Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different classes.This task is prevalent in practical scenarios such as indust...Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different classes.This task is prevalent in practical scenarios such as industrial fault diagnosis,network intrusion detection,cancer detection,etc.In imbalanced classification tasks,the focus is typically on achieving high recognition accuracy for the minority class.However,due to the challenges presented by imbalanced multi-class datasets,such as the scarcity of samples in minority classes and complex inter-class relationships with overlapping boundaries,existing methods often do not perform well in multi-class imbalanced data classification tasks,particularly in terms of recognizing minority classes with high accuracy.Therefore,this paper proposes a multi-class imbalanced data classification method called CSDSResNet,which is based on a cost-sensitive dualstream residual network.Firstly,to address the issue of limited samples in the minority class within imbalanced datasets,a dual-stream residual network backbone structure is designed to enhance the model’s feature extraction capability.Next,considering the complexities arising fromimbalanced inter-class sample quantities and imbalanced inter-class overlapping boundaries in multi-class imbalanced datasets,a unique cost-sensitive loss function is devised.This loss function places more emphasis on the minority class and the challenging classes with high interclass similarity,thereby improving the model’s classification ability.Finally,the effectiveness and generalization of the proposed method,CSDSResNet,are evaluated on two datasets:‘DryBeans’and‘Electric Motor Defects’.The experimental results demonstrate that CSDSResNet achieves the best performance on imbalanced datasets,with macro_F1-score values improving by 2.9%and 1.9%on the two datasets compared to current state-of-the-art classification methods,respectively.Furthermore,it achieves the highest precision in single-class recognition tasks for the minority class.展开更多
Single Image Super-Resolution(SISR)technology aims to reconstruct a clear,high-resolution image with more information from an input low-resolution image that is blurry and contains less information.This technology has...Single Image Super-Resolution(SISR)technology aims to reconstruct a clear,high-resolution image with more information from an input low-resolution image that is blurry and contains less information.This technology has significant research value and is widely used in fields such as medical imaging,satellite image processing,and security surveillance.Despite significant progress in existing research,challenges remain in reconstructing clear and complex texture details,with issues such as edge blurring and artifacts still present.The visual perception effect still needs further enhancement.Therefore,this study proposes a Pyramid Separable Channel Attention Network(PSCAN)for the SISR task.Thismethod designs a convolutional backbone network composed of Pyramid Separable Channel Attention blocks to effectively extract and fuse multi-scale features.This expands the model’s receptive field,reduces resolution loss,and enhances the model’s ability to reconstruct texture details.Additionally,an innovative artifact loss function is designed to better distinguish between artifacts and real edge details,reducing artifacts in the reconstructed images.We conducted comprehensive ablation and comparative experiments on the Arabidopsis root image dataset and several public datasets.The experimental results show that the proposed PSCAN method achieves the best-known performance in both subjective visual effects and objective evaluation metrics,with improvements of 0.84 in Peak Signal-to-Noise Ratio(PSNR)and 0.017 in Structural Similarity Index(SSIM).This demonstrates that the method can effectively preserve high-frequency texture details,reduce artifacts,and have good generalization performance.展开更多
Wheat flour,as the most important source of food globally,is one of the most common causative agents of food allergy.This study aimed to investigate the effects of fermentation on wheat protein digestibility and aller...Wheat flour,as the most important source of food globally,is one of the most common causative agents of food allergy.This study aimed to investigate the effects of fermentation on wheat protein digestibility and allergenicity.Protein digestibility were evaluated using the sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis and enzyme-linked immunosorbent assay.The effect of protein on intestinal permeability was investigated by Caco-2 cell monolayers.Co-culture fermentation with Pediococcus acidilactici XZ31 and yeast leads to improvement in digestibility of wheat protein compared to single strain fermentation.Fermentation leads