Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete v...Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.展开更多
The conventional single model strategy may be ill- suited due to the multiplicity of operation phases and system uncertainty. A novel global-local discriminant analysis （GLDA） based Gaussian process regression （GPR...The conventional single model strategy may be ill- suited due to the multiplicity of operation phases and system uncertainty. A novel global-local discriminant analysis （GLDA） based Gaussian process regression （GPR） approach is developed for the quality prediction of nonlinear and multiphase batch processes. After the collected data is preprocessed through batchwise unfolding, the hidden Markov model （HMM） is applied to identify different operation phases. A GLDA algorithm is also presented to extract the appropriate process variables highly correlated with the quality variables, decreasing the complexity of modeling. Besides, the multiple local GPR models are built in the reduced- dimensional space for all the identified operation phases. Furthermore, the HMM-based state estimation is used to classify each measurement sample of a test batch into a corresponding phase with the maximal likelihood estimation. Therefore, the local GPR model with respect to specific phase is selected for online prediction. The effectiveness of the proposed prediction approach is demonstrated through the multiphase penicillin fermentation process. The comparison results show that the proposed GLDA-GPR approach is superior to the regular GPR model and the GPR based on HMM （HMM-GPR） model.展开更多
The stress distribution surrounding the fastener hole in thick laminate mechanical joints is complex. It is time-consuming to analyze the distribution using finite element method. To accurately and efficiently obtain ...The stress distribution surrounding the fastener hole in thick laminate mechanical joints is complex. It is time-consuming to analyze the distribution using finite element method. To accurately and efficiently obtain the stress state around the fastener hole in multi-bolt thick laminate joints, a global-local approach is introduced. In the method, the most seriously damaged zone is 3D modeled by taking the displacement field got from the 2D global model as boundary conditions. Through comparison and analysis there are the following findings: the global-local finite element method is a reliable and efficient way to solve the stress distribution problem; the stress distribution around the fastener hole is quite uneven in through-the-thickness direction, and the stresses of the elements close to the shearing plane are much higher than the stresses of the elements far away from the shearing plane; the out-of-plane stresses introduced by the single-lap joint cannot be ignored due to the delamination failure; the stress state is a useful criterion for further more complex studies involving failure analysis.展开更多
Efficient bolted joint design is an essential part of designing the minimum weight aerospace structures, since structural failures usually occur at connections and interface. A comprehensive numerical study of three-d...Efficient bolted joint design is an essential part of designing the minimum weight aerospace structures, since structural failures usually occur at connections and interface. A comprehensive numerical study of three-dimensional(3D) stress variations is prohibitively expensive for a large-scale structure where hundreds of bolts can be present. In this work, the hybrid composite-to-metal bolted connections used in the upper stage of European Ariane 5ME rocket are analyzed using the global-local finite element(FE) approach which involves an approximate analysis of the whole structure followed by a detailed analysis of a significantly smaller region of interest. We calculate the Tsai-Wu failure index and the margin of safety using the stresses obtained from ABAQUS. We find that the composite part of a hybrid bolted connection is prone to failure compared to the metal part. We determine the bolt preload based on the clamp-up load calculated using a maximum preload to make the composite part safe. We conclude that the unsuitable bolt preload may cause the failure of the composite part due to the high stress concentration in the vicinity of the bolt. The global-local analysis provides an efficient computational tool for enhancing 3D stress analysis in the highly loaded region.展开更多
A continuous wavelet transform(CWT)and globallocal feature(GLF)extraction-based signal classificationalgorithm is proposed to improve the signal classification accuracy.First,the CWT is utilized to generate the timefr...A continuous wavelet transform(CWT)and globallocal feature(GLF)extraction-based signal classificationalgorithm is proposed to improve the signal classification accuracy.First,the CWT is utilized to generate the timefrequency scalogram.Then,the GLF extraction method is proposed to extract features from the time-frequency scalogram.Finally,a classification method based on the support vector machine(SVM)is proposed to classify the extracted features.Experimental results show that the extended binary phase shift keying(EBPSK)bit error rate(BER)of the proposed classification algorithm is1.3x10_5under the environment of additional white Gaussian noise with the signal-to-noise ratio of-3dB,which is24times lower than that of the SVM-based signal classification method.Meanwhile,the BER using the GLF extraction method is13times lower than the one using the global feature extraction method and24times lower than the one using the local feature extraction method.展开更多
