Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Ou...Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.展开更多
Traditional tumor models do not tend to accurately simulate tumor growth in vitro or enable personalized treatment and are particularly unable to discover more beneficial targeted drugs.To address this,this study desc...Traditional tumor models do not tend to accurately simulate tumor growth in vitro or enable personalized treatment and are particularly unable to discover more beneficial targeted drugs.To address this,this study describes the use of threedimensional(3D)bioprinting technology to construct a 3D model with human hepatocarcinoma SMMC-7721 cells(3DP-7721)by combining gelatin methacrylate(GelMA)and poly(ethylene oxide)(PEO)as two immiscible aqueous phases to form a bioink and innovatively applying fluorescent carbon quantum dots for long-term tracking of cells.The GelMA(10%,mass fraction)and PEO(1.6%,mass fraction)hydrogel with 3:1 volume ratio offered distinct pore-forming characteristics,satisfactorymechanical properties,and biocompatibility for the creation of the 3DP-7721 model.Immunofluorescence analysis and quantitative real-time fluorescence polymerase chain reaction(PCR)were used to evaluate the biological properties of the model.Compared with the two-dimensional culture cell model(2D-7721)and the 3D mixed culture cell model(3DM-7721),3DP-7721 significantly improved the proliferation of cells and expression of tumor-related proteins and genes.Moreover,we evaluated the differences between the three culture models and the effectiveness of antitumor drugs in the three models and discovered that the efficacy of antitumor drugs varied because of significant differences in resistance proteins and genes between the three models.In addition,the comparison of tumor formation in the three models found that the cells cultured by the 3DP-7721 model had strong tumorigenicity in nude mice.Immunohistochemical evaluation of the levels of biochemical indicators related to the formation of solid tumors showed that the 3DP-7721 model group exhibited pathological characteristics of malignant tumors,the generated solid tumors were similar to actual tumors,and the deterioration was higher.This research therefore acts as a foundation for the application of 3DP-7721 models in drug development research.展开更多
Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transp...Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.展开更多
Landslide is the second largest natural disaster after earthquake. It is of significance to study the evolution laws and failure mechanism of landslides based on its surface 3D deformation information. Based on the ra...Landslide is the second largest natural disaster after earthquake. It is of significance to study the evolution laws and failure mechanism of landslides based on its surface 3D deformation information. Based on the rainfall-triggered waste dump instability model test, we studied the failure mechanisms of the waste dump by integrating surface deformation and internal slope stress and proposed novel parameters for identifying landslide stability. We developed a noncontact measurement device, which can obtain millimeter-level 3D deformation data for surface scene in physical model test;Then we developed the similar materials and established a test model for a waste dump. Based on the failure characteristics of slope surface, internal stress of slope body and displacement contours during the whole process, we divided the slope instability process in model test into four stages: rainfall infiltration and surface erosion, shallow sliding, deep sliding, and overall instability. Based on the obtained surface deformation data, we calculated the volume change during slope instability process and compared it with the point displacement on slope surface. The results showed that the volume change can not only reflect the slow-ultra acceleration process of slope failure, but also fully reflect the above four stages and reduce the fluctuations caused by random factors. Finally, this paper proposed two stability identification parameters: the volume change rate above the slip surface and the relative velocity of volume change rate. According to the calculation of these two parameters in model test, they can be used for study the deformation and failure mechanism of slope stability.展开更多
Catastrophic geological disasters frequently occur on slopes with obliquely inclined bedding structures(also referred to as obliquely inclined bedding slopes),where the apparent dip sliding is not readily visible.This...Catastrophic geological disasters frequently occur on slopes with obliquely inclined bedding structures(also referred to as obliquely inclined bedding slopes),where the apparent dip sliding is not readily visible.This phenomenon has become a focal point in landslide research.Yet,there is a lack of studies on the failure modes and mechanisms of hidden,steep obliquely inclined bedding slopes.This study investigated the Shanyang landslide in Shaanxi Province,China.Using field investigations,laboratory tests of geotechnical parameters,and the 3DEC software,this study developed a numerical model of the landslide to analyze the failure process of such slopes.The findings indicate that the Shanyang landslide primarily crept along a weak