To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory.Firstly,the structure o...To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory.Firstly,the structure of evaluation model is constructed according to evaluation indicator system.Then evaluation samples are generated and provided to train this model.Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator.Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected.Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system.Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.展开更多
Salient object detection(SOD)models struggle to simultaneously preserve global structure,maintain sharp object boundaries,and sustain computational efficiency in complex scenes.In this study,we propose SPSALNet,a task...Salient object detection(SOD)models struggle to simultaneously preserve global structure,maintain sharp object boundaries,and sustain computational efficiency in complex scenes.In this study,we propose SPSALNet,a task-driven two-stage(macro–micro)architecture that restructures the SOD process around superpixel representations.In the proposed approach,a“split-and-enhance”principle,introduced to our knowledge for the first time in the SOD literature,hierarchically classifies superpixels and then applies targeted refinement only to ambiguous or error-prone regions.At the macro stage,the image is partitioned into content-adaptive superpixel regions,and each superpixel is represented by a high-dimensional region-level feature vector.These representations define a regional decomposition problem in which superpixels are assigned to three classes:background,object interior,and transition regions.Superpixel tokens interact with a global feature vector from a deep network backbone through a cross-attention module and are projected into an enriched embedding space that jointly encodes local topology and global context.At the micro stage,the model employs a U-Net-based refinement process that allocates computational resources only to ambiguous transition regions.The image and distance–similarity maps derived from superpixels are processed through a dual-encoder pathway.Subsequently,channel-aware fusion blocks adaptively combine information from these two sources,producing sharper and more stable object boundaries.Experimental results show that SPSALNet achieves high accuracy with lower computational cost compared to recent competing methods.On the PASCAL-S and DUT-OMRON datasets,SPSALNet exhibits a clear performance advantage across all key metrics,and it ranks first on accuracy-oriented measures on HKU-IS.On the challenging DUT-OMRON benchmark,SPSALNet reaches a MAE of 0.034.Across all datasets,it preserves object boundaries and regional structure in a stable and competitive manner.展开更多
Inspections of power transmission lines(PTLs)conducted using unmanned aerial vehicles(UAVs)are complicated by the fine structure of the lines and complex backgrounds,making accurate and efficient segmentation challeng...Inspections of power transmission lines(PTLs)conducted using unmanned aerial vehicles(UAVs)are complicated by the fine structure of the lines and complex backgrounds,making accurate and efficient segmentation challenging.This study presents the Wavelet-Guided Transformer U-Net(WGT-UNet)model,a new hybrid net-work that combines Convolutional Neural Networks(CNNs),Discrete Wavelet Transform(DWT),and Transformer architectures.The model’s primary contribution is based on spatial and channel attention mechanisms derived from wavelet subbands to guide the Transformer’s self-attention structure.Thus,low and high frequency components are separated at each stage using DWT,suppressing structural noise and making linear objects more prominent.The developed design is supported by multi-component hybrid cost functions that simultaneously solve class imbalance,edge sharpness,structural integrity,and spatial regularity issues.Furthermore,high segmentation success has been achieved in producing sharp boundaries and continuous line structures with the DWT-guided attention mechanism.Experiments conducted on the TTPLA dataset reveal that the version using the ConvNeXt backbone outperforms the current state-of-the-art approaches with an F1-Score of 79.33%and an Intersection over Union(IoU)value of 68.38%.The models and visual outputs of the developed method and all compared models can be accessed at https://github.com/burhanbarakli/WGT-UNET.展开更多
Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional ...Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.展开更多