A global-local finite element modeling technique is employed in this paper to predict the separation in steel cord-rubber composite materials of radial truck tires. The local model uses a finite element analysis in co...A global-local finite element modeling technique is employed in this paper to predict the separation in steel cord-rubber composite materials of radial truck tires. The local model uses a finite element analysis in conjunction with a glob-al-local technique in ABAQUS. A 3-dimensional finite element local model calculates the maximum cyclic shear strain of an interface between steel cord and rubber materials at the carcass ply shoulder region. It is found that the maximum cyclic shear strain is reliable as a result of the analysis of carcass ply separation in radial truck tires. Using the analysis of the local model, a study of the cyclic shear strain is performed in the shoulder region and used to deter-mine the carcass ply separation. The effect of the change of carcass ply design on the separation in steel cord-rubber composite materials of radial truck tires is discussed.展开更多
This research addresses the critical challenge of enhancing satellite images captured under low-light conditions,which suffer from severely degraded quality,including a lack of detail,poor contrast,and low usability.O...This research addresses the critical challenge of enhancing satellite images captured under low-light conditions,which suffer from severely degraded quality,including a lack of detail,poor contrast,and low usability.Overcoming this limitation is essential for maximizing the value of satellite imagery in downstream computer vision tasks(e.g.,spacecraft on-orbit connection,spacecraft surface repair,space debris capture)that rely on clear visual information.Our key novelty lies in an unsupervised generative adversarial network featuring two main contributions:(1)an improved U-Net(IU-Net)generator with multi-scale feature fusion in the contracting path for richer semantic feature extraction,and(2)a Global Illumination Attention Module(GIA)at the end of the contracting path to couple local and global information,significantly improving detail recovery and illumination adjustment.The proposed algorithm operates in an unsupervised manner.It is trained and evaluated on our self-constructed,unpaired Spacecraft Dataset for Detection,Enforcement,and Parts Recognition(SDDEP),designed specifically for low-light enhancement tasks.Extensive experiments demonstrate that our method outperforms the baseline EnlightenGAN,achieving improvements of 2.7%in structural similarity(SSIM),4.7%in peak signal-to-noise ratio(PSNR),6.3%in learning perceptual image patch similarity(LPIPS),and 53.2%in DeltaE 2000.Qualitatively,the enhanced images exhibit higher overall and local brightness,improved contrast,and more natural visual effects.展开更多
The off situ accurate reconstruction of the core neutron field is an important step in realizing real-time reactor monitoring.The existing off situ reconstruction method of the neutron field is only applicable to case...The off situ accurate reconstruction of the core neutron field is an important step in realizing real-time reactor monitoring.The existing off situ reconstruction method of the neutron field is only applicable to cases wherein a single region changes at a specified location of the core.However,when the neutron field changes are complex,the accurate identification of the individual changed regions becomes challenging,which seriously affects the accuracy and stability of the neutron field recon-struction.Therefore,this study proposed a dual-task hybrid network architecture(DTHNet)for off situ reconstruction of the core neutron field,which trained the outermost assembly reconstruction task and the core reconstruction task jointly such that the former could assist the latter in the reconstruction of the core neutron field under core complex changes.Furthermore,to exploit the characteristics of the ex-core detection signals,this study designed a global-local feature upsampling module that efficiently distributed the ex-core detection signals to each reconstruction unit to improve the accuracy and stability of reconstruction.Reconstruction experiments were performed on the simulation datasets of the CLEAR-I reactor to verify the accuracy and stability of the proposed method.The results showed that when the location uncertainty of a single region did not exceed nine and the number of multiple changed regions did not exceed five.Further,the reconstructed ARD was within 2%,RD_(max)was maintained within 17.5%,and the number of RD≥10%was maintained within 10.Furthermore,when the noise interference of the ex-core detection signals was within±2%,although the average number of RD≥10%increased to 16,the average ARD was still within in 2%,and the average RD_(max)was within 22%.Collectively,these results show that,theoretically,the DTHNet can accurately and stably reconstruct most of the neutron field under certain complex core changes.展开更多
