are derived for one-dimensional diffusion of degradable organic contaminant(DOC)in the multi-layered media containing geomembranes under a time-varying concentration boundary condition,where the variable substitution and separated variable approaches are employed.These analytical solutions with clear expressions can be used not only to study the diffusion behaviors of DOC in bottom and vertical composite barrier systems,but also to verify other complex numerical models.The proposed general analytical solutions are then fully validated via three comparative analyses,including comparisons with the experimental measurements,an existing analytical solution,and a finite-difference solution.Ultimately,the influences of different factors on the composite cutoff wall’s(CCW,which consists of two soil-bentonite layers and a geomembrane)service performance are investigated through a composite vertical barrier system as the application example.The findings obtained from this investigation can provide scientific guidance for the barrier performance evaluation and the engineering design of CCWs.This application example also exhibits the necessity and effectiveness of the developed analytical solutions.
基金supported by the National Key Research and Development Program of China(No.2022YFB3404700)the National Natural Science Foundation of China(Nos.52105313 and 52275299)+2 种基金the Research and Development Program of Beijing Municipal Education Commission,China(No.KM202210005036)the Natural Science Foundation of Chongqing,China(No.CSTB2023NSCQ-MSX0701)the National Defense Basic Research Projects of China(No.JCKY2022405C002).
文摘At present,the emerging solid-phase friction-based additive manufacturing technology,including friction rolling additive man-ufacturing(FRAM),can only manufacture simple single-pass components.In this study,multi-layer multi-pass FRAM-deposited alumin-um alloy samples were successfully prepared using a non-shoulder tool head.The material flow behavior and microstructure of the over-lapped zone between adjacent layers and passes during multi-layer multi-pass FRAM deposition were studied using the hybrid 6061 and 5052 aluminum alloys.The results showed that a mechanical interlocking structure was formed between the adjacent layers and the adja-cent passes in the overlapped center area.Repeated friction and rolling of the tool head led to different degrees of lateral flow and plastic deformation of the materials in the overlapped zone,which made the recrystallization degree in the left and right edge zones of the over-lapped zone the highest,followed by the overlapped center zone and the non-overlapped zone.The tensile strength of the overlapped zone exceeded 90%of that of the single-pass deposition sample.It is proved that although there are uneven grooves on the surface of the over-lapping area during multi-layer and multi-pass deposition,they can be filled by the flow of materials during the deposition of the next lay-er,thus ensuring the dense microstructure and excellent mechanical properties of the overlapping area.The multi-layer multi-pass FRAM deposition overcomes the limitation of deposition width and lays the foundation for the future deposition of large-scale high-performance components.
基金the National Natural Science Foundation of China(No.U1764264/61873165)the Shanghai Automotive Industry Science and Technology Development Foundation(No.1733/1807)。
文摘Environmental perception is a key technology for autonomous driving.Owing to the limitations of a single sensor,multiple sensors are often used in practical applications.However,multi-sensor fusion faces some problems,such as the choice of sensors and fusion methods.To solve these issues,we proposed a machine learning-based fusion sensing system that uses a camera and radar,and that can be used in intelligent vehicles.First,the object detection algorithm is used to detect the image obtained by the camera;in sequence,the radar data is preprocessed,coordinate transformation is performed,and a multi-layer perceptron model for correlating the camera detection results with the radar data is proposed.The proposed fusion sensing system was verified by comparative experiments in a real-world environment.The experimental results show that the system can effectively integrate camera and radar data results,and obtain accurate and comprehensive object information in front of intelligent vehicles.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘Human Action Recognition(HAR)is an active research topic in machine learning for the last few decades.Visual surveillance,robotics,and pedestrian detection are the main applications for action recognition.Computer vision researchers have introduced many HAR techniques,but they still face challenges such as redundant features and the cost of computing.In this article,we proposed a new method for the use of deep learning for HAR.In the proposed method,video frames are initially pre-processed using a global contrast approach and later used to train a deep learning model using domain transfer learning.The Resnet-50 Pre-Trained Model is used as a deep learning model in this work.Features are extracted from two layers:Global Average Pool(GAP)and Fully Connected(FC).The features of both layers are fused by the Canonical Correlation Analysis(CCA).Then features are selected using the Shanon Entropy-based threshold function.The selected features are finally passed to multiple classifiers for final classification.Experiments are conducted on five publicly available datasets as IXMAS,UCF Sports,YouTube,UT-Interaction,and KTH.The accuracy of these data sets was 89.6%,99.7%,100%,96.7%and 96.6%,respectively.Comparison with existing techniques has shown that the proposed method provides improved accuracy for HAR.Also,the proposed method is computationally fast based on the time of execution.