interlayer under the action of gravity.The landslide,initially following a dip angle with the support of a stable inclined rock mass,shifted direction under the influence of argillization in the weak interlayer,moving towards the apparent dip angle.The slide resistance effect of the karstic dissolution zone was increasingly significant during this process,with lateral friction being the primary resistance force.A reduction in the lateral friction due to karstic dissolution made the apparent dip sliding characteristics of the Shanyang landslide more pronounced.Notably,deformations such as bending and uplift at the slope’s foot suggest that the main slide resistance shifts from lateral friction within the karstic dissolution zone to the slope foot’s resistance force,leading to the eventual buckling failure of the landslide.This study unveils a novel failure mode of apparent dip creep-buckling in the Shanyang landslide,highlighting the critical role of lateral friction from the karstic dissolution zone in its failure mechanism.These insights offer a valuable reference for mitigating risks and preventing disasters related to obliquely inclined bedding landslides.展开更多
The paper presents our contribution to the full 3D finite element modelling of a hybrid stepping motor using COMSOL Multiphysics software. This type of four-phase motor has a permanent magnet interposed between the tw...The paper presents our contribution to the full 3D finite element modelling of a hybrid stepping motor using COMSOL Multiphysics software. This type of four-phase motor has a permanent magnet interposed between the two identical and coaxial half stators. The calculation of the field with or without current in the windings (respectively with or without permanent magnet) is done using a mixed formulation with strong coupling. In addition, the local high saturation of the ferromagnetic material and the radial and axial components of the magnetic flux are taken into account. The results obtained make it possible to clearly observe, as a function of the intensity of the bus current or the remanent induction, the saturation zones, the lines, the orientations and the magnetic flux densities. 3D finite element modelling provide more accurate numerical data on the magnetic field through multiphysics analysis. This analysis considers the actual operating conditions and leads to the design of an optimized machine structure, with or without current in the windings and/or permanent magnet.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
The wave/particle duality of particles in Physics is well known. Particles have properties that uniquely characterize them from one another, such as mass, charge and spin. Charged particles have associated Electric an...The wave/particle duality of particles in Physics is well known. Particles have properties that uniquely characterize them from one another, such as mass, charge and spin. Charged particles have associated Electric and Magnetic fields. Also, every moving particle has a De Broglie wavelength determined by its mass and velocity. This paper shows that all of these properties of a particle can be derived from a single wave function equation for that particle. Wave functions for the Electron and the Positron are presented and principles are provided that can be used to calculate the wave functions of all the fundamental particles in Physics. Fundamental particles such as electrons and positrons are considered to be point particles in the Standard Model of Physics and are not considered to have a structure. This paper demonstrates that they do indeed have structure and that this structure extends into the space around the particle’s center (in fact, they have infinite extent), but with rapidly diminishing energy density with the distance from that center. The particles are formed from Electromagnetic standing waves, which are stable solutions to the Schrödinger and Classical wave equations. This stable structure therefore accounts for both the wave and particle nature of these particles. In fact, all of their properties such as mass, spin and electric charge, can be accounted for from this structure. These particle properties appear to originate from a single point at the center of the wave function structure, in the same sort of way that the Shell theorem of gravity causes the gravity of a body to appear to all originate from a central point. This paper represents the first two fully characterized fundamental particles, with a complete description of their structure and properties, built up from the underlying Electromagnetic waves that comprise these and all fundamental particles.展开更多
骨盆CT影像精确分割是骨盆骨疾病的临床诊断和手术规划中非常重要的环节。针对目前2D骨盆分割方法对三维医学影像进行切片处理时损失空间信息的问题,提出了改进3D U-Net网络实现对骨盆CT影像3D自动分割。实验数据为公开数据集CTPelvic1K...骨盆CT影像精确分割是骨盆骨疾病的临床诊断和手术规划中非常重要的环节。针对目前2D骨盆分割方法对三维医学影像进行切片处理时损失空间信息的问题,提出了改进3D U-Net网络实现对骨盆CT影像3D自动分割。实验数据为公开数据集CTPelvic1K共1184名患者骨盆CT影像,其中包含骶骨、左髋骨、右髋骨和腰椎四个部位标签。以3D U-Net骨干网络为基础,结合自注意力机制提出3D多类分割模型3D Trans U-Net,并使用迁移学习训练3D U-Net、V-Net、Attention U-Net作为对照实验。实验结果表明:3D Trans U-Net在测试集上整个骨盆区域、骶骨、左髋骨、右髋骨、腰椎Dice系数分别达到97.99%,96.70%,97.96%,97.95%,96.89%;Dice系数、豪斯多夫距离等评价指标均优于现有经典网络3D U-Net、V-Net、Attention U-Net。因此,改进的3D Trans U-Net对骨盆不同部位具有较好的分割效果,为精准医治骨盆骨疾病提供了一条有效的技术途径。展开更多