In many batch processes, there are related or independence relationships among process variables. The traditional monitoring method usually carries out a single statistical model according to the related or independen...In many batch processes, there are related or independence relationships among process variables. The traditional monitoring method usually carries out a single statistical model according to the related or independent method, and in the feature extraction there is not fully taken into account the characterization of fault information, it will make the process monitoring ineffective, so a fault monitoring method based on WGNPE(weighted global neighborhood preserving embedding)–GSVDD(greedy support vector data description) related and independent variables is proposed. First, mutual information method is used to separate the related variables and independent variables. Secondly, WGNPE method is used to extract the local and global structures of the related variables in batch process and highlight the fault information, GSVDD method is used to extract the process information of the independent variables quickly and effectively. Finally, the statistical monitoring model is established to achieve process monitoring based on WGNPE and GSVDD. The effectiveness of the proposed method was verified by the penicillin fermentation process.展开更多
In view of rite effective elastic moduli theory([1]), analyzing the thick composite laminated bars subjected to an externally applied torque are presented by three-dimensional finite element (3-D FEM) and global-local...In view of rite effective elastic moduli theory([1]), analyzing the thick composite laminated bars subjected to an externally applied torque are presented by three-dimensional finite element (3-D FEM) and global-local method in this paper. Numerical results involving the distribution of shearing stresses olt cross-section and the torsional deformation and the interlaminar stresses near to free edges are given. If necessary elements discretization may be densely carried out only in the high stress gradient, region. Obviously, it requires less computer memory and computational time so that it offers an effective way for evaluating strength of laminated bars torsion with a greet number of layers.展开更多
In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and...In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.展开更多
In this paper,a method is proposed for calculating the bearing capacity of composite reinforced wing box panels under compression after impact,which has improved calculation accuracy and accelerated analysis time.The ...In this paper,a method is proposed for calculating the bearing capacity of composite reinforced wing box panels under compression after impact,which has improved calculation accuracy and accelerated analysis time.The paper describes a method for two-phase calculation of the bearing capacity of reinforced panels,taking into account defects,based on the transfer of the stress-strain state from the global model to the local one.To implement the method,a discrete finite element mesh of the study area and a local model of the reinforced skin are created.The method of two-phase analysis of the bearing capacity of reinforced skinswith applied impact defects consists of twomain components.The first phase is a static calculation of the global shell model of the entire structure under critical loading conditions-the design case that takes place during the flight of the aircraft at small positive angles of attack,in which the aircraft realizes the maximum lift and torque for a given aircraft.In the second phase,a detailed local solid model of the studied area of the reinforced skin is prepared and a dynamic impact analysis is performed.Next,compressive force flows or displacements are transferred from the global model to the closed contour of the local zone and a solution is made in a dynamic or static formulation.This article presents the developed method of global-local modeling,which makes it possible to analyze the bearing capacity of reinforced skin after impact with a more detailed grid without sampling the global model,which speeds up and refines the calculation.展开更多
