In medical image segmentation task,convolutional neural networks(CNNs)are difficult to capture long-range dependencies,but transformers can model the long-range dependencies effectively.However,transformers have a fle...In medical image segmentation task,convolutional neural networks(CNNs)are difficult to capture long-range dependencies,but transformers can model the long-range dependencies effectively.However,transformers have a flexible structure and seldom assume the structural bias of input data,so it is difficult for transformers to learn positional encoding of the medical images when using fewer images for training.To solve these problems,a dual branch structure is proposed.In one branch,Mix-Feed-Forward Network(Mix-FFN)and axial attention are adopted to capture long-range dependencies and keep the translation invariance of the model.Mix-FFN whose depth-wise convolutions can provide position information is better than ordinary positional encoding.In the other branch,traditional convolutional neural networks(CNNs)are used to extract different features of fewer medical images.In addition,the attention fusion module BiFusion is used to effectively integrate the information from the CNN branch and Transformer branch,and the fused features can effectively capture the global and local context of the current spatial resolution.On the public standard datasets Gland Segmentation(GlaS),Colorectal adenocarcinoma gland(CRAG)and COVID-19 CT Images Segmentation,the F1-score,Intersection over Union(IoU)and parameters of the proposed TC-Fuse are superior to those by Axial Attention U-Net,U-Net,Medical Transformer and other methods.And F1-score increased respectively by 2.99%,3.42%and 3.95%compared with Medical Transformer.展开更多
Accurate and reliable crack segmentation is a challenge and meaningful task.In this article,aiming at the characteristics of cracks on the concrete images,the intensity frequency information of source images which is ...Accurate and reliable crack segmentation is a challenge and meaningful task.In this article,aiming at the characteristics of cracks on the concrete images,the intensity frequency information of source images which is obtained by Discrete Wavelet Transform(DWT)is fed into deep learning-based networks to enhance the ability of network on crack segmentation.To well integrate frequency information into network an effective and novel DWTA module based on the DWT and scSE attention mechanism is proposed.The semantic information of cracks is enhanced and the irrelevant information is suppressed by DWTA module.And the gap between frequency information and convolution information from network is balanced by DWTA module which can well fuse wavelet information into image segmentation network.The Unet-DWTA is proposed to preserved the information of crack boundary and thin crack in intermediate feature maps by adding DWTA module in the encoderdecoder structures.In decoder,diverse level feature maps are fused to capture the information of crack boundary and the abstract semantic information which is beneficial to crack pixel classification.The proposed method is verified on three classic datasets including CrackDataset,CrackForest,and DeepCrack datasets.Compared with the other crack methods,the proposed Unet-DWTA shows better performance based on the evaluation of the subjective analysis and objective metrics about image semantic segmentation.展开更多
Retinal vessel segmentation in fundus images plays an essential role in the screening,diagnosis,and treatment of many diseases.The acquired fundus images generally have the following problems:uneven illumination,high ...Retinal vessel segmentation in fundus images plays an essential role in the screening,diagnosis,and treatment of many diseases.The acquired fundus images generally have the following problems:uneven illumination,high noise,and complex structure.It makes vessel segmentation very challenging.Previous methods of retinal vascular segmentation mainly use convolutional neural networks on U Network(U-Net)models,and they have many limitations and shortcomings,such as the loss of microvascular details at the end of the vessels.We address the limitations of convolution by introducing the transformer into retinal vessel segmentation.Therefore,we propose a hybrid method for retinal vessel segmentation based on modulated deformable convolution and the transformer,named DT-Net.Firstly,multi-scale image features are extracted by deformable convolution and multi-head selfattention(MHSA).Secondly,image information is recovered,and vessel morphology is refined by the proposed transformer decoder block.Finally,the local prediction results are obtained by the side output layer.The accuracy of the vessel segmentation is improved by the hybrid loss function.Experimental results show that our method obtains good segmentation performance on Specificity(SP),Sensitivity(SE),Accuracy(ACC),Curve(AUC),and F1-score on three publicly available fundus datasets such as DRIVE,STARE,and CHASE_DB1.展开更多
Modulate the electronic structure and surface energy by nanostructure and heteroatom doping is an efficient strategy to improve electrocatalytic activity of hydrogen evolution reaction(HER).Herein,nickel incorporated ...Modulate the electronic structure and surface energy by nanostructure and heteroatom doping is an efficient strategy to improve electrocatalytic activity of hydrogen evolution reaction(HER).Herein,nickel incorporated WP2 self-supporting nanosheet arrays cathode was synthesized on carbon cloth(Ni-WP2 NS/CC)by in-situ phosphating reduction of the Ni-doped WO3.It shows that heteroatom doping and the three-dimensional(3D)nanosheet arrays morphology both facilitate to reduce the interfacial transfer resistance and increase electrochemical-active surface areas,which effectively improve electrocatalytic hydrogen evolution reaction(HER)activity.The optimized catalyst,1%Ni-WP2 NS/CC,exhibits an outstanding electrocatalytic performance with an overpotential of 110 m V at 10 m A cm-2 and a Tafel slope of 65 m V dec-1 in the acid solution.DFT calculations further demonstrate the nickel doping can adjust the intrinsic structure of electronics,lower the Gibbs free energy of adsorption of hydrogen(DGH*),and effectively improve the HER performance.展开更多