Deep learning-based systems have succeeded in many computer vision tasks.However,it is found that the latest study indicates that these systems are in danger in the presence of adversarial attacks.These attacks can qu...Deep learning-based systems have succeeded in many computer vision tasks.However,it is found that the latest study indicates that these systems are in danger in the presence of adversarial attacks.These attacks can quickly spoil deep learning models,e.g.,different convolutional neural networks(CNNs),used in various computer vision tasks from image classification to object detection.The adversarial examples are carefully designed by injecting a slight perturbation into the clean images.The proposed CRU-Net defense model is inspired by state-of-the-art defense mechanisms such as MagNet defense,Generative Adversarial Net-work Defense,Deep Regret Analytic Generative Adversarial Networks Defense,Deep Denoising Sparse Autoencoder Defense,and Condtional Generattive Adversarial Network Defense.We have experimentally proved that our approach is better than previous defensive techniques.Our proposed CRU-Net model maps the adversarial image examples into clean images by eliminating the adversarial perturbation.The proposed defensive approach is based on residual and U-Net learning.Many experiments are done on the datasets MNIST and CIFAR10 to prove that our proposed CRU-Net defense model prevents adversarial example attacks in WhiteBox and BlackBox settings and improves the robustness of the deep learning algorithms especially in the computer visionfield.We have also reported similarity(SSIM and PSNR)between the original and restored clean image examples by the proposed CRU-Net defense model.展开更多
This research is focused on the analysis of the sequence stratigraphic units of F3 Block,within a wave-dominated delta of Plio–Pleistocene age.Three wells of F3 block and a 3D seismic data,are utilized in this resear...This research is focused on the analysis of the sequence stratigraphic units of F3 Block,within a wave-dominated delta of Plio–Pleistocene age.Three wells of F3 block and a 3D seismic data,are utilized in this research.The conventional techniques of 3D seismic interpretation were utilized to mark the 11 surfaces on the seismic section.Integration of seismic sequence stratigraphic interpretation,using well logs,and subsequent 3D geostatistical modeling,using seismic data,aided to evaluate the shallow hydrocarbon traps.The resulting models were obtained using System Tract and Facies models,which were generated by using sequential stimulation method and their variograms made by spherical method,moreover,these models are validated via histograms.The CDF curve generated from upscaling of well logs using geometric method,shows a good relation with less percentage of errors(1 to 2 for Facies and 3 to 4 for System Tract models)between upscaled and raw data that complements the resulted models.These approaches help us to delineate the best possible reservoir,lateral extent of system tracts(LST and/or HST)in the respective surface,and distribution of sand and shale in the delta.The clinoform break points alteration observed on seismic sections,also validates the sequence stratigraphic interpretation.The GR log-based Facies model and sequence stratigraphy-based System Tract model of SU-04-2 showed the reservoir characteristics,presence of sand bodies and majorly LST,respectively,mainly adjacent to the main fault of the studied area.Moreover,on the seismic section,SU-04-2 exhibits the presence of gas pockets at the same location that also complements the generated Facies and System Tract models.The generated models can be utilized for any similar kind of study and for the further research in the F3 block reservoir characterization.展开更多
文摘Deep learning, especially through convolutional neural networks (CNN) such as the U-Net 3D model, has revolutionized fault identification from seismic data, representing a significant leap over traditional methods. Our review traces the evolution of CNN, emphasizing the adaptation and capabilities of the U-Net 3D model in automating seismic fault delineation with unprecedented accuracy. We find: 1) The transition from basic neural networks to sophisticated CNN has enabled remarkable advancements in image recognition, which are directly applicable to analyzing seismic data. The U-Net 3D model, with its innovative architecture, exemplifies this progress by providing a method for detailed and accurate fault detection with reduced manual interpretation bias. 2) The U-Net 3D model has demonstrated its superiority over traditional fault identification methods in several key areas: it has enhanced interpretation accuracy, increased operational efficiency, and reduced the subjectivity of manual methods. 3) Despite these achievements, challenges such as the need for effective data preprocessing, acquisition of high-quality annotated datasets, and achieving model generalization across different geological conditions remain. Future research should therefore focus on developing more complex network architectures and innovative training strategies to refine fault identification performance further. Our findings confirm the transformative potential of deep learning, particularly CNN like the U-Net 3D model, in geosciences, advocating for its broader integration to revolutionize geological exploration and seismic analysis.