We introduce a new Python package glabcmcmc,which implements an approximate Bayesiancomputation Markovchain Monte Carlo(ABCMCMC)algorithm that combines global and local proposal strategies to address the limitations o...We introduce a new Python package glabcmcmc,which implements an approximate Bayesiancomputation Markovchain Monte Carlo(ABCMCMC)algorithm that combines global and local proposal strategies to address the limitations of standard ABC-MCMC.The proposed package includes key innovations such as the determination of global proposal frequencies,the implementation of a hybrid ABC-MCMC algorithm integrating global and local proposals,and an adaptive version that utilizes normalizing flows and gradient-based computations for enhanced proposal mechanisms.The functionality of the software package is demonstrated through illustrative examples.展开更多
Given the escalating intricacy and multifaceted nature of contemporary social systems,manually generating solutions to address pertinent social issues has become a formidable task.In response to this challenge,the rap...Given the escalating intricacy and multifaceted nature of contemporary social systems,manually generating solutions to address pertinent social issues has become a formidable task.In response to this challenge,the rapid development of artificial intelligence has spurred the exploration of computational methodologies aimed at automatically generating solutions.However,current methods for the auto-generation of solutions mainly concentrate on local social regulations that pertain to specific scenarios.Here,we report an automatic social operating system(ASOS)designed for general social solution generation built upon agent-based models that enables both global and local analyses and regulations of social problems across spatial and temporal dimensions.ASOS adopts a hypergraph with extensible social semantics for a comprehensive and structured representation of social dynamics.It also incorporates a generalized protocol for standardized hypergraph operations and a symbolic hybrid framework that delivers interpretable solutions,yielding a balance between regulatory efficacy and functional viability.To demonstrate the effectiveness of the ASOS,we apply it to the domain of averting extreme events within international oil futures markets.By generating a new trading role supplemented by new mechanisms,ASOS can adeptly discern precarious market conditions and make front-running interventions for nonprofit purposes.This study demonstrated that ASOS provides an efficient and systematic approach for generating solutions for enhancing our society.展开更多
As a vital vision task,person re-identification(Re-ID)aims to retrieve the same person under non-overlapping cameras.It is a very challenging task due to the presence of complex backgrounds,diverse illuminations and d...As a vital vision task,person re-identification(Re-ID)aims to retrieve the same person under non-overlapping cameras.It is a very challenging task due to the presence of complex backgrounds,diverse illuminations and different perspectives.In this work,we integrate the advantages of convolutional neural networks(CNNs)and transformers,and propose a novel learning framework named convolutional multi-level transformer(CMT)for image-based person Re-ID.More specifically,wefirst propose a scale-aware feature enhancement(SFE)module to extract multi-scale local features from a pre-trained CNN backbone.Then,we introduce a part-aware transformer encoder(PTE)to further mine discriminative local information guided by global semantics.Finally,a deeply-supervised learning(DSL)technique is adopted to optimize the proposed CMT and improve its training efficiency.Extensive experiments on four large-scale Re-ID benchmarks demonstrate that our method performs favorably against several state-of-the-art methods.展开更多
Aiming at the problem that honeycomb lung lesions are difficult to accurately segment due to diverse morphology and complex distribution,a network with parallel two-branch structure is proposed.In the encoder,the Pyra...Aiming at the problem that honeycomb lung lesions are difficult to accurately segment due to diverse morphology and complex distribution,a network with parallel two-branch structure is proposed.In the encoder,the Pyramid Pooling Transformer(P2T)backbone is used as the Transformer branch to obtain the global features of the lesions,the convolutional branch is used to extract the lesions’local feature information,and the feature fusion module is designed to effectively fuse the features in the dual branches;subsequently,in the decoder,the channel prior convolutional attention is used to enhance the localization ability of the model to the lesion region.To resolve the problem of model accuracy degradation caused by the class imbalance of the dataset,an adaptive weighted hybrid loss function is designed for model training.Finally,extensive experimental results show that the method in this paper performs well on the Honeycomb Lung Dataset,with Intersection over Union(IoU),mean Intersection over Union(mIoU),Dice coefficient,and Precision(Pre)of 0.8750,0.9363,0.9298,and 0.9012,respectively,which are better than other methods.In addition,its IoU and Dice coefficient of 0.7941 and 0.8875 on the Covid dataset further prove its excellent performance.展开更多
基金the National Natural Science Foundation of China(No.62266025)。
文摘Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.