容迟网络DTN(Delay Tolerant Network)是物联网中的一种新型的计算机网络,该网络中的源节点和目的节点之间可能并不总是存在完整的端到端的通信链路。DTN间歇连接的特点对设计有效路由算法是巨大的挑战。文章在原有Epidemic和Prophet路...容迟网络DTN(Delay Tolerant Network)是物联网中的一种新型的计算机网络,该网络中的源节点和目的节点之间可能并不总是存在完整的端到端的通信链路。DTN间歇连接的特点对设计有效路由算法是巨大的挑战。文章在原有Epidemic和Prophet路由算法的基础上,提出了一种改进的基于节点间相遇概率的路由算法RAEPBN(Routing Algorithm Based on Encounter Probability Between Nodes),并详细介绍了该算法的路由建立过程。仿真结果表明,与现有的Epidemic和Prophet路由算法相比,RAEPBN在投递率、平均时延和网络开销上的性能均最优。展开更多
Composite organohydrogels have been widely used in wearable electronics.However,it remains a great challenge to develop mechanically robust and multifunctional composite organohydrogels with good dispersion of nanofil...Composite organohydrogels have been widely used in wearable electronics.However,it remains a great challenge to develop mechanically robust and multifunctional composite organohydrogels with good dispersion of nanofillers and strong interfacial interactions.Here,multifunctional nanofiber composite reinforced organohydrogels(NCROs)are prepared.The NCRO with a sandwich-like structure possesses excellent multi-level interfacial bonding.Simultaneously,the synergistic strengthening and toughening mechanism at three different length scales endow the NCRO with outstanding mechanical properties with a tensile strength(up to 7.38±0.24 MPa),fracture strain(up to 941±17%),toughness(up to 31.59±1.53 MJ m~(-3))and fracture energy(up to 5.41±0.63 kJ m~(-2)).Moreover,the NCRO can be used for high performance electromagnetic interference shielding and strain sensing due to its high conductivity and excellent environmental tolerance such as anti-freezing performance.Remarkably,owing to the organohydrogel stabilized conductive network,the NCRO exhibits superior long-term sensing stability and durability compared to the nanofiber composite itself.This work provides new ideas for the design of high-strength,tough,stretchable,anti-freezing and conductive organohydrogels with potential applications in multifunctional and wearable electronics.展开更多
基金Supported by the Shanxi Natural Science Foundation under contract number 20041070 and Natural Science Foundation of north u-niversity of China.
文摘To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory.Firstly,the structure of evaluation model is constructed according to evaluation indicator system.Then evaluation samples are generated and provided to train this model.Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator.Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected.Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system.Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.
文摘Salient object detection(SOD)models struggle to simultaneously preserve global structure,maintain sharp object boundaries,and sustain computational efficiency in complex scenes.In this study,we propose SPSALNet,a task-driven two-stage(macro–micro)architecture that restructures the SOD process around superpixel representations.In the proposed approach,a“split-and-enhance”principle,introduced to our knowledge for the first time in the SOD literature,hierarchically classifies superpixels and then applies targeted refinement only to ambiguous or error-prone regions.At the macro stage,the image is partitioned into content-adaptive superpixel regions,and each superpixel is represented by a high-dimensional region-level feature vector.These representations define a regional decomposition problem in which superpixels are assigned to three classes:background,object interior,and transition regions.Superpixel tokens interact with a global feature vector from a deep network backbone through a cross-attention module and are projected into an enriched embedding space that jointly encodes local topology and global context.At the micro stage,the model employs a U-Net-based refinement process that allocates computational resources only to ambiguous transition regions.The image and distance–similarity maps derived from superpixels are processed through a dual-encoder pathway.Subsequently,channel-aware fusion blocks adaptively combine information from these two sources,producing sharper and more stable object boundaries.Experimental results show that SPSALNet achieves high accuracy with lower computational cost compared to recent competing methods.On the PASCAL-S and DUT-OMRON datasets,SPSALNet exhibits a clear performance advantage across all key metrics,and it ranks first on accuracy-oriented measures on HKU-IS.On the challenging DUT-OMRON benchmark,SPSALNet reaches a MAE of 0.034.Across all datasets,it preserves object boundaries and regional structure in a stable and competitive manner.
