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
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
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
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
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
The outputs of the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System(FGOALSf3-L)model for the decadal climate prediction project(DCPP)of the Coupled Model Intercomparison Project Phase 6(CMI...The outputs of the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System(FGOALSf3-L)model for the decadal climate prediction project(DCPP)of the Coupled Model Intercomparison Project Phase 6(CMIP6)are described in this paper.The FGOALS-f3-L was initialized through the upgraded,weakly coupled data assimilation scheme,referred to as EnOI-IAU,which assimilates observational anomalies of sea surface temperature(SST)and upper-level(0–1000-m)ocean temperature and salinity profiles into the coupled model.Then,nine ensemble members of 10-year hindcast/forecast experiments were conducted for each initial year over the period of 1960–2021,based on initial conditions produced by three initialization experiments.The hindcast and forecast experiments follow the experiment designs of the Component-A and Component-B of the DCPP,respectively.The decadal prediction output datasets contain a total of 44 monthly mean atmospheric and oceanic variables.The preliminary evaluation indicates that the hindcast experiments show significant predictive skill for the interannual variations of SST in the north Pacific and multi-year variations of SST in the subtropical Pacific and the southern Indian Ocean.展开更多
Mining industrial areas with anthropogenic engineering structures are one of the most distinctive features of the real world.3D models of the real world have been increasingly popular with numerous applications,such a...Mining industrial areas with anthropogenic engineering structures are one of the most distinctive features of the real world.3D models of the real world have been increasingly popular with numerous applications,such as digital twins and smart factory management.In this study,3D models of mining engineering structures were built based on the CityGML standard.For collecting spatial data,the two most popular geospatial technologies,namely UAV-SfM and TLS were employed.The accuracy of the UAV survey was at the centimeter level,and it satisfied the absolute positional accuracy requirement of creat-ing all levels of detail(LoD)according to the CityGML standard.Therefore,the UAV-SfM point cloud dataset was used to build LoD 2 models.In addition,the comparison between the UAV-SfM and TLS sub-clouds of facades and roofs indicates that the UAV-SfM and TLS point clouds of these objects are highly consistent,therefore,point clouds with a higher level of detail and accuracy provided by the integration of UAV-SfM and TLS were used to build LoD 3 models.The resulting 3D CityGML models include 39 buildings at LoD 2,and two mine shafts with hoistrooms,headframes,and sheave wheels at LoD3.展开更多
To understand the current application and development of 3D modeling in Digital Twins(DTs),abundant literatures on DTs and 3D modeling are investigated by means of literature review.The transition process from 3D mode...To understand the current application and development of 3D modeling in Digital Twins(DTs),abundant literatures on DTs and 3D modeling are investigated by means of literature review.The transition process from 3D modeling to DTs modeling is analyzed,as well as the current application of DTs modeling in various industries.The application of 3D DTs modeling in theelds of smartmanufacturing,smart ecology,smart transportation,and smart buildings in smart cities is analyzed in detail,and the current limitations are summarized.It is found that the 3D modeling technology in DTs has broad prospects for development and has a huge impact on all walks of life and even human lifestyles.At the same time,the development of DTs modeling relies on the development and support capabilities of mature technologies such as Big Data,Internet of Things,Cloud Computing,Articial Intelligence,and game technology.Therefore,although some results have been achieved,there are still limitations.This work aims to provide a good theoretical support for the further development of 3D DTs modeling.展开更多
Background With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system ...Background With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system load and resource consumption in the processing of these huge models, the huge 3D model lightweighting methods for online Web3D visualization are reviewed in this paper. Methods By observing the geometry redundancy introduced by man-made operations in the modeling procedure, several categories of light-weighting related work that aim at reducing the amount of data and resource consumption are elaborated for Web3D visualization. Results By comparing perspectives, the characteristics of each method are summarized, and among the reviewed methods, the geometric redundancy removal that achieves the lightweight goal by detecting and removing the repeated components is an appropriate method for current online Web3D visualization. Meanwhile, the learning algorithm, still in improvement period at present, is our expected future research topic. Conclusions Various aspects should be considered in an efficient lightweight method for online Web3D visualization, such as characteristics of original data, combination or extension of existing methods, scheduling strategy, cache man-agement, and rendering mechanism. Meanwhile, innovation methods, particularly the learning algorithm, are worth exploring.展开更多
In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a...In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.展开更多