基金The Fundamental Research Funds for the Central Universities(No.JUDCF12027,JUSRP51323B)the Scientific Innovation Research of College Graduates in Jiangsu Province(No.CXLX12_0734)
文摘The conventional single model strategy may be ill- suited due to the multiplicity of operation phases and system uncertainty. A novel global-local discriminant analysis （GLDA） based Gaussian process regression （GPR） approach is developed for the quality prediction of nonlinear and multiphase batch processes. After the collected data is preprocessed through batchwise unfolding, the hidden Markov model （HMM） is applied to identify different operation phases. A GLDA algorithm is also presented to extract the appropriate process variables highly correlated with the quality variables, decreasing the complexity of modeling. Besides, the multiple local GPR models are built in the reduced- dimensional space for all the identified operation phases. Furthermore, the HMM-based state estimation is used to classify each measurement sample of a test batch into a corresponding phase with the maximal likelihood estimation. Therefore, the local GPR model with respect to specific phase is selected for online prediction. The effectiveness of the proposed prediction approach is demonstrated through the multiphase penicillin fermentation process. The comparison results show that the proposed GLDA-GPR approach is superior to the regular GPR model and the GPR based on HMM （HMM-GPR） model.
文摘The stress distribution surrounding the fastener hole in thick laminate mechanical joints is complex. It is time-consuming to analyze the distribution using finite element method. To accurately and efficiently obtain the stress state around the fastener hole in multi-bolt thick laminate joints, a global-local approach is introduced. In the method, the most seriously damaged zone is 3D modeled by taking the displacement field got from the 2D global model as boundary conditions. Through comparison and analysis there are the following findings: the global-local finite element method is a reliable and efficient way to solve the stress distribution problem; the stress distribution around the fastener hole is quite uneven in through-the-thickness direction, and the stresses of the elements close to the shearing plane are much higher than the stresses of the elements far away from the shearing plane; the out-of-plane stresses introduced by the single-lap joint cannot be ignored due to the delamination failure; the stress state is a useful criterion for further more complex studies involving failure analysis.
基金Project(282522)supported by the European Union's Research and Innovation Funding Programme
文摘Efficient bolted joint design is an essential part of designing the minimum weight aerospace structures, since structural failures usually occur at connections and interface. A comprehensive numerical study of three-dimensional(3D) stress variations is prohibitively expensive for a large-scale structure where hundreds of bolts can be present. In this work, the hybrid composite-to-metal bolted connections used in the upper stage of European Ariane 5ME rocket are analyzed using the global-local finite element(FE) approach which involves an approximate analysis of the whole structure followed by a detailed analysis of a significantly smaller region of interest. We calculate the Tsai-Wu failure index and the margin of safety using the stresses obtained from ABAQUS. We find that the composite part of a hybrid bolted connection is prone to failure compared to the metal part. We determine the bolt preload based on the clamp-up load calculated using a maximum preload to make the composite part safe. We conclude that the unsuitable bolt preload may cause the failure of the composite part due to the high stress concentration in the vicinity of the bolt. The global-local analysis provides an efficient computational tool for enhancing 3D stress analysis in the highly loaded region.
基金The National Key Technology R&D Program(No.2012BAH15B00)the Scientific Innovation Research of College Graduates in Jiangsu Province(No.KYLX150076)
文摘A continuous wavelet transform(CWT)and globallocal feature(GLF)extraction-based signal classificationalgorithm is proposed to improve the signal classification accuracy.First,the CWT is utilized to generate the timefrequency scalogram.Then,the GLF extraction method is proposed to extract features from the time-frequency scalogram.Finally,a classification method based on the support vector machine(SVM)is proposed to classify the extracted features.Experimental results show that the extended binary phase shift keying(EBPSK)bit error rate(BER)of the proposed classification algorithm is1.3x10_5under the environment of additional white Gaussian noise with the signal-to-noise ratio of-3dB,which is24times lower than that of the SVM-based signal classification method.Meanwhile,the BER using the GLF extraction method is13times lower than the one using the global feature extraction method and24times lower than the one using the local feature extraction method.
