To balance the speed and accuracy in semantic segmentation of the urban street images for autonomous driving,we proposed an improved U-Net network.Firstly,to improve the model representation capability,our improved U-...To balance the speed and accuracy in semantic segmentation of the urban street images for autonomous driving,we proposed an improved U-Net network.Firstly,to improve the model representation capability,our improved U-Net network structure was designed as three parts,shallow layer,intermediate layer and deep layer.Different attention mechanisms were used according to their feature extraction characteristics.Specifically,a spatial attention module was used in the shallow network,a dual attention module was used in the intermediate layer network and a channel attention module was used in the deep network.At the same time,the traditional convolution was replaced by depthwise separable convolution in above three parts,which can largely reduce the number of network parameters,and improve the network operation speed greatly.The experimental results on three datasets show that our improved U-Net semantic segmentation model for street images can get better results in both segmentation accuracy and speed.The average mean intersection over union(MIoU)is 68.8%,which is increased by 9.2%and the computation speed is about 38 ms/frame.We can process 27 frames images for segmentation per second,which meets the real-time process and accuracy requirements for semantic segmentation of urban street images.展开更多
To solve the problems of convolutional neural network–principal component analysis(CNN-PCA)in fine description and generalization of complex reservoir geological features,a 3D attention U-Net network was proposed not...To solve the problems of convolutional neural network–principal component analysis(CNN-PCA)in fine description and generalization of complex reservoir geological features,a 3D attention U-Net network was proposed not using a trained C3D video motion analysis model to extract the style of a 3D model,and applied to complement the details of geologic model lost in the dimension reduction of PCA method in this study.The 3D attention U-Net network was applied to a complex river channel sandstone reservoir to test its effects.The results show that compared with CNN-PCA method,the 3D attention U-Net network could better complement the details of geological model lost in the PCA dimension reduction,better reflect the fluid flow features in the original geologic model,and improve history matching results.展开更多
Brown adipose tissue(BAT)is a kind of adipose tissue engaging in thermoregulatory thermogenesis,metaboloregulatory thermogenesis,and secretory.Current studies have revealed that BAT activity is negatively correlated w...Brown adipose tissue(BAT)is a kind of adipose tissue engaging in thermoregulatory thermogenesis,metaboloregulatory thermogenesis,and secretory.Current studies have revealed that BAT activity is negatively correlated with adult body weight and is considered a target tissue for the treatment of obesity and other metabolic-related diseases.Additionally,the activity of BAT presents certain differences between different ages and genders.Clinically,BAT segmentation based on PET/CT data is a reliable method for brown fat research.However,most of the current BAT segmentation methods rely on the experience of doctors.In this paper,an improved U-net network,ICA-Unet,is proposed to achieve automatic and precise segmentation of BAT.First,the traditional 2D convolution layer in the encoder is replaced with a depth-wise overparameterized convolutional(Do-Conv)layer.Second,the channel attention block is introduced between the double-layer convolution.Finally,the image information entropy(IIE)block is added in the skip connections to strengthen the edge features.Furthermore,the performance of this method is evaluated on the dataset of PET/CT images from 368 patients.The results demonstrate a strong agreement between the automatic segmentation of BAT and manual annotation by experts.The average DICE coeffcient(DSC)is 0.9057,and the average Hausdorff distance is 7.2810.Experimental results suggest that the method proposed in this paper can achieve effcient and accurate automatic BAT segmentation and satisfy the clinical requirements of BAT.展开更多
Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific ...Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.展开更多
基金supported by the National Natural Science Foundation China(No.61601174)the Postdoctoral Research Foundation of Heilongjiang Province(No.LBH-Q17150)the Science and Technology Innovative Research Team in Higher Educational Institutions of Heilongjiang Province(No.2012TD007)。
文摘To balance the speed and accuracy in semantic segmentation of the urban street images for autonomous driving,we proposed an improved U-Net network.Firstly,to improve the model representation capability,our improved U-Net network structure was designed as three parts,shallow layer,intermediate layer and deep layer.Different attention mechanisms were used according to their feature extraction characteristics.Specifically,a spatial attention module was used in the shallow network,a dual attention module was used in the intermediate layer network and a channel attention module was used in the deep network.At the same time,the traditional convolution was replaced by depthwise separable convolution in above three parts,which can largely reduce the number of network parameters,and improve the network operation speed greatly.The experimental results on three datasets show that our improved U-Net semantic segmentation model for street images can get better results in both segmentation accuracy and speed.The average mean intersection over union(MIoU)is 68.8%,which is increased by 9.2%and the computation speed is about 38 ms/frame.We can process 27 frames images for segmentation per second,which meets the real-time process and accuracy requirements for semantic segmentation of urban street images.
基金Supported by the China National Oil and Gas Major Project(2016ZX05010-003)PetroChina Science and Technology Major Project(2019B1210,2021DJ1201).
文摘To solve the problems of convolutional neural network–principal component analysis(CNN-PCA)in fine description and generalization of complex reservoir geological features,a 3D attention U-Net network was proposed not using a trained C3D video motion analysis model to extract the style of a 3D model,and applied to complement the details of geologic model lost in the dimension reduction of PCA method in this study.The 3D attention U-Net network was applied to a complex river channel sandstone reservoir to test its effects.The results show that compared with CNN-PCA method,the 3D attention U-Net network could better complement the details of geological model lost in the PCA dimension reduction,better reflect the fluid flow features in the original geologic model,and improve history matching results.
基金supported in part by the National Natural Science Foundation of China(61701403,82122033,81871379)National Key Research and Development Program of China(2016YFC0103804,2019YFC1521103,2020YFC1523301,2019YFC-1521102)+3 种基金Key R&D Projects in Shaanxi Province(2019ZDLSF07-02,2019ZDLGY10-01)Key R&D Projects in Qinghai Province(2020-SF-143)China Post-doctoral Science Foundation(2018M643719)Young Talent Support Program of the Shaanxi Association for Science and Technology(20190107).
文摘Brown adipose tissue(BAT)is a kind of adipose tissue engaging in thermoregulatory thermogenesis,metaboloregulatory thermogenesis,and secretory.Current studies have revealed that BAT activity is negatively correlated with adult body weight and is considered a target tissue for the treatment of obesity and other metabolic-related diseases.Additionally,the activity of BAT presents certain differences between different ages and genders.Clinically,BAT segmentation based on PET/CT data is a reliable method for brown fat research.However,most of the current BAT segmentation methods rely on the experience of doctors.In this paper,an improved U-net network,ICA-Unet,is proposed to achieve automatic and precise segmentation of BAT.First,the traditional 2D convolution layer in the encoder is replaced with a depth-wise overparameterized convolutional(Do-Conv)layer.Second,the channel attention block is introduced between the double-layer convolution.Finally,the image information entropy(IIE)block is added in the skip connections to strengthen the edge features.Furthermore,the performance of this method is evaluated on the dataset of PET/CT images from 368 patients.The results demonstrate a strong agreement between the automatic segmentation of BAT and manual annotation by experts.The average DICE coeffcient(DSC)is 0.9057,and the average Hausdorff distance is 7.2810.Experimental results suggest that the method proposed in this paper can achieve effcient and accurate automatic BAT segmentation and satisfy the clinical requirements of BAT.
基金Project(2020YFC2008605)supported by the National Key Research and Development Project of ChinaProject(52072412)supported by the National Natural Science Foundation of ChinaProject(2021JJ30359)supported by the Natural Science Foundation of Hunan Province,China。
文摘Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.