基金supported by the National Natural Science Foundation of China(Nos.51975400 and 62031022)Shanxi Provincial Key Medical Scientific Research Project(Nos.2020XM06 and 2021XM12)+3 种基金Fundamental Research Program of Shanxi Province(No.202103021224081)Shanxi Provincial Basic Research Project(Nos.202103021221006 and 202103021223040)Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(No.2021L044)Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering(No.2022SX-TD026).
文摘Traditional tumor models do not tend to accurately simulate tumor growth in vitro or enable personalized treatment and are particularly unable to discover more beneficial targeted drugs.To address this,this study describes the use of threedimensional(3D)bioprinting technology to construct a 3D model with human hepatocarcinoma SMMC-7721 cells(3DP-7721)by combining gelatin methacrylate(GelMA)and poly(ethylene oxide)(PEO)as two immiscible aqueous phases to form a bioink and innovatively applying fluorescent carbon quantum dots for long-term tracking of cells.The GelMA(10%,mass fraction)and PEO(1.6%,mass fraction)hydrogel with 3:1 volume ratio offered distinct pore-forming characteristics,satisfactorymechanical properties,and biocompatibility for the creation of the 3DP-7721 model.Immunofluorescence analysis and quantitative real-time fluorescence polymerase chain reaction(PCR)were used to evaluate the biological properties of the model.Compared with the two-dimensional culture cell model(2D-7721)and the 3D mixed culture cell model(3DM-7721),3DP-7721 significantly improved the proliferation of cells and expression of tumor-related proteins and genes.Moreover,we evaluated the differences between the three culture models and the effectiveness of antitumor drugs in the three models and discovered that the efficacy of antitumor drugs varied because of significant differences in resistance proteins and genes between the three models.In addition,the comparison of tumor formation in the three models found that the cells cultured by the 3DP-7721 model had strong tumorigenicity in nude mice.Immunohistochemical evaluation of the levels of biochemical indicators related to the formation of solid tumors showed that the 3DP-7721 model group exhibited pathological characteristics of malignant tumors,the generated solid tumors were similar to actual tumors,and the deterioration was higher.This research therefore acts as a foundation for the application of 3DP-7721 models in drug development research.
基金the National Natural Science Foundation of China(Nos.62272063,62072056 and 61902041)the Natural Science Foundation of Hunan Province(Nos.2022JJ30617 and 2020JJ2029)+4 种基金Open Research Fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology,Nanjing University of Posts and Telecommunications(No.JZNY202102)the Traffic Science and Technology Project of Hunan Province,China(No.202042)Hunan Provincial Key Research and Development Program(No.2022GK2019)this work was funded by the Researchers Supporting Project Number(RSPD2023R681)King Saud University,Riyadh,Saudi Arabia.
文摘Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.