文摘A global-local finite element modeling technique is employed in this paper to predict the separation in steel cord-rubber composite materials of radial truck tires. The local model uses a finite element analysis in conjunction with a glob-al-local technique in ABAQUS. A 3-dimensional finite element local model calculates the maximum cyclic shear strain of an interface between steel cord and rubber materials at the carcass ply shoulder region. It is found that the maximum cyclic shear strain is reliable as a result of the analysis of carcass ply separation in radial truck tires. Using the analysis of the local model, a study of the cyclic shear strain is performed in the shoulder region and used to deter-mine the carcass ply separation. The effect of the change of carcass ply design on the separation in steel cord-rubber composite materials of radial truck tires is discussed.
基金supported by Anhui Province University Key Science and Technology Project(2024AH053415)Anhui Province University Major Science and Technology Project(2024AH040229).
文摘This research addresses the critical challenge of enhancing satellite images captured under low-light conditions,which suffer from severely degraded quality,including a lack of detail,poor contrast,and low usability.Overcoming this limitation is essential for maximizing the value of satellite imagery in downstream computer vision tasks(e.g.,spacecraft on-orbit connection,spacecraft surface repair,space debris capture)that rely on clear visual information.Our key novelty lies in an unsupervised generative adversarial network featuring two main contributions:(1)an improved U-Net(IU-Net)generator with multi-scale feature fusion in the contracting path for richer semantic feature extraction,and(2)a Global Illumination Attention Module(GIA)at the end of the contracting path to couple local and global information,significantly improving detail recovery and illumination adjustment.The proposed algorithm operates in an unsupervised manner.It is trained and evaluated on our self-constructed,unpaired Spacecraft Dataset for Detection,Enforcement,and Parts Recognition(SDDEP),designed specifically for low-light enhancement tasks.Extensive experiments demonstrate that our method outperforms the baseline EnlightenGAN,achieving improvements of 2.7%in structural similarity(SSIM),4.7%in peak signal-to-noise ratio(PSNR),6.3%in learning perceptual image patch similarity(LPIPS),and 53.2%in DeltaE 2000.Qualitatively,the enhanced images exhibit higher overall and local brightness,improved contrast,and more natural visual effects.
基金supported by the National Natural Science Foundation of China(No.12305344)the 2023 Anhui university research project of China(No.2023AH052179).
文摘The off situ accurate reconstruction of the core neutron field is an important step in realizing real-time reactor monitoring.The existing off situ reconstruction method of the neutron field is only applicable to cases wherein a single region changes at a specified location of the core.However,when the neutron field changes are complex,the accurate identification of the individual changed regions becomes challenging,which seriously affects the accuracy and stability of the neutron field recon-struction.Therefore,this study proposed a dual-task hybrid network architecture(DTHNet)for off situ reconstruction of the core neutron field,which trained the outermost assembly reconstruction task and the core reconstruction task jointly such that the former could assist the latter in the reconstruction of the core neutron field under core complex changes.Furthermore,to exploit the characteristics of the ex-core detection signals,this study designed a global-local feature upsampling module that efficiently distributed the ex-core detection signals to each reconstruction unit to improve the accuracy and stability of reconstruction.Reconstruction experiments were performed on the simulation datasets of the CLEAR-I reactor to verify the accuracy and stability of the proposed method.The results showed that when the location uncertainty of a single region did not exceed nine and the number of multiple changed regions did not exceed five.Further,the reconstructed ARD was within 2%,RD_(max)was maintained within 17.5%,and the number of RD≥10%was maintained within 10.Furthermore,when the noise interference of the ex-core detection signals was within±2%,although the average number of RD≥10%increased to 16,the average ARD was still within in 2%,and the average RD_(max)was within 22%.Collectively,these results show that,theoretically,the DTHNet can accurately and stably reconstruct most of the neutron field under certain complex core changes.