This study used the stable and convergent Dufort-Frankel method to differentially discretize the diffusion equation of the ground-well transient electromagnetic secondary field.The absorption boundary condition of com...This study used the stable and convergent Dufort-Frankel method to differentially discretize the diffusion equation of the ground-well transient electromagnetic secondary field.The absorption boundary condition of complex frequency-shifted perfectly matched layer(CFS-PML)was used for truncation so that the low-frequency electromagnetic wave can be better absorbed at the model boundary.A typical three-dimensional(3D)homogeneous half-space model was established and a low-resistivity cube model was analyzed under the half-space condition.The response patterns and drivers of the low-resistivity cube model were discussed under the influence of a low-resistivity overburden.The absorption boundary conditions of CFS-PML significantly affected the low-frequency electromagnetic waves.For a low-resistivity cube around the borehole,its response curve exhibited a single-peak,and the extreme point of the curve corresponded to the center of the low-resistivity body.When the low-resistivity cube was directly below the borehole,the response curve showed three extreme values(two high and one low),with the low corresponding to the center of the low-resistivity body.The total field response of the low-resistivity overburden was stronger than that of the uniform half-space model due to the low-resistivity shielding effect of electromagnetic waves.When the receiving-transmitting distance gradually increased,the effect of the low-resistivity overburden was gradually weakened,and the response of the low-resistivity cube was strengthened.It was affected by the ratio of the overburden resistivity to the resistivity of the low-resistivity body.展开更多
Nowadays,Web browsers have become an important carrier of 3D model visualization because of their convenience and portability.During the process of large-scale 3D model visualization based on Web scenes with the probl...Nowadays,Web browsers have become an important carrier of 3D model visualization because of their convenience and portability.During the process of large-scale 3D model visualization based on Web scenes with the problems of slow rendering speed and low FPS(Frames Per Second),occlusion culling,as an important method for rendering optimization,can remove most of the occluded objects and improve rendering efficiency.The traditional occlusion culling algorithm(TOCA)is calculated by traversing all objects in the scene,which involves a large amount of repeated calculation and time consumption.To advance the rendering process and enhance rendering efficiency,this paper proposes an occlusion culling with three different optimization methods based on the WebGPU Computing Pipeline.Firstly,for the problem of large amounts of repeated calculation processes in TOCA,these units are moved from the CPU to the GPU for parallel computing,thereby accelerating the calculation of the Potential Visible Sets(PVS);Then,for the huge overhead of creating pipeline caused by too many 3D models in a certain scene,the Breaking Occlusion Culling Algorithm(BOCA)is introduced,which removes some nodes according to building a Hierarchical Bounding Volume(BVH)scene tree to reduce the overhead of creating pipelines;After that,the structure of the scene tree is transmitted to the GPU in the order of depth-first traversal and finally,the PVS is obtained by parallel computing.In the experiments,3D geological models with five different scales from 1:5,000 to 1:500,000 are used for testing.The results show that the proposed methods can reduce the time overhead of repeated calculation caused by the computing pipeline creation and scene tree recursive traversal in the occlusion culling algorithm effectively,with 97%rendering efficiency improvement compared with BOCA,thereby accelerating the rendering process on Web browsers.展开更多
The growing demand for current and precise geographic information that pertains to urban areas has given rise to a significant interest in digital surface models that exhibit a high level of detail. Traditional method...The growing demand for current and precise geographic information that pertains to urban areas has given rise to a significant interest in digital surface models that exhibit a high level of detail. Traditional methods for creating digital surface models are insufficient to reflect the details of earth’s features. These models only represent three-dimensional objects in a single texture and fail to offer a realistic depiction of the real world. Furthermore, the need for current and precise geographic information regarding urban areas has been increasing significantly. This study proposes a new technique to address this problem, which involves integrating remote sensing, Geographic Information Systems (GIS), and Architecture Environment software environments to generate a detailed three-dimensional model. The processing of this study starts with: 1) Downloading high-resolution satellite imagery; 2) Collecting ground truth datasets from fieldwork; 3) Imaging nose removing; 4) Generating a Two-dimensional Model to create a digital surface model in GIS using the extracted building outlines; 5) Converting the model into multi-patch layers to construct a 3D model for each object separately. The results show that the 3D model obtained through this method is highly detailed and effective for various applications, including environmental studies, urban development, expansion planning, and shape understanding tasks.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
LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previou...LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previous object detection methods,due to the pre-processing of the original LIDAR point cloud into voxels or pillars,lose the coordinate information of the original point cloud,slow detection speed,and gain inaccurate bounding box positioning.To address the issues above,this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++,which effectively preserves the original point cloud coordinate information.To improve the detection accuracy,a shell-based modeling method is proposed.It roughly determines which spherical shell the coordinates belong to.Then,the results are refined to ground truth,thereby narrowing the localization range and improving the detection accuracy.To improve the recall of 3D object detection with bounding boxes,this paper designs a self-attention module for 3D object detection with a skip connection structure.Some of these features are highlighted by weighting them on the feature dimensions.After training,it makes the feature weights that are favorable for object detection get larger.Thus,the extracted features are more adapted to the object detection task.Extensive comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.展开更多
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.