基金funded by the National Key R&D Program of China (Grant No. 2021YFB3901402)the Fundamental Research Funds for the Central Universities (Project No. 2022CDJKYJH037)。
文摘Landslide is the second largest natural disaster after earthquake. It is of significance to study the evolution laws and failure mechanism of landslides based on its surface 3D deformation information. Based on the rainfall-triggered waste dump instability model test, we studied the failure mechanisms of the waste dump by integrating surface deformation and internal slope stress and proposed novel parameters for identifying landslide stability. We developed a noncontact measurement device, which can obtain millimeter-level 3D deformation data for surface scene in physical model test;Then we developed the similar materials and established a test model for a waste dump. Based on the failure characteristics of slope surface, internal stress of slope body and displacement contours during the whole process, we divided the slope instability process in model test into four stages: rainfall infiltration and surface erosion, shallow sliding, deep sliding, and overall instability. Based on the obtained surface deformation data, we calculated the volume change during slope instability process and compared it with the point displacement on slope surface. The results showed that the volume change can not only reflect the slow-ultra acceleration process of slope failure, but also fully reflect the above four stages and reduce the fluctuations caused by random factors. Finally, this paper proposed two stability identification parameters: the volume change rate above the slip surface and the relative velocity of volume change rate. According to the calculation of these two parameters in model test, they can be used for study the deformation and failure mechanism of slope stability.
基金jointly supported by the projects of the China Geological Survey(DD20230092,DD20201119)。
文摘Catastrophic geological disasters frequently occur on slopes with obliquely inclined bedding structures(also referred to as obliquely inclined bedding slopes),where the apparent dip sliding is not readily visible.This phenomenon has become a focal point in landslide research.Yet,there is a lack of studies on the failure modes and mechanisms of hidden,steep obliquely inclined bedding slopes.This study investigated the Shanyang landslide in Shaanxi Province,China.Using field investigations,laboratory tests of geotechnical parameters,and the 3DEC software,this study developed a numerical model of the landslide to analyze the failure process of such slopes.The findings indicate that the Shanyang landslide primarily crept along a weak interlayer under the action of gravity.The landslide,initially following a dip angle with the support of a stable inclined rock mass,shifted direction under the influence of argillization in the weak interlayer,moving towards the apparent dip angle.The slide resistance effect of the karstic dissolution zone was increasingly significant during this process,with lateral friction being the primary resistance force.A reduction in the lateral friction due to karstic dissolution made the apparent dip sliding characteristics of the Shanyang landslide more pronounced.Notably,deformations such as bending and uplift at the slope’s foot suggest that the main slide resistance shifts from lateral friction within the karstic dissolution zone to the slope foot’s resistance force,leading to the eventual buckling failure of the landslide.This study unveils a novel failure mode of apparent dip creep-buckling in the Shanyang landslide,highlighting the critical role of lateral friction from the karstic dissolution zone in its failure mechanism.These insights offer a valuable reference for mitigating risks and preventing disasters related to obliquely inclined bedding landslides.
文摘The paper presents our contribution to the full 3D finite element modelling of a hybrid stepping motor using COMSOL Multiphysics software. This type of four-phase motor has a permanent magnet interposed between the two identical and coaxial half stators. The calculation of the field with or without current in the windings (respectively with or without permanent magnet) is done using a mixed formulation with strong coupling. In addition, the local high saturation of the ferromagnetic material and the radial and axial components of the magnetic flux are taken into account. The results obtained make it possible to clearly observe, as a function of the intensity of the bus current or the remanent induction, the saturation zones, the lines, the orientations and the magnetic flux densities. 3D finite element modelling provide more accurate numerical data on the magnetic field through multiphysics analysis. This analysis considers the actual operating conditions and leads to the design of an optimized machine structure, with or without current in the windings and/or permanent magnet.
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.
文摘The wave/particle duality of particles in Physics is well known. Particles have properties that uniquely characterize them from one another, such as mass, charge and spin. Charged particles have associated Electric and Magnetic fields. Also, every moving particle has a De Broglie wavelength determined by its mass and velocity. This paper shows that all of these properties of a particle can be derived from a single wave function equation for that particle. Wave functions for the Electron and the Positron are presented and principles are provided that can be used to calculate the wave functions of all the fundamental particles in Physics. Fundamental particles such as electrons and positrons are considered to be point particles in the Standard Model of Physics and are not considered to have a structure. This paper demonstrates that they do indeed have structure and that this structure extends into the space around the particle’s center (in fact, they have infinite extent), but with rapidly diminishing energy density with the distance from that center. The particles are formed from Electromagnetic standing waves, which are stable solutions to the Schrödinger and Classical wave equations. This stable structure therefore accounts for both the wave and particle nature of these particles. In fact, all of their properties such as mass, spin and electric charge, can be accounted for from this structure. These particle properties appear to originate from a single point at the center of the wave function structure, in the same sort of way that the Shell theorem of gravity causes the gravity of a body to appear to all originate from a central point. This paper represents the first two fully characterized fundamental particles, with a complete description of their structure and properties, built up from the underlying Electromagnetic waves that comprise these and all fundamental particles.