基金Supported by the National Natural Science Foundation of China(No.61763029)the Natural Science Foundation of Gansu Province(1610RJZA016)
文摘In many batch processes, there are related or independence relationships among process variables. The traditional monitoring method usually carries out a single statistical model according to the related or independent method, and in the feature extraction there is not fully taken into account the characterization of fault information, it will make the process monitoring ineffective, so a fault monitoring method based on WGNPE(weighted global neighborhood preserving embedding)–GSVDD(greedy support vector data description) related and independent variables is proposed. First, mutual information method is used to separate the related variables and independent variables. Secondly, WGNPE method is used to extract the local and global structures of the related variables in batch process and highlight the fault information, GSVDD method is used to extract the process information of the independent variables quickly and effectively. Finally, the statistical monitoring model is established to achieve process monitoring based on WGNPE and GSVDD. The effectiveness of the proposed method was verified by the penicillin fermentation process.
文摘In view of rite effective elastic moduli theory([1]), analyzing the thick composite laminated bars subjected to an externally applied torque are presented by three-dimensional finite element (3-D FEM) and global-local method in this paper. Numerical results involving the distribution of shearing stresses olt cross-section and the torsional deformation and the interlaminar stresses near to free edges are given. If necessary elements discretization may be densely carried out only in the high stress gradient, region. Obviously, it requires less computer memory and computational time so that it offers an effective way for evaluating strength of laminated bars torsion with a greet number of layers.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.U20A20197,62306187the Foundation of Ministry of Industry and Information Technology TC220H05X-04.
文摘In recent years,semantic segmentation on 3D point cloud data has attracted much attention.Unlike 2D images where pixels distribute regularly in the image domain,3D point clouds in non-Euclidean space are irregular and inherently sparse.Therefore,it is very difficult to extract long-range contexts and effectively aggregate local features for semantic segmentation in 3D point cloud space.Most current methods either focus on local feature aggregation or long-range context dependency,but fail to directly establish a global-local feature extractor to complete the point cloud semantic segmentation tasks.In this paper,we propose a Transformer-based stratified graph convolutional network(SGT-Net),which enlarges the effective receptive field and builds direct long-range dependency.Specifically,we first propose a novel dense-sparse sampling strategy that provides dense local vertices and sparse long-distance vertices for subsequent graph convolutional network(GCN).Secondly,we propose a multi-key self-attention mechanism based on the Transformer to further weight augmentation for crucial neighboring relationships and enlarge the effective receptive field.In addition,to further improve the efficiency of the network,we propose a similarity measurement module to determine whether the neighborhood near the center point is effective.We demonstrate the validity and superiority of our method on the S3DIS and ShapeNet datasets.Through ablation experiments and segmentation visualization,we verify that the SGT model can improve the performance of the point cloud semantic segmentation.
文摘In this paper,a method is proposed for calculating the bearing capacity of composite reinforced wing box panels under compression after impact,which has improved calculation accuracy and accelerated analysis time.The paper describes a method for two-phase calculation of the bearing capacity of reinforced panels,taking into account defects,based on the transfer of the stress-strain state from the global model to the local one.To implement the method,a discrete finite element mesh of the study area and a local model of the reinforced skin are created.The method of two-phase analysis of the bearing capacity of reinforced skinswith applied impact defects consists of twomain components.The first phase is a static calculation of the global shell model of the entire structure under critical loading conditions-the design case that takes place during the flight of the aircraft at small positive angles of attack,in which the aircraft realizes the maximum lift and torque for a given aircraft.In the second phase,a detailed local solid model of the studied area of the reinforced skin is prepared and a dynamic impact analysis is performed.Next,compressive force flows or displacements are transferred from the global model to the closed contour of the local zone and a solution is made in a dynamic or static formulation.This article presents the developed method of global-local modeling,which makes it possible to analyze the bearing capacity of reinforced skin after impact with a more detailed grid without sampling the global model,which speeds up and refines the calculation.
基金supported by the National Natural Science Foundation of China[grant numbers 12131001 and 12101333]the startup fund of ShanghaiTech University,the Fundamental Research Funds for the Central Universities,LPMC,and KLMDASR.