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.
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.
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.
基金supported by National Key Research and Development Program of China(Grant No.2018YFA0606300)the NSFC(Grant No.42075163),the NSFC BSCTPES project(Grant No.41988101)+1 种基金the NSFC(Grant No.42205039)supported by the Jiangsu Collaborative Innovation Center for Climate Change and the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab).
文摘The outputs of the Chinese Academy of Sciences(CAS)Flexible Global Ocean-Atmosphere-Land System(FGOALSf3-L)model for the decadal climate prediction project(DCPP)of the Coupled Model Intercomparison Project Phase 6(CMIP6)are described in this paper.The FGOALS-f3-L was initialized through the upgraded,weakly coupled data assimilation scheme,referred to as EnOI-IAU,which assimilates observational anomalies of sea surface temperature(SST)and upper-level(0–1000-m)ocean temperature and salinity profiles into the coupled model.Then,nine ensemble members of 10-year hindcast/forecast experiments were conducted for each initial year over the period of 1960–2021,based on initial conditions produced by three initialization experiments.The hindcast and forecast experiments follow the experiment designs of the Component-A and Component-B of the DCPP,respectively.The decadal prediction output datasets contain a total of 44 monthly mean atmospheric and oceanic variables.The preliminary evaluation indicates that the hindcast experiments show significant predictive skill for the interannual variations of SST in the north Pacific and multi-year variations of SST in the subtropical Pacific and the southern Indian Ocean.
基金his research was funded by Hanoi university of Mining and Geology,Grant Number T22-47.
文摘Mining industrial areas with anthropogenic engineering structures are one of the most distinctive features of the real world.3D models of the real world have been increasingly popular with numerous applications,such as digital twins and smart factory management.In this study,3D models of mining engineering structures were built based on the CityGML standard.For collecting spatial data,the two most popular geospatial technologies,namely UAV-SfM and TLS were employed.The accuracy of the UAV survey was at the centimeter level,and it satisfied the absolute positional accuracy requirement of creat-ing all levels of detail(LoD)according to the CityGML standard.Therefore,the UAV-SfM point cloud dataset was used to build LoD 2 models.In addition,the comparison between the UAV-SfM and TLS sub-clouds of facades and roofs indicates that the UAV-SfM and TLS point clouds of these objects are highly consistent,therefore,point clouds with a higher level of detail and accuracy provided by the integration of UAV-SfM and TLS were used to build LoD 3 models.The resulting 3D CityGML models include 39 buildings at LoD 2,and two mine shafts with hoistrooms,headframes,and sheave wheels at LoD3.
文摘To understand the current application and development of 3D modeling in Digital Twins(DTs),abundant literatures on DTs and 3D modeling are investigated by means of literature review.The transition process from 3D modeling to DTs modeling is analyzed,as well as the current application of DTs modeling in various industries.The application of 3D DTs modeling in theelds of smartmanufacturing,smart ecology,smart transportation,and smart buildings in smart cities is analyzed in detail,and the current limitations are summarized.It is found that the 3D modeling technology in DTs has broad prospects for development and has a huge impact on all walks of life and even human lifestyles.At the same time,the development of DTs modeling relies on the development and support capabilities of mature technologies such as Big Data,Internet of Things,Cloud Computing,Articial Intelligence,and game technology.Therefore,although some results have been achieved,there are still limitations.This work aims to provide a good theoretical support for the further development of 3D DTs modeling.