to a decrease in albumin/globulin antigenicity and an increase in gluten R5 reactivity,with the most significant changes in the co-culture group.Digestion strengthen the decrease of protein antigenicity and counteracts the difference in antigenicity induced by fermentation between groups.However,pretreatment with P.acidilactici XZ31 reduces the amount of allergens across Caco-2 monolayer and attenuates the gluten-induced increase in permeability of Caco-2 cell monolayer by reducing actin polymerization and villous atrophy.Co-culture fermentation reduces gluten-induced cell monolayer damage to a greater extent than P.acidilactici XZ31 monoculture.These results gives valuable insight into the effects of P.acidilactici XZ31 fermentation on the allergenicity and toxicity of wheat proteins,which contribute to promoting the application of multi-strain leavening agent in hypoallergenic and gluten-free wheat products.展开更多
针对公租房市场中租赁客户身份复杂、变动频繁,难以安全有效管理、阻止租户转租难题,设计了一个基于NB-IoT(narrow band Internet of Things)的安全智能锁系统。它利用位置证明和时间戳加密机制,实现了对房屋安全门锁权限统一管理,并可...针对公租房市场中租赁客户身份复杂、变动频繁,难以安全有效管理、阻止租户转租难题,设计了一个基于NB-IoT(narrow band Internet of Things)的安全智能锁系统。它利用位置证明和时间戳加密机制,实现了对房屋安全门锁权限统一管理,并可防止远程开锁、重放攻击及中间人攻击。理论分析和测试结果表明,所提方案能够在安全高效管理公租房屋、阻止用户转租的同时具备较低的计算和通信开销。展开更多
在窄带物联网(Narrow Band Internet of Things,NB-IoT)通信系统中,设备间的数据通信以无连接的UDP(User Datagram Protocol)报文方式传输。在不可靠的UDP传输机制下,密钥的可靠更新成了安全门锁机制研究中的难点。文中设计了一个无连...在窄带物联网(Narrow Band Internet of Things,NB-IoT)通信系统中,设备间的数据通信以无连接的UDP(User Datagram Protocol)报文方式传输。在不可靠的UDP传输机制下,密钥的可靠更新成了安全门锁机制研究中的难点。文中设计了一个无连接通信链路上的密钥可靠更新方案,该方案利用智能门锁密钥更新的特点,通过精心设计的密钥传输交互机制,使门锁设备通过UDP协议获取密钥并且可靠地完成密钥更新。理论分析和原型实验的结果表明,该方案能够可靠地更新密钥,并具有较小的通信开销和计算开销。展开更多
This paper presents a microscopic traffic simulation-based method for urban traffic state estimation using Assisted Global Positioning System (A-GPS) mobile phones. In this approach, real-time location data are collec...This paper presents a microscopic traffic simulation-based method for urban traffic state estimation using Assisted Global Positioning System (A-GPS) mobile phones. In this approach, real-time location data are collected by A-GPS mobile phones to track vehicles traveling on urban roads. In addition, tracking data obtained from individual mobile probes are aggregated to provide estimations of average road link speeds along rolling time periods. Moreover, the estimated average speeds are classified to different traffic condition levels, which are prepared for displaying a real-time traffic map on mobile phones. Simulation results demonstrate the effectiveness of the proposed method, which are fundamental for the subsequent development of a system demonstrator.展开更多
基金supported by Beijing Municipal Science and Technology Project(No.Z221100007122003)。
文摘Imbalanced data classification is the task of classifying datasets where there is a significant disparity in the number of samples between different classes.This task is prevalent in practical scenarios such as industrial fault diagnosis,network intrusion detection,cancer detection,etc.In imbalanced classification tasks,the focus is typically on achieving high recognition accuracy for the minority class.However,due to the challenges presented by imbalanced multi-class datasets,such as the scarcity of samples in minority classes and complex inter-class relationships with overlapping boundaries,existing methods often do not perform well in multi-class imbalanced data classification tasks,particularly in terms of recognizing minority classes with high accuracy.Therefore,this paper proposes a multi-class imbalanced data classification method called CSDSResNet,which is based on a cost-sensitive dualstream residual network.Firstly,to address the issue of limited samples in the minority class within imbalanced datasets,a dual-stream residual network backbone structure is designed to enhance the model’s feature extraction capability.Next,considering the complexities arising fromimbalanced inter-class sample quantities and imbalanced inter-class overlapping boundaries in multi-class imbalanced datasets,a unique cost-sensitive loss function is devised.This loss function places more emphasis on the minority class and the challenging classes with high interclass similarity,thereby improving the model’s classification ability.Finally,the effectiveness and generalization of the proposed method,CSDSResNet,are evaluated on two datasets:‘DryBeans’and‘Electric Motor Defects’.The experimental results demonstrate that CSDSResNet achieves the best performance on imbalanced datasets,with macro_F1-score values improving by 2.9%and 1.9%on the two datasets compared to current state-of-the-art classification methods,respectively.Furthermore,it achieves the highest precision in single-class recognition tasks for the minority class.
基金supported by Beijing Municipal Science and Technology Project(No.Z221100007122003).