文摘Inspections of power transmission lines(PTLs)conducted using unmanned aerial vehicles(UAVs)are complicated by the fine structure of the lines and complex backgrounds,making accurate and efficient segmentation challenging.This study presents the Wavelet-Guided Transformer U-Net(WGT-UNet)model,a new hybrid net-work that combines Convolutional Neural Networks(CNNs),Discrete Wavelet Transform(DWT),and Transformer architectures.The model’s primary contribution is based on spatial and channel attention mechanisms derived from wavelet subbands to guide the Transformer’s self-attention structure.Thus,low and high frequency components are separated at each stage using DWT,suppressing structural noise and making linear objects more prominent.The developed design is supported by multi-component hybrid cost functions that simultaneously solve class imbalance,edge sharpness,structural integrity,and spatial regularity issues.Furthermore,high segmentation success has been achieved in producing sharp boundaries and continuous line structures with the DWT-guided attention mechanism.Experiments conducted on the TTPLA dataset reveal that the version using the ConvNeXt backbone outperforms the current state-of-the-art approaches with an F1-Score of 79.33%and an Intersection over Union(IoU)value of 68.38%.The models and visual outputs of the developed method and all compared models can be accessed at https://github.com/burhanbarakli/WGT-UNET.
基金supported in part by the Research on the Application of Multimodal Artificial Intelligence in Diagnosis and Treatment of Type 2 Diabetes under Grant No.2020SK50910in part by the Hunan Provincial Natural Science Foundation of China under Grant 2023JJ60020.
文摘Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.
基金Supported by the National Natural Science Foundation of China under Grant Nos.9041201190612004+2 种基金60673180(国家自然科学基金)the International Science and Technology Cooperative Program of China under Grant No.2006DFA11080(科技部国际科技合作计划项目)the Research Program of Federal Ministry of Education and Research of Germany under Grant No.01BU0680(德国教研部资助项目)
文摘容迟网络DTN(Delay Tolerant Network)是物联网中的一种新型的计算机网络,该网络中的源节点和目的节点之间可能并不总是存在完整的端到端的通信链路。DTN间歇连接的特点对设计有效路由算法是巨大的挑战。文章在原有Epidemic和Prophet路由算法的基础上,提出了一种改进的基于节点间相遇概率的路由算法RAEPBN(Routing Algorithm Based on Encounter Probability Between Nodes),并详细介绍了该算法的路由建立过程。仿真结果表明,与现有的Epidemic和Prophet路由算法相比,RAEPBN在投递率、平均时延和网络开销上的性能均最优。
基金financially supported by Natural Science Foundation of China(No.51873178)Qing Lan Project of Yangzhou University and Jiangsu Province+1 种基金High-end Talent Project of Yangzhou UniversityJiangsu Students'Innovation and Entrepreneurship Training Program(202211117059Z)。
文摘Composite organohydrogels have been widely used in wearable electronics.However,it remains a great challenge to develop mechanically robust and multifunctional composite organohydrogels with good dispersion of nanofillers and strong interfacial interactions.Here,multifunctional nanofiber composite reinforced organohydrogels(NCROs)are prepared.The NCRO with a sandwich-like structure possesses excellent multi-level interfacial bonding.Simultaneously,the synergistic strengthening and toughening mechanism at three different length scales endow the NCRO with outstanding mechanical properties with a tensile strength(up to 7.38±0.24 MPa),fracture strain(up to 941±17%),toughness(up to 31.59±1.53 MJ m~(-3))and fracture energy(up to 5.41±0.63 kJ m~(-2)).Moreover,the NCRO can be used for high performance electromagnetic interference shielding and strain sensing due to its high conductivity and excellent environmental tolerance such as anti-freezing performance.Remarkably,owing to the organohydrogel stabilized conductive network,the NCRO exhibits superior long-term sensing stability and durability compared to the nanofiber composite itself.This work provides new ideas for the design of high-strength,tough,stretchable,anti-freezing and conductive organohydrogels with potential applications in multifunctional and wearable electronics.