文摘Background With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system load and resource consumption in the processing of these huge models, the huge 3D model lightweighting methods for online Web3D visualization are reviewed in this paper. Methods By observing the geometry redundancy introduced by man-made operations in the modeling procedure, several categories of light-weighting related work that aim at reducing the amount of data and resource consumption are elaborated for Web3D visualization. Results By comparing perspectives, the characteristics of each method are summarized, and among the reviewed methods, the geometric redundancy removal that achieves the lightweight goal by detecting and removing the repeated components is an appropriate method for current online Web3D visualization. Meanwhile, the learning algorithm, still in improvement period at present, is our expected future research topic. Conclusions Various aspects should be considered in an efficient lightweight method for online Web3D visualization, such as characteristics of original data, combination or extension of existing methods, scheduling strategy, cache man-agement, and rendering mechanism. Meanwhile, innovation methods, particularly the learning algorithm, are worth exploring.
基金supported by the project of the National Social Science Fundation(21BJL052,20BJY020,20BJL127,19BJY090)the 2018 Fujian Social Science Planning Project(FJ2018B067)The Planning Fund Project of Humanities and Social Sciences Research of the Ministry of Education in 2019(19YJA790102),The grant has been received by Aoqi Xu.
文摘In many problems,to analyze the process/metabolism behavior,a mod-el of the system is identified.The main gap is the weakness of current methods vs.noisy environments.The primary objective of this study is to present a more robust method against uncertainties.This paper proposes a new deep learning scheme for modeling and identification applications.The suggested approach is based on non-singleton type-3 fuzzy logic systems(NT3-FLSs)that can support measurement errors and high-level uncertainties.Besides the rule optimization,the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square root cubature Kalmanfilter(SCKF).In the learn-ing algorithm,the presented NT3-FLSs are deeply learned,and their nonlinear structure is preserved.The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets.Through various ana-lyses and comparisons,the better efficiency of the proposed fuzzy modeling scheme is verified.The main advantages of the suggested approach include better resistance against uncertainties,deep learning,and good convergence.
基金This work was supported by China Postdoctoral Science Foundation(No.2022M723391)the Science and Technology Innovation Project of Higher Education in Shanxi Province(No.2019L0754)+1 种基金the Central Guiding Local Science and Technology Development Fund Project(No.YDZJSX2021B021)Shanxi Province Basic Research Plan General Project(No.202203021221294).
文摘This study used the stable and convergent Dufort-Frankel method to differentially discretize the diffusion equation of the ground-well transient electromagnetic secondary field.The absorption boundary condition of complex frequency-shifted perfectly matched layer(CFS-PML)was used for truncation so that the low-frequency electromagnetic wave can be better absorbed at the model boundary.A typical three-dimensional(3D)homogeneous half-space model was established and a low-resistivity cube model was analyzed under the half-space condition.The response patterns and drivers of the low-resistivity cube model were discussed under the influence of a low-resistivity overburden.The absorption boundary conditions of CFS-PML significantly affected the low-frequency electromagnetic waves.For a low-resistivity cube around the borehole,its response curve exhibited a single-peak,and the extreme point of the curve corresponded to the center of the low-resistivity body.When the low-resistivity cube was directly below the borehole,the response curve showed three extreme values(two high and one low),with the low corresponding to the center of the low-resistivity body.The total field response of the low-resistivity overburden was stronger than that of the uniform half-space model due to the low-resistivity shielding effect of electromagnetic waves.When the receiving-transmitting distance gradually increased,the effect of the low-resistivity overburden was gradually weakened,and the response of the low-resistivity cube was strengthened.It was affected by the ratio of the overburden resistivity to the resistivity of the low-resistivity body.