文摘Single Image Super-Resolution(SISR)technology aims to reconstruct a clear,high-resolution image with more information from an input low-resolution image that is blurry and contains less information.This technology has significant research value and is widely used in fields such as medical imaging,satellite image processing,and security surveillance.Despite significant progress in existing research,challenges remain in reconstructing clear and complex texture details,with issues such as edge blurring and artifacts still present.The visual perception effect still needs further enhancement.Therefore,this study proposes a Pyramid Separable Channel Attention Network(PSCAN)for the SISR task.Thismethod designs a convolutional backbone network composed of Pyramid Separable Channel Attention blocks to effectively extract and fuse multi-scale features.This expands the model’s receptive field,reduces resolution loss,and enhances the model’s ability to reconstruct texture details.Additionally,an innovative artifact loss function is designed to better distinguish between artifacts and real edge details,reducing artifacts in the reconstructed images.We conducted comprehensive ablation and comparative experiments on the Arabidopsis root image dataset and several public datasets.The experimental results show that the proposed PSCAN method achieves the best-known performance in both subjective visual effects and objective evaluation metrics,with improvements of 0.84 in Peak Signal-to-Noise Ratio(PSNR)and 0.017 in Structural Similarity Index(SSIM).This demonstrates that the method can effectively preserve high-frequency texture details,reduce artifacts,and have good generalization performance.
基金supported by the National Key Research and Development Program of China(2019YFC1605000)National Natural Science Foundation of China(31872904)。
文摘Wheat flour,as the most important source of food globally,is one of the most common causative agents of food allergy.This study aimed to investigate the effects of fermentation on wheat protein digestibility and allergenicity.Protein digestibility were evaluated using the sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis and enzyme-linked immunosorbent assay.The effect of protein on intestinal permeability was investigated by Caco-2 cell monolayers.Co-culture fermentation with Pediococcus acidilactici XZ31 and yeast leads to improvement in digestibility of wheat protein compared to single strain fermentation.Fermentation leads to a decrease in albumin/globulin antigenicity and an increase in gluten R5 reactivity,with the most significant changes in the co-culture group.Digestion strengthen the decrease of protein antigenicity and counteracts the difference in antigenicity induced by fermentation between groups.However,pretreatment with P.acidilactici XZ31 reduces the amount of allergens across Caco-2 monolayer and attenuates the gluten-induced increase in permeability of Caco-2 cell monolayer by reducing actin polymerization and villous atrophy.Co-culture fermentation reduces gluten-induced cell monolayer damage to a greater extent than P.acidilactici XZ31 monoculture.These results gives valuable insight into the effects of P.acidilactici XZ31 fermentation on the allergenicity and toxicity of wheat proteins,which contribute to promoting the application of multi-strain leavening agent in hypoallergenic and gluten-free wheat products.
文摘针对公租房市场中租赁客户身份复杂、变动频繁,难以安全有效管理、阻止租户转租难题,设计了一个基于NB-IoT(narrow band Internet of Things)的安全智能锁系统。它利用位置证明和时间戳加密机制,实现了对房屋安全门锁权限统一管理,并可防止远程开锁、重放攻击及中间人攻击。理论分析和测试结果表明,所提方案能够在安全高效管理公租房屋、阻止用户转租的同时具备较低的计算和通信开销。
文摘在窄带物联网(Narrow Band Internet of Things,NB-IoT)通信系统中,设备间的数据通信以无连接的UDP(User Datagram Protocol)报文方式传输。在不可靠的UDP传输机制下,密钥的可靠更新成了安全门锁机制研究中的难点。文中设计了一个无连接通信链路上的密钥可靠更新方案,该方案利用智能门锁密钥更新的特点,通过精心设计的密钥传输交互机制,使门锁设备通过UDP协议获取密钥并且可靠地完成密钥更新。理论分析和原型实验的结果表明,该方案能够可靠地更新密钥,并具有较小的通信开销和计算开销。
文摘This paper presents a microscopic traffic simulation-based method for urban traffic state estimation using Assisted Global Positioning System (A-GPS) mobile phones. In this approach, real-time location data are collected by A-GPS mobile phones to track vehicles traveling on urban roads. In addition, tracking data obtained from individual mobile probes are aggregated to provide estimations of average road link speeds along rolling time periods. Moreover, the estimated average speeds are classified to different traffic condition levels, which are prepared for displaying a real-time traffic map on mobile phones. Simulation results demonstrate the effectiveness of the proposed method, which are fundamental for the subsequent development of a system demonstrator.