This article aims to address the clustering effect caused by unorganized charging of electric vehicles by adopting a two-tier recommendation method.The electric vehicles(EVs)are classified into high-level alerts and g...This article aims to address the clustering effect caused by unorganized charging of electric vehicles by adopting a two-tier recommendation method.The electric vehicles(EVs)are classified into high-level alerts and general alerts based on their state of charge(SOC).EVs with high-level alerts have the most urgent charging needs,so the distance to charging stations is set as the highest priority for recommendations.For users with general alerts,a comprehensive EV charging station recommendation model is proposed,taking into account factors such as charging price,charging time,charging station preference,and distance to the charging station.Using real data from EV charging stations and ride-hailing vehicles in Xiamen City,Fujian Province,simulation analyses are conducted using Python for different periods of the day.The research results show that the stability of the multi-factor recommendation model in terms of service density variance,coverage rate,price cost,and distance cost outperform single-factor models.This indicates that our composite multi-factor recommendation model has significant practical value in resolving the clustering phenomenon caused by unorganized EV charging,optimizing the EV charging service system,and improving user satisfaction.展开更多
[目的]对比分析合并肩袖损伤的肱二头肌长头腱(long head of biceps tendon,LHBT)病变固定与切断术的疗效.[方法]回顾性分析2018年1月-2020年6月关节镜治疗合并肩袖损伤的LHBT病变30例患者的临床资料.其中,11例行LHBT固定术(固定组),19...[目的]对比分析合并肩袖损伤的肱二头肌长头腱(long head of biceps tendon,LHBT)病变固定与切断术的疗效.[方法]回顾性分析2018年1月-2020年6月关节镜治疗合并肩袖损伤的LHBT病变30例患者的临床资料.其中,11例行LHBT固定术(固定组),19例行LHBT切断术(切断组),两组均行肩袖修补.比较两组近期临床效果.[结果]两组患者均顺利完成手术,无严重并发症,两组手术时间、切口长度、失血量的差异均无统计学意义(P>0.05).与术前相比,末次随访时两组VAS评分均显著降低,而ASES评分显著增加(P0.05).单纯切断组出现1例Popeye畸形,术后两组患者屈伸肘部肌力无明显差异,肩关节主被动活动度无显著障碍.[结论]对合并肩袖损伤的LHBT病变治疗,LHBT固定术与LHBT切断术联合肩袖修补均有显著的近期临床疗效,但单纯腱切断术后有Popeye畸形发生可能.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 61972267the National Natural Science Foundation of Hebei Province under Grant F2018210148+1 种基金the University Science Research Project of Hebei Province under Grant ZD2021334the Science and Technology Project of Hebei Education Department(ZD2022098).
摘要In medical image segmentation task,convolutional neural networks(CNNs)are difficult to capture long-range dependencies,but transformers can model the long-range dependencies effectively.However,transformers have a flexible structure and seldom assume the structural bias of input data,so it is difficult for transformers to learn positional encoding of the medical images when using fewer images for training.To solve these problems,a dual branch structure is proposed.In one branch,Mix-Feed-Forward Network(Mix-FFN)and axial attention are adopted to capture long-range dependencies and keep the translation invariance of the model.Mix-FFN whose depth-wise convolutions can provide position information is better than ordinary positional encoding.In the other branch,traditional convolutional neural networks(CNNs)are used to extract different features of fewer medical images.In addition,the attention fusion module BiFusion is used to effectively integrate the information from the CNN branch and Transformer branch,and the fused features can effectively capture the global and local context of the current spatial resolution.On the public standard datasets Gland Segmentation(GlaS),Colorectal adenocarcinoma gland(CRAG)and COVID-19 CT Images Segmentation,the F1-score,Intersection over Union(IoU)and parameters of the proposed TC-Fuse are superior to those by Axial Attention U-Net,U-Net,Medical Transformer and other methods.And F1-score increased respectively by 2.99%,3.42%and 3.95%compared with Medical Transformer.
基金National Natural Science Foundation of China under Grant 61972267National Natural Science Foundation of Hebei Province under Grant F2018210148University Science Research Project of Hebei Province under Grant ZD2021334。
摘要Accurate and reliable crack segmentation is a challenge and meaningful task.In this article,aiming at the characteristics of cracks on the concrete images,the intensity frequency information of source images which is obtained by Discrete Wavelet Transform(DWT)is fed into deep learning-based networks to enhance the ability of network on crack segmentation.To well integrate frequency information into network an effective and novel DWTA module based on the DWT and scSE attention mechanism is proposed.The semantic information of cracks is enhanced and the irrelevant information is suppressed by DWTA module.And the gap between frequency information and convolution information from network is balanced by DWTA module which can well fuse wavelet information into image segmentation network.The Unet-DWTA is proposed to preserved the information of crack boundary and thin crack in intermediate feature maps by adding DWTA module in the encoderdecoder structures.In decoder,diverse level feature maps are fused to capture the information of crack boundary and the abstract semantic information which is beneficial to crack pixel classification.The proposed method is verified on three classic datasets including CrackDataset,CrackForest,and DeepCrack datasets.Compared with the other crack methods,the proposed Unet-DWTA shows better performance based on the evaluation of the subjective analysis and objective metrics about image semantic segmentation.