基金supported by the National Natural Science Foundation of China (42172333,41902304,U1711267)the fund of the State Key Laboratory of Biogeology and Environmental Geology (2021)+1 种基金Science and Technology Strategic Prospecting Project of Guizhou Province ( [2022]ZD003)the Knowledge Innovation Program of Wuhan-Shuguang Project (2022010801020206).
文摘Nowadays,Web browsers have become an important carrier of 3D model visualization because of their convenience and portability.During the process of large-scale 3D model visualization based on Web scenes with the problems of slow rendering speed and low FPS(Frames Per Second),occlusion culling,as an important method for rendering optimization,can remove most of the occluded objects and improve rendering efficiency.The traditional occlusion culling algorithm(TOCA)is calculated by traversing all objects in the scene,which involves a large amount of repeated calculation and time consumption.To advance the rendering process and enhance rendering efficiency,this paper proposes an occlusion culling with three different optimization methods based on the WebGPU Computing Pipeline.Firstly,for the problem of large amounts of repeated calculation processes in TOCA,these units are moved from the CPU to the GPU for parallel computing,thereby accelerating the calculation of the Potential Visible Sets(PVS);Then,for the huge overhead of creating pipeline caused by too many 3D models in a certain scene,the Breaking Occlusion Culling Algorithm(BOCA)is introduced,which removes some nodes according to building a Hierarchical Bounding Volume(BVH)scene tree to reduce the overhead of creating pipelines;After that,the structure of the scene tree is transmitted to the GPU in the order of depth-first traversal and finally,the PVS is obtained by parallel computing.In the experiments,3D geological models with five different scales from 1:5,000 to 1:500,000 are used for testing.The results show that the proposed methods can reduce the time overhead of repeated calculation caused by the computing pipeline creation and scene tree recursive traversal in the occlusion culling algorithm effectively,with 97%rendering efficiency improvement compared with BOCA,thereby accelerating the rendering process on Web browsers.
文摘The growing demand for current and precise geographic information that pertains to urban areas has given rise to a significant interest in digital surface models that exhibit a high level of detail. Traditional methods for creating digital surface models are insufficient to reflect the details of earth’s features. These models only represent three-dimensional objects in a single texture and fail to offer a realistic depiction of the real world. Furthermore, the need for current and precise geographic information regarding urban areas has been increasing significantly. This study proposes a new technique to address this problem, which involves integrating remote sensing, Geographic Information Systems (GIS), and Architecture Environment software environments to generate a detailed three-dimensional model. The processing of this study starts with: 1) Downloading high-resolution satellite imagery; 2) Collecting ground truth datasets from fieldwork; 3) Imaging nose removing; 4) Generating a Two-dimensional Model to create a digital surface model in GIS using the extracted building outlines; 5) Converting the model into multi-patch layers to construct a 3D model for each object separately. The results show that the 3D model obtained through this method is highly detailed and effective for various applications, including environmental studies, urban development, expansion planning, and shape understanding tasks.
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This work was supported,in part,by the National Nature Science Foundation of China under grant numbers 62272236in part,by the Natural Science Foundation of Jiangsu Province under grant numbers BK20201136,BK20191401in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fund.
文摘LIDAR point cloud-based 3D object detection aims to sense the surrounding environment by anchoring objects with the Bounding Box(BBox).However,under the three-dimensional space of autonomous driving scenes,the previous object detection methods,due to the pre-processing of the original LIDAR point cloud into voxels or pillars,lose the coordinate information of the original point cloud,slow detection speed,and gain inaccurate bounding box positioning.To address the issues above,this study proposes a new two-stage network structure to extract point cloud features directly by PointNet++,which effectively preserves the original point cloud coordinate information.To improve the detection accuracy,a shell-based modeling method is proposed.It roughly determines which spherical shell the coordinates belong to.Then,the results are refined to ground truth,thereby narrowing the localization range and improving the detection accuracy.To improve the recall of 3D object detection with bounding boxes,this paper designs a self-attention module for 3D object detection with a skip connection structure.Some of these features are highlighted by weighting them on the feature dimensions.After training,it makes the feature weights that are favorable for object detection get larger.Thus,the extracted features are more adapted to the object detection task.Extensive comparison experiments and ablation experiments conducted on the KITTI dataset verify the effectiveness of our proposed method in improving recall and precision.
