Two branches of Tangjiagou rock avalanche were triggered by Lushan earthquake in Sichuan Province,China on April 20th,2013.The rock avalanche has transported about 1500000 m3 of sandstone from the source area.Based on...Two branches of Tangjiagou rock avalanche were triggered by Lushan earthquake in Sichuan Province,China on April 20th,2013.The rock avalanche has transported about 1500000 m3 of sandstone from the source area.Based on discrete element modeling,this study simulates the deformation,failure and movement process of the rock avalanche.Under seismic loading,the mechanism and process of deformation,failure,and runout of the two branches are similar.In detail,the stress concentration occur firstly on the top of the mountain ridge,and accordingly,the tensile deformation appears.With the increase of seismic loading,the strain concentration zone extends in the forward and backward directions along the slipping surface,forming a locking segment.As a result,the slipping surface penetrates and the slide mass begin to slide down with high speed.Finally,the avalanche accumulates in the downstream and forms a small barrier lake.Modeling shows that a number of rocks on the surface exhibit patterns of horizontal throwing and vertical jumping under strong ground shaking.We suggest that the movement of the rock avalanche is a complicated process with multiple stages,including formation of the two branches,high-speed sliding,transformation into debris flows,further movement and collision,accumulation,and the final steady state.Topographic amplification effects are also revealed based on acceleration and velocity of special monitoring points.The horizontal and vertical runout distances of the surface materials are much greater than those of the internal materials.Besides,the sliding duration is also longer than that of the internal rock mass.展开更多
In the field of hyperspectral image(HSI)classification in remote sensing,the combination of spectral and spatial features has gained considerable attention.In addition,the multiscale feature extraction approach is ver...In the field of hyperspectral image(HSI)classification in remote sensing,the combination of spectral and spatial features has gained considerable attention.In addition,the multiscale feature extraction approach is very effective at improving the classification accuracy for HSIs,capable of capturing a large amount of intrinsic information.However,some existing methods for extracting spectral and spatial features can only generate low-level features and consider limited scales,leading to low classification results,and dense-connection based methods enhance the feature propagation at the cost of high model complexity.This paper presents a two-branch multiscale spectral-spatial feature extraction network(TBMSSN)for HSI classification.We design the mul-tiscale spectral feature extraction(MSEFE)and multiscale spatial feature extraction(MSAFE)modules to improve the feature representation,and a spatial attention mechanism is applied in the MSAFE module to reduce redundant information and enhance the representation of spatial fea-tures at multiscale.Then we densely connect series of MSEFE or MSAFE modules respectively in a two-branch framework to balance efficiency and effectiveness,alleviate the vanishing-gradient problem and strengthen the feature propagation.To evaluate the effectiveness of the proposed method,the experimental results were carried out on bench mark HsI datasets,demonstrating that TBMSSN obtained higher classification accuracy compared with 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supported by the NationalNatural Science Foundation of China(41402254)Department of Science and Technology of Shaanxi Province(2019ZDLSF07-0701)。
文摘Two branches of Tangjiagou rock avalanche were triggered by Lushan earthquake in Sichuan Province,China on April 20th,2013.The rock avalanche has transported about 1500000 m3 of sandstone from the source area.Based on discrete element modeling,this study simulates the deformation,failure and movement process of the rock avalanche.Under seismic loading,the mechanism and process of deformation,failure,and runout of the two branches are similar.In detail,the stress concentration occur firstly on the top of the mountain ridge,and accordingly,the tensile deformation appears.With the increase of seismic loading,the strain concentration zone extends in the forward and backward directions along the slipping surface,forming a locking segment.As a result,the slipping surface penetrates and the slide mass begin to slide down with high speed.Finally,the avalanche accumulates in the downstream and forms a small barrier lake.Modeling shows that a number of rocks on the surface exhibit patterns of horizontal throwing and vertical jumping under strong ground shaking.We suggest that the movement of the rock avalanche is a complicated process with multiple stages,including formation of the two branches,high-speed sliding,transformation into debris flows,further movement and collision,accumulation,and the final steady state.Topographic amplification effects are also revealed based on acceleration and velocity of special monitoring points.The horizontal and vertical runout distances of the surface materials are much greater than those of the internal materials.Besides,the sliding duration is also longer than that of the internal rock mass.
基金supported by the National Natural Science Foundation of China(62077038,61672405,62176196 and 62271374)。
文摘In the field of hyperspectral image(HSI)classification in remote sensing,the combination of spectral and spatial features has gained considerable attention.In addition,the multiscale feature extraction approach is very effective at improving the classification accuracy for HSIs,capable of capturing a large amount of intrinsic information.However,some existing methods for extracting spectral and spatial features can only generate low-level features and consider limited scales,leading to low classification results,and dense-connection based methods enhance the feature propagation at the cost of high model complexity.This paper presents a two-branch multiscale spectral-spatial feature extraction network(TBMSSN)for HSI classification.We design the mul-tiscale spectral feature extraction(MSEFE)and multiscale spatial feature extraction(MSAFE)modules to improve the feature representation,and a spatial attention mechanism is applied in the MSAFE module to reduce redundant information and enhance the representation of spatial fea-tures at multiscale.Then we densely connect series of MSEFE or MSAFE modules respectively in a two-branch framework to balance efficiency and effectiveness,alleviate the vanishing-gradient problem and strengthen the feature propagation.To evaluate the effectiveness of the proposed method,the experimental results were carried out on bench mark HsI datasets,demonstrating that TBMSSN obtained higher classification accuracy compared with 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.