基金supported in part by the National Natural Science Foundation of China under Grant 61972267the National Natural Science Foundation of Hebei Province under Grant F2018210148the University Science Research Project of Hebei Province under Grant ZD2021334.
摘要Retinal vessel segmentation in fundus images plays an essential role in the screening,diagnosis,and treatment of many diseases.The acquired fundus images generally have the following problems:uneven illumination,high noise,and complex structure.It makes vessel segmentation very challenging.Previous methods of retinal vascular segmentation mainly use convolutional neural networks on U Network(U-Net)models,and they have many limitations and shortcomings,such as the loss of microvascular details at the end of the vessels.We address the limitations of convolution by introducing the transformer into retinal vessel segmentation.Therefore,we propose a hybrid method for retinal vessel segmentation based on modulated deformable convolution and the transformer,named DT-Net.Firstly,multi-scale image features are extracted by deformable convolution and multi-head selfattention(MHSA).Secondly,image information is recovered,and vessel morphology is refined by the proposed transformer decoder block.Finally,the local prediction results are obtained by the side output layer.The accuracy of the vessel segmentation is improved by the hybrid loss function.Experimental results show that our method obtains good segmentation performance on Specificity(SP),Sensitivity(SE),Accuracy(ACC),Curve(AUC),and F1-score on three publicly available fundus datasets such as DRIVE,STARE,and CHASE_DB1.
基金supported by the National Natural Science Foundation of China(21503051,21563007)the Natural Science Foundation of Guangxi Province(2019GXNSFFA245016,2018GXNSFAA138108)。
摘要Modulate the electronic structure and surface energy by nanostructure and heteroatom doping is an efficient strategy to improve electrocatalytic activity of hydrogen evolution reaction(HER).Herein,nickel incorporated WP2 self-supporting nanosheet arrays cathode was synthesized on carbon cloth(Ni-WP2 NS/CC)by in-situ phosphating reduction of the Ni-doped WO3.It shows that heteroatom doping and the three-dimensional(3D)nanosheet arrays morphology both facilitate to reduce the interfacial transfer resistance and increase electrochemical-active surface areas,which effectively improve electrocatalytic hydrogen evolution reaction(HER)activity.The optimized catalyst,1%Ni-WP2 NS/CC,exhibits an outstanding electrocatalytic performance with an overpotential of 110 m V at 10 m A cm-2 and a Tafel slope of 65 m V dec-1 in the acid solution.DFT calculations further demonstrate the nickel doping can adjust the intrinsic structure of electronics,lower the Gibbs free energy of adsorption of hydrogen(DGH*),and effectively improve the HER performance.
基金the Jiangsu Provincial College Students Innovation and Entrepreneurship Training Plan Project(Grant Number 202311276097Y).
摘要This article aims to address the clustering effect caused by unorganized charging of electric vehicles by adopting a two-tier recommendation method.The electric vehicles(EVs)are classified into high-level alerts and general alerts based on their state of charge(SOC).EVs with high-level alerts have the most urgent charging needs,so the distance to charging stations is set as the highest priority for recommendations.For users with general alerts,a comprehensive EV charging station recommendation model is proposed,taking into account factors such as charging price,charging time,charging station preference,and distance to the charging station.Using real data from EV charging stations and ride-hailing vehicles in Xiamen City,Fujian Province,simulation analyses are conducted using Python for different periods of the day.The research results show that the stability of the multi-factor recommendation model in terms of service density variance,coverage rate,price cost,and distance cost outperform single-factor models.This indicates that our composite multi-factor recommendation model has significant practical value in resolving the clustering phenomenon caused by unorganized EV charging,optimizing the EV charging service system,and improving user satisfaction.
摘要[目的]对比分析合并肩袖损伤的肱二头肌长头腱(long head of biceps tendon,LHBT)病变固定与切断术的疗效.[方法]回顾性分析2018年1月-2020年6月关节镜治疗合并肩袖损伤的LHBT病变30例患者的临床资料.其中,11例行LHBT固定术(固定组),19例行LHBT切断术(切断组),两组均行肩袖修补.比较两组近期临床效果.[结果]两组患者均顺利完成手术,无严重并发症,两组手术时间、切口长度、失血量的差异均无统计学意义(P>0.05).与术前相比,末次随访时两组VAS评分均显著降低,而ASES评分显著增加(P0.05).单纯切断组出现1例Popeye畸形,术后两组患者屈伸肘部肌力无明显差异,肩关节主被动活动度无显著障碍.[结论]对合并肩袖损伤的LHBT病变治疗,LHBT固定术与LHBT切断术联合肩袖修补均有显著的近期临床疗效,但单纯腱切断术后有Popeye畸形发生可能.