With the development of smart agriculture,accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge.This study focused on apple leaf disease,which is...With the development of smart agriculture,accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge.This study focused on apple leaf disease,which is closely related to the final yield of apples.A multiscale fusion dense network combined with an efficient multiscale attention(EMA)mechanism called Incept_EMA_DenseNet was developed to better identify eight complex apple leaf disease images.Incept_EMA_DenseNet consists of three crucial parts:the inception module,which substituted the convolution layer with multiscale fusion methods in the shallow feature extraction layer;the EMA mechanism,which is used for obtaining appropriate weights of different dense blocks;and the improved DenseNet based on DenseNet_121.Specifically,to find appropriate multiscale fusion methods,the residual module and inception module were compared to determine the performance of each technique,and Incept_EMA_DenseNet achieved an accuracy of 95.38%.Second,this work used three attention mechanisms,and the efficient multiscale attention mechanism obtained the best performance.Third,the convolution layers and bottlenecks were modified without performance degradation,reducing half of the computational load compared with the original models.Incept_EMA_DenseNet,as proposed in this paper,has an accuracy of 96.76%,being 2.93%,3.44%,and 4.16%better than Resnet50,DenseNet_121 and GoogLeNet,respectively,proved to be reliable and beneficial,and can effectively and conveniently assist apple growers with leaf disease identification in the field.展开更多
The goal of street-to-aerial cross-view image geo-localization is to determine the location of the query street-view image by retrieving the aerial-view image from the same place.The drastic viewpoint and appearance g...The goal of street-to-aerial cross-view image geo-localization is to determine the location of the query street-view image by retrieving the aerial-view image from the same place.The drastic viewpoint and appearance gap between the aerial-view and the street-view images brings a huge challenge against this task.In this paper,we propose a novel multiscale attention encoder to capture the multiscale contextual information of the aerial/street-view images.To bridge the domain gap between these two view images,we first use an inverse polar transform to make the street-view images approximately aligned with the aerial-view images.Then,the explored multiscale attention encoder is applied to convert the image into feature representation with the guidance of the learnt multiscale information.Finally,we propose a novel global mining strategy to enable the network to pay more attention to hard negative exemplars.Experiments on standard benchmark datasets show that our approach obtains 81.39%top-1 recall rate on the CVUSA dataset and 71.52%on the CVACT dataset,achieving the state-of-the-art performance and outperforming most of the existing methods significantly.展开更多
The state-of-charge(SOC)and state-of-health(SOH)of lithium-ion batteries affect their operating performance and safety.The coupled SOC and SOH are difficult to estimate adaptively in multi-temperatures and aging.This ...The state-of-charge(SOC)and state-of-health(SOH)of lithium-ion batteries affect their operating performance and safety.The coupled SOC and SOH are difficult to estimate adaptively in multi-temperatures and aging.This paper proposes a novel transformer-embedded lithium-ion battery model for joint estimation of state-ofcharge and state-of-health.The battery model is formulated across temperatures and aging,which provides accurate feedback for unscented Kalman filter-based SOC estimation and aging information.The open-circuit voltages(OCVs)are corrected globally by the temporal convolutional network with accurate OCVs in time-sliding windows.Arrhenius equation is combined with estimated SOH for temperature-aging migration.A novel transformer model is introduced,which integrates multiscale attention with the transformer's encoder to incorporate SOC-voltage differential derived from battery model.This model simultaneously extracts local aging information from various sequences and aging channels using a self-attention and depth-separate convolution.By leveraging multi-head attention,the model establishes information dependency relationships across different aging levels,enabling rapid and precise SOH estimation.Specifically,the root mean square error for SOC and SOH under conditions of 15℃dynamic stress test and 25℃constant current cycling was less than 0.9%and 0.8%,respectively.Notably,the proposed method exhibits excellent adaptability to varying temperature and aging conditions,accurately estimating SOC and SOH.展开更多
Automatic segmentation and classification of brain tumors are of great importance to clinical treatment.However,they are challenging due to the varied and small morphology of the tumors.In this paper,we propose a mult...Automatic segmentation and classification of brain tumors are of great importance to clinical treatment.However,they are challenging due to the varied and small morphology of the tumors.In this paper,we propose a multitask multiscale residual attention network(MMRAN)to simultaneously solve the problem of accurately segmenting and classifying brain tumors.The proposed MMRAN is based on U-Net,and a parallel branch is added at the end of the encoder as the classification network.First,we propose a novel multiscale residual attention module(MRAM)that can aggregate contextual features and combine channel attention and spatial attention better and add it to the shared parameter layer of MMRAN.Second,we propose a method of dynamic weight training that can improve model performance while minimizing the need for multiple experiments to determine the optimal weights for each task.Finally,prior knowledge of brain tumors is added to the postprocessing of segmented images to further improve the segmentation accuracy.We evaluated MMRAN on a brain tumor data set containing meningioma,glioma,and pituitary tumors.In terms of segmentation performance,our method achieves Dice,Hausdorff distance(HD),mean intersection over union(MIoU),and mean pixel accuracy(MPA)values of 80.03%,6.649 mm,84.38%,and 89.41%,respectively.In terms of classification performance,our method achieves accuracy,recall,precision,and F1-score of 89.87%,90.44%,88.56%,and 89.49%,respectively.Compared with other networks,MMRAN performs better in segmentation and classification,which significantly aids medical professionals in brain tumor management.The code and data set are available at https://github.com/linkenfaqiu/MMRAN.展开更多
基金fully supported by the National Natural Science Foundation of China(52072412)。
文摘With the development of smart agriculture,accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge.This study focused on apple leaf disease,which is closely related to the final yield of apples.A multiscale fusion dense network combined with an efficient multiscale attention(EMA)mechanism called Incept_EMA_DenseNet was developed to better identify eight complex apple leaf disease images.Incept_EMA_DenseNet consists of three crucial parts:the inception module,which substituted the convolution layer with multiscale fusion methods in the shallow feature extraction layer;the EMA mechanism,which is used for obtaining appropriate weights of different dense blocks;and the improved DenseNet based on DenseNet_121.Specifically,to find appropriate multiscale fusion methods,the residual module and inception module were compared to determine the performance of each technique,and Incept_EMA_DenseNet achieved an accuracy of 95.38%.Second,this work used three attention mechanisms,and the efficient multiscale attention mechanism obtained the best performance.Third,the convolution layers and bottlenecks were modified without performance degradation,reducing half of the computational load compared with the original models.Incept_EMA_DenseNet,as proposed in this paper,has an accuracy of 96.76%,being 2.93%,3.44%,and 4.16%better than Resnet50,DenseNet_121 and GoogLeNet,respectively,proved to be reliable and beneficial,and can effectively and conveniently assist apple growers with leaf disease identification in the field.