文摘骨盆CT影像精确分割是骨盆骨疾病的临床诊断和手术规划中非常重要的环节。针对目前2D骨盆分割方法对三维医学影像进行切片处理时损失空间信息的问题,提出了改进3D U-Net网络实现对骨盆CT影像3D自动分割。实验数据为公开数据集CTPelvic1K共1184名患者骨盆CT影像,其中包含骶骨、左髋骨、右髋骨和腰椎四个部位标签。以3D U-Net骨干网络为基础,结合自注意力机制提出3D多类分割模型3D Trans U-Net,并使用迁移学习训练3D U-Net、V-Net、Attention U-Net作为对照实验。实验结果表明:3D Trans U-Net在测试集上整个骨盆区域、骶骨、左髋骨、右髋骨、腰椎Dice系数分别达到97.99%,96.70%,97.96%,97.95%,96.89%;Dice系数、豪斯多夫距离等评价指标均优于现有经典网络3D U-Net、V-Net、Attention U-Net。因此,改进的3D Trans U-Net对骨盆不同部位具有较好的分割效果,为精准医治骨盆骨疾病提供了一条有效的技术途径。
文摘Deep learning-based systems have succeeded in many computer vision tasks.However,it is found that the latest study indicates that these systems are in danger in the presence of adversarial attacks.These attacks can quickly spoil deep learning models,e.g.,different convolutional neural networks(CNNs),used in various computer vision tasks from image classification to object detection.The adversarial examples are carefully designed by injecting a slight perturbation into the clean images.The proposed CRU-Net defense model is inspired by state-of-the-art defense mechanisms such as MagNet defense,Generative Adversarial Net-work Defense,Deep Regret Analytic Generative Adversarial Networks Defense,Deep Denoising Sparse Autoencoder Defense,and Condtional Generattive Adversarial Network Defense.We have experimentally proved that our approach is better than previous defensive techniques.Our proposed CRU-Net model maps the adversarial image examples into clean images by eliminating the adversarial perturbation.The proposed defensive approach is based on residual and U-Net learning.Many experiments are done on the datasets MNIST and CIFAR10 to prove that our proposed CRU-Net defense model prevents adversarial example attacks in WhiteBox and BlackBox settings and improves the robustness of the deep learning algorithms especially in the computer visionfield.We have also reported similarity(SSIM and PSNR)between the original and restored clean image examples by the proposed CRU-Net defense model.
文摘This research is focused on the analysis of the sequence stratigraphic units of F3 Block,within a wave-dominated delta of Plio–Pleistocene age.Three wells of F3 block and a 3D seismic data,are utilized in this research.The conventional techniques of 3D seismic interpretation were utilized to mark the 11 surfaces on the seismic section.Integration of seismic sequence stratigraphic interpretation,using well logs,and subsequent 3D geostatistical modeling,using seismic data,aided to evaluate the shallow hydrocarbon traps.The resulting models were obtained using System Tract and Facies models,which were generated by using sequential stimulation method and their variograms made by spherical method,moreover,these models are validated via histograms.The CDF curve generated from upscaling of well logs using geometric method,shows a good relation with less percentage of errors(1 to 2 for Facies and 3 to 4 for System Tract models)between upscaled and raw data that complements the resulted models.These approaches help us to delineate the best possible reservoir,lateral extent of system tracts(LST and/or HST)in the respective surface,and distribution of sand and shale in the delta.The clinoform break points alteration observed on seismic sections,also validates the sequence stratigraphic interpretation.The GR log-based Facies model and sequence stratigraphy-based System Tract model of SU-04-2 showed the reservoir characteristics,presence of sand bodies and majorly LST,respectively,mainly adjacent to the main fault of the studied area.Moreover,on the seismic section,SU-04-2 exhibits the presence of gas pockets at the same location that also complements the generated Facies and System Tract models.The generated models can be utilized for any similar kind of study and for the further research in the F3 block reservoir characterization.
