3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

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.

A review of road 3D modeling based on light detection and ranging point clouds

Increasing development of accurate and efficient road three-dimensional(3D)modeling presents great opportunities to improve the data exchange and integration of building information modeling(BIM)models.3D modeling of road scenes is crucial for reference in asset management,construction,and maintenance.Light detection and ranging(Li DAR)technology is increasingly employed to generate high-quality point clouds for road inventory.In this paper,we specifically investigate the use of Li DAR data for road 3D modeling.The purpose of this review is to provide references about the existing work on the road 3D modeling based on Li DAR point clouds,critically discuss them,and provide challenges for further study.Besides,we introduce modeling standards for roads and