文摘We introduce a new Python package glabcmcmc,which implements an approximate Bayesiancomputation Markovchain Monte Carlo(ABCMCMC)algorithm that combines global and local proposal strategies to address the limitations of standard ABC-MCMC.The proposed package includes key innovations such as the determination of global proposal frequencies,the implementation of a hybrid ABC-MCMC algorithm integrating global and local proposals,and an adaptive version that utilizes normalizing flows and gradient-based computations for enhanced proposal mechanisms.The functionality of the software package is demonstrated through illustrative examples.
基金supported by the National Key Research and Development Program of China(No.2021ZD0200300)the National Nature Science Foundation of China(Nos.61836004 and 62088102)the IDG/McGovern Institute for Brain Research at Tsinghua University,China.
文摘Given the escalating intricacy and multifaceted nature of contemporary social systems,manually generating solutions to address pertinent social issues has become a formidable task.In response to this challenge,the rapid development of artificial intelligence has spurred the exploration of computational methodologies aimed at automatically generating solutions.However,current methods for the auto-generation of solutions mainly concentrate on local social regulations that pertain to specific scenarios.Here,we report an automatic social operating system(ASOS)designed for general social solution generation built upon agent-based models that enables both global and local analyses and regulations of social problems across spatial and temporal dimensions.ASOS adopts a hypergraph with extensible social semantics for a comprehensive and structured representation of social dynamics.It also incorporates a generalized protocol for standardized hypergraph operations and a symbolic hybrid framework that delivers interpretable solutions,yielding a balance between regulatory efficacy and functional viability.To demonstrate the effectiveness of the ASOS,we apply it to the domain of averting extreme events within international oil futures markets.By generating a new trading role supplemented by new mechanisms,ASOS can adeptly discern precarious market conditions and make front-running interventions for nonprofit purposes.This study demonstrated that ASOS provides an efficient and systematic approach for generating solutions for enhancing our society.
文摘As a vital vision task,person re-identification(Re-ID)aims to retrieve the same person under non-overlapping cameras.It is a very challenging task due to the presence of complex backgrounds,diverse illuminations and different perspectives.In this work,we integrate the advantages of convolutional neural networks(CNNs)and transformers,and propose a novel learning framework named convolutional multi-level transformer(CMT)for image-based person Re-ID.More specifically,wefirst propose a scale-aware feature enhancement(SFE)module to extract multi-scale local features from a pre-trained CNN backbone.Then,we introduce a part-aware transformer encoder(PTE)to further mine discriminative local information guided by global semantics.Finally,a deeply-supervised learning(DSL)technique is adopted to optimize the proposed CMT and improve its training efficiency.Extensive experiments on four large-scale Re-ID benchmarks demonstrate that our method performs favorably against several state-of-the-art methods.
基金supported by the Central Leading Local Science and Technology Development Fund(Nos.YDZJSX2021C004 and YDZJSX20231C004)the Natural Science Foundation of Shanxi Province(No.20210302124554).
文摘Aiming at the problem that honeycomb lung lesions are difficult to accurately segment due to diverse morphology and complex distribution,a network with parallel two-branch structure is proposed.In the encoder,the Pyramid Pooling Transformer(P2T)backbone is used as the Transformer branch to obtain the global features of the lesions,the convolutional branch is used to extract the lesions’local feature information,and the feature fusion module is designed to effectively fuse the features in the dual branches;subsequently,in the decoder,the channel prior convolutional attention is used to enhance the localization ability of the model to the lesion region.To resolve the problem of model accuracy degradation caused by the class imbalance of the dataset,an adaptive weighted hybrid loss function is designed for model training.Finally,extensive experimental results show that the method in this paper performs well on the Honeycomb Lung Dataset,with Intersection over Union(IoU),mean Intersection over Union(mIoU),Dice coefficient,and Precision(Pre)of 0.8750,0.9363,0.9298,and 0.9012,respectively,which are better than other methods.In addition,its IoU and Dice coefficient of 0.7941 and 0.8875 on the Covid dataset further prove its excellent performance.