基金Supported by the Aviation Science Funds (20090580013)the Fundamental Research Funds for the Central Universities (ZYGX2009J092)
文摘The random noises of multi-sensor and the environment make observations uncertain and correlative, so the performance of fusion algorithms is reduced by using observations directly. To solve this problem, a multi-layer track fusion algorithm based on supporting degree matrix is proposed. Combined with the track fusion algorithm based on filtering step by step, it uses multi-sensor observations to establish supporting degree matrix and realize multi-layer fusion. Simulation results show its estimation precision is higher than the original algorithm and is increased by 20% around. Therefore, it solves the problem of target tracking further in the distributed track fusion system.
基金Supported by the National Natural Science Foundation of China(Grant No.52172409)Postdoctoral Innovation Talents Support Program(Grant No.BX20240298)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.2682024GF023)Heilongjiang Province Postdoctoral Foundation Project(Grant No.LBH-Z23041).
文摘The huge impact kinetic energy cannot be quickly dissipated by the energy-absorbing structure and transferred to the other vehicle through the car body structure,which will cause structural damage and threaten the lives of the occupants.Therefore,it is necessary to understand the laws of energy conversion,dissipation and transfer during train collisions.This study proposes a multi-layer progressive analysis method of energy flow during train collisions,considering the characteristics of the train.In this method,the train collision system is divided into conversion,dissipation,and transfer layers from the perspective of the train,collision interface,and car body structure to analyze the energy conversion,dissipation and transfer characteristics.Taking the collision process of a rail train as an example,a train collision energy transfer path analysis model was established based on power flow theory.The results show that when the maximum mean acceleration of the vehicle meets the standard requirements,the jerk may exceed the allowable limit of the human body,and there is a risk of injury to the occupants of a secondary collision.The decay rate of the collision energy along the direction of train operation reaches 79%.As the collision progresses,the collision energy gradually converges in the structure with holes,and the structure deforms when the gathered energy is greater than the maximum energy the structure can withstand.The proposed method helps to understand the train collision energy flow law and provides theoretical support for the train crashworthiness design in the future.
基金Nourah bint Abdulrahman University for funding this project through the Researchers Supporting Project(PNURSP2025R319)Riyadh,Saudi Arabia and Prince Sultan University for covering the article processing charges(APC)associated with this publication.Special acknowledgement to Automated Systems&Soft Computing Lab(ASSCL),Prince Sultan University,Riyadh,Saudi Arabia.
文摘The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems(IDS)for enhanced network security.While conventional IDSs can be unsuitable for detecting different and emerging attacks,there is a demand for better techniques to improve detection reliability.This study introduces a new method,the Deep Adaptive Multi-Layer Attention Network(DAMLAN),to boost the result of intrusion detection on network data.Due to its multi-scale attention mechanisms and graph features,DAMLAN aims to address both known and unknown intrusions.The real-world NSL-KDD dataset,a popular choice among IDS researchers,is used to assess the proposed model.There are 67,343 normal samples and 58,630 intrusion attacks in the training set,12,833 normal samples,and 9711 intrusion attacks in the test set.Thus,the proposed DAMLAN method is more effective than the standard models due to the consideration of patterns by the attention layers.The experimental performance of the proposed model demonstrates that it achieves 99.26%training accuracy and 90.68%testing accuracy,with precision reaching 98.54%on the training set and 96.64%on the testing set.The recall and F1 scores again support the model with training set values of 99.90%and 99.21%and testing set values of 86.65%and 91.37%.These results provide a strong basis for the claims made regarding the model’s potential to identify intrusion attacks and affirm its relatively strong overall performance,irrespective of type.Future work would employ more attempts to extend the scalability and applicability of DAMLAN for real-time use in intrusion detection systems.