基金National Natural Science Foundation of China,Grant/Award Number:62106177supported by the Central University Basic Research Fund of China(No.2042020KF0016)supported by the supercomputing system in the Supercomputing Center of Wuhan University.
文摘The goal of street-to-aerial cross-view image geo-localization is to determine the location of the query street-view image by retrieving the aerial-view image from the same place.The drastic viewpoint and appearance gap between the aerial-view and the street-view images brings a huge challenge against this task.In this paper,we propose a novel multiscale attention encoder to capture the multiscale contextual information of the aerial/street-view images.To bridge the domain gap between these two view images,we first use an inverse polar transform to make the street-view images approximately aligned with the aerial-view images.Then,the explored multiscale attention encoder is applied to convert the image into feature representation with the guidance of the learnt multiscale information.Finally,we propose a novel global mining strategy to enable the network to pay more attention to hard negative exemplars.Experiments on standard benchmark datasets show that our approach obtains 81.39%top-1 recall rate on the CVUSA dataset and 71.52%on the CVACT dataset,achieving the state-of-the-art performance and outperforming most of the existing methods significantly.
基金financially supported by the Science and Technology Major Project of Fujian Province of China(No.2022HZ028018)the National Natural Science Foundation of China(No.51907030)。
文摘The state-of-charge(SOC)and state-of-health(SOH)of lithium-ion batteries affect their operating performance and safety.The coupled SOC and SOH are difficult to estimate adaptively in multi-temperatures and aging.This paper proposes a novel transformer-embedded lithium-ion battery model for joint estimation of state-ofcharge and state-of-health.The battery model is formulated across temperatures and aging,which provides accurate feedback for unscented Kalman filter-based SOC estimation and aging information.The open-circuit voltages(OCVs)are corrected globally by the temporal convolutional network with accurate OCVs in time-sliding windows.Arrhenius equation is combined with estimated SOH for temperature-aging migration.A novel transformer model is introduced,which integrates multiscale attention with the transformer's encoder to incorporate SOC-voltage differential derived from battery model.This model simultaneously extracts local aging information from various sequences and aging channels using a self-attention and depth-separate convolution.By leveraging multi-head attention,the model establishes information dependency relationships across different aging levels,enabling rapid and precise SOH estimation.Specifically,the root mean square error for SOC and SOH under conditions of 15℃dynamic stress test and 25℃constant current cycling was less than 0.9%and 0.8%,respectively.Notably,the proposed method exhibits excellent adaptability to varying temperature and aging conditions,accurately estimating SOC and SOH.
基金This paper was supported by National Natural Science Foundation of China(No.61977063 and 61872020).The authors thank all the patients for providing their MRI images and School of Biomedical Engineering at Southern Medical University,China for providing the brain tumor data set.We appreciate Dr.Fenfen Li,Wenzhou Eye Hospital,Wenzhou Medical University,China,for her support with clinical consulting and language editing.
文摘Automatic segmentation and classification of brain tumors are of great importance to clinical treatment.However,they are challenging due to the varied and small morphology of the tumors.In this paper,we propose a multitask multiscale residual attention network(MMRAN)to simultaneously solve the problem of accurately segmenting and classifying brain tumors.The proposed MMRAN is based on U-Net,and a parallel branch is added at the end of the encoder as the classification network.First,we propose a novel multiscale residual attention module(MRAM)that can aggregate contextual features and combine channel attention and spatial attention better and add it to the shared parameter layer of MMRAN.Second,we propose a method of dynamic weight training that can improve model performance while minimizing the need for multiple experiments to determine the optimal weights for each task.Finally,prior knowledge of brain tumors is added to the postprocessing of segmented images to further improve the segmentation accuracy.We evaluated MMRAN on a brain tumor data set containing meningioma,glioma,and pituitary tumors.In terms of segmentation performance,our method achieves Dice,Hausdorff distance(HD),mean intersection over union(MIoU),and mean pixel accuracy(MPA)values of 80.03%,6.649 mm,84.38%,and 89.41%,respectively.In terms of classification performance,our method achieves accuracy,recall,precision,and F1-score of 89.87%,90.44%,88.56%,and 89.49%,respectively.Compared with other networks,MMRAN performs better in segmentation and classification,which significantly aids medical professionals in brain tumor management.The code and data set are available at https://github.com/linkenfaqiu/MMRAN.