基金Projects(42477162,52108347,52178371,52168046,52178321,52308383)supported by the National Natural Science Foundation of ChinaProjects(2023C03143,2022C01099,2024C01219,2022C03151)supported by the Zhejiang Key Research and Development Plan,China+6 种基金Project(LQ22E080010)supported by the Exploring Youth Project of Zhejiang Natural Science Foundation,ChinaProject(LR21E080005)supported by the Outstanding Youth Project of Natural Science Foundation of Zhejiang Province,ChinaProject(2022M712964)supported by the Postdoctoral Science Foundation of ChinaProject(2023AFB008)supported by the Natural Science Foundation of Hubei Province for Youth,ChinaProject(202203)supported by Engineering Research Centre of Rock-Soil Drilling&Excavation and Protection,Ministry of Education,ChinaProject(202305-2)supported by the Science and Technology Project of Zhejiang Provincial Communication Department,ChinaProject(2021K256)supported by the Construction Research Founds of Department of Housing and Urban-Rural Development of Zhejiang Province,China。
文摘This study proposes a general imperfect thermal contact model to predict the thermal contact resistance at the interface among multi-layered composite structures.Based on the Green-Lindsay(GL)thermoelastic theory,semi analytical solutions of temperature increment and displacement of multi-layered composite structures are obtained by using the Laplace transform method,upon which the effects of thermal resistance coefficient,partition coefficient,thermal conductivity ratio and heat capacity ratio on the responses are studied.The results show that the generalized imperfect thermal contact model can realistically describe the imperfect thermal contact problem.Accordingly,it may degenerate into other thermal contact models by adjusting the thermal resistance coefficient and partition coefficient.
基金supported by Fundamental Research Funds for the Central Universities(No.lzujbky-2024-05)Innovation Foundation of Provincial Education Department of Gansu(2024B-005)+2 种基金Scientific Department of Gansu(24CXGA083,24CXGA024,JK2024-28,JK2024-32 and 23CXJA0007)Industrial Support Plan Project of Provincial Education Department of Gansu(2025CYZC-003 and CYZC-2024-10)the Hunan Natural Science Foundation Science and Education Joint Fund Project(2022JJ60109).
文摘Transportation structures such as composite pavements and railway foundations typically consist of multi-layered media designed to withstand high bearing capacity.A theoretical understanding of load transfer mechanisms in these multi-layer composites is essential,as it offers intuitive insights into parametric influences and facilitates enhanced structural performance.This paper employs an improved transfer matrix method to address the limitations of existing theoretical approaches for analyzing multi-layer composite structures.By establishing a twodimensional composite pavement model,it investigates load transfer characteristics and validates the accuracy through finite element simulation.The proposed method offers a straightforward analytical approach for examining internal interactions between structural layers.Case studies indicate that the concrete surface layer is the main load-bearing layer for most vertical normal and shear stresses.The soil base layer reduces the overall mechanical response of the substructure,while horizontal actions increase the risk of interfacial slip and cracking.Structural optimization analysis demonstrates that increasing the thickness of the concrete surface layer,enhancing the thickness and stiffness of the soil base layer,or incorporating gradient layers can significantly mitigate these risks of interfacial slip and cracking.The findings of this study can guide the optimization design,parameter analysis,and damage prevention of multi-layer composite structures.
基金supported by the National Natural Science Foundation of China(No.62401597)Natural Science Foundation of Hunan Province,China(No.2024JJ6469)the Research Project of National University of Defense Technology,China(No.ZK22-02).
文摘Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.
文摘Stab-resistant textiles play a critical role in personal protection,necessitating a deeper understanding of how structural and layering factors influence their performance.The current study experimentally examines the effects of textile structure,layering,and ply orientation on the stab resistance of multi-layer textiles.Three 3D warp interlock(3DWI)structures({f1},{f2},{f3})and a 2D woven fabric({f4}),all made of high-performance p-aramid yarns,were engineered and manufactured.Multi-layer specimens were prepared and subjected to drop-weight stabbing tests following HOSBD standards.Stabbing performance metrics,including Depth of Trauma(DoT),Depth of Penetration(DoP),and trauma deformation(Ymax,Xmax),were investigated and analyzed.Statistical analyses(Two-and One-Way ANOVA)indicated that fabric type and layer number significantly impacted DoP(P<0.05),while ply orientation significantly affected DoP(P<0.05)but not DoT(P>0.05).Further detailed analysis revealed that 2D woven fabrics exhibited greater trauma deformation than 3D WIF structures.Increasing the number of layers reduced both DoP and DoT across all fabric structures,with f3 demonstrating the best performance in multi-layer configurations.Aligned ply orientations also enhanced stab resistance,underscoring the importance of alignment in dissipating impact energy.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549)supported through Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R508)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting times and subjective interpretations,make this task difficult.The recent advancement of deep learning in healthcare has shownmuch success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics.Deep learning improves the speed and precision of skin cancer diagnosis,leading to earlier prediction and treatment.In this work,we proposed a novel deep architecture for skin cancer classification in innovative healthcare.The proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected dataset.The proposed architecture is based on two customized,innovative Convolutional neural network(CNN)models based on small depth and filter sizes.In the first model,four residual blocks are added in a squeezed fashion with a small filter size.In the second model,five residual blocks are added with smaller depth and more useful weight information of the lesion region.To make models more useful,we selected the hyperparameters through Bayesian Optimization,in which the learning rate is selected.After training the proposed models,deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance technique.The final features are passed on to the classifiers,and classification results are obtained.Also,the proposed trained model is interpreted through LIME-based localization on the HAM10000 dataset.The experimental process of the proposed architecture is performed on two dermoscopic datasets,HAM10000 and ISIC2019.We obtained an improved accuracy of 90.8%and 99.3%on these datasets,respectively.Also,the proposed architecture returned 91.6%for the cancer localization.In conclusion,the proposed architecture accuracy is compared with several pre-trained and state-of-the-art(SOTA)techniques and shows improved performance.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12072229 and 11602167).
文摘Silicon-based carbon composites are believed as promising anodes in the near future due to their outstanding specific capacity and relatively lower volume effect compared to pure silicon anodes.Herein,a multilayer spherical core-shell(M-SCS)electrode with a graphite framework prepared with Si@O-MCMB/C nanoparticles is developed,which aims to realize chemically/mechanically stability during the lithiation/delithiation process with high specific capacity.An electrochemical-/mechanical-coupling model for the M-SCS structure is established with various chemical/mechanical boundary conditions.The simulation of finite difference method(FDM)has been conducted based on the proposed coupling model,by which the diffusion-induced stress along both the radial and the circumferential directions is determined.Moreover,factors that influence the diffusion-induced stress of the M-SCS structure have been discussed and analyzed in detail.
基金Shaanxi Province Qin Chuangyuan“Scientist+Engineer”Team Construction Project(2022KXJ-071)2022 Qin Chuangyuan Achievement Transformation Incubation Capacity Improvement Project(2022JH-ZHFHTS-0012)+8 种基金Shaanxi Province Key Research and Development Plan-“Two Chains”Integration Key Project-Qin Chuangyuan General Window Industrial Cluster Project(2023QCY-LL-02)Xixian New Area Science and Technology Plan(2022-YXYJ-003,2022-XXCY-010)2024 Scientific Research Project of Shaanxi National Defense Industry Vocational and Technical College(Gfy24-07)Shaanxi Vocational and Technical Education Association 2024 Vocational Education Teaching Reform Research Topic(2024SZX354)National Natural Science Foundation of China(U24A20115)2024 Shaanxi Provincial Education Department Service Local Special Scientific Research Program Project-Industrialization Cultivation Project(24JC005,24JC063)Shaanxi Province“14th Five-Year Plan”Education Science Plan,2024 Project(SGH24Y3181)National Key Research and Development Program of China(2023YFB4606400)Longmen Laboratory Frontier Exploration Topics Project(LMQYTSKT003)。
文摘A dual-phase synergistic enhancement method was adopted to strengthen the Al-Mn-Mg-Sc-Zr alloy fabricated by laser powder bed fusion(LPBF)by leveraging the unique advantages of Er and TiB_(2).Spherical powders of 0.5wt%Er-1wt%TiB_(2)/Al-Mn-Mg-Sc-Zr nanocomposite were prepared using vacuum homogenization technique,and the density of samples prepared through the LPBF process reached 99.8%.The strengthening and toughening mechanisms of Er-TiB_(2)were investigated.The results show that Al_(3)Er diffraction peaks are detected by X-ray diffraction analysis,and texture strength decreases according to electron backscatter diffraction results.The added Er and TiB_(2)nano-reinforcing phases act as heterogeneous nucleation sites during the LPBF forming process,hindering grain growth and effectively refining the grains.After incorporating the Er-TiB_(2)dual-phase nano-reinforcing phases,the tensile strength and elongation at break of the LPBF-deposited samples reach 550 MPa and 18.7%,which are 13.4%and 26.4%higher than those of the matrix material,respectively.
基金supported in part by the National Natural Science Foundation of China(NSFC)(Nos.62033010,62102134)in part by the Leading talents of science and technology in the Central Plain of China(No.224200510004)+2 种基金in part by the Key R&D projects in Henan Province,China(No.231111222600)in part by the Aeronautical Science Foundation of China(No.2019460T5001)in part by the Scientific and Technological Innovation Talents of Colleges and Universities in Henan Province,China(No.22HASTIT014).
文摘Recently,the Cooperative Training Algorithm(CTA),a well-known Semi-Supervised Learning(SSL)technique,has garnered significant attention in the field of image classification.However,traditional CTA approaches face challenges such as high computational complexity and low classification accuracy.To overcome these limitations,we present a novel approach called Weighted fusion based Cooperative Training Algorithm(W-CTA),which leverages the cooperative training technique and unlabeled data to enhance classification performance.Moreover,we introduce the K-means Cooperative Training Algorithm(km-CTA)to prevent the occurrence of local optima during the training phase.Finally,we conduct various experiments to verify the performance of the proposed methods.Experimental results show that W-CTA and km-CTA are effective and efficient on CIFAR-10 dataset.
基金funded by the Jilin Provincial Department of Science and Technology,grant number 20230101208JC.
文摘Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.
基金Supported by the Scientific Research Projects of the Health System in Pingshan District,No.2023122.
文摘BACKGROUND Lumbar interbody fusion(LIF)is the primary treatment for lumbar degenerative diseases.Elderly patients are prone to anxiety and depression after undergoing surgery,which affects their postoperative recovery speed and quality of life.Effective prevention of anxiety and depression in elderly patients has become an urgent problem.AIM To investigate the trajectory of anxiety and depression levels in elderly patients after LIF,and the influencing factors.METHODS Random sampling was used to select 239 elderly patients who underwent LIF from January 2020 to December 2024 in Shenzhen Pingle Orthopedic Hospital.General information and surgery-related indices were recorded,and participants completed measures of psychological status,lumbar spine dysfunction,and quality of life.A latent class growth model was used to analyze the post-LIF trajectory of anxiety and depression levels,and unordered multi-categorical logistic regression was used to analyze the influencing factors.RESULTS Three trajectories of change in anxiety level were identified:Increasing anxiety(n=26,10.88%),decreasing anxiety(n=27,11.30%),and stable anxiety(n=186,77.82%).Likewise,three trajectories of change in depression level were identified:Increasing depression(n=30,12.55%),decreasing depression(n=26,10.88%),and stable depression(n=183,76.57%).Regression analysis showed that having no partner,female sex,elevated Oswestry dysfunction index(ODI)scores,and reduced 36-Item Short Form Health Survey scores all contributed to increased anxiety levels,whereas female sex,postoperative opioid use,and elevated ODI scores all contributed to increased depression levels.CONCLUSION During clinical observation,combining factors to predict anxiety and depression in post-LIF elderly patients enables timely intervention,quickens recovery,and enhances quality of life.
文摘BACKGROUND Salvage of the infected long stem revision total knee arthroplasty is challenging due to the presence of well-fixed ingrown or cemented stems.Reconstructive options are limited.Above knee amputation(AKA)is often recommended.We present a surgical technique that was successfully used on four such patients to convert them to a knee fusion(KF)using a cephalomedullary nail.CASE SUMMARY Four patients with infected long stem revision knee replacements that refused AKA had a single stage removal of their infected revision total knee followed by a KF.They were all treated with a statically locked antegrade cephalomedullary fusion nail,augmented with antibiotic impregnated bone cement.All patients had successful limb salvage and were ambulatory with assistive devices at the time of last follow-up.All were infection free at an average follow-up of 25.5 months(range 16-31).CONCLUSION Single stage cephalomedullary nailing can result in a successful KF in patients with infected long stem revision total knees.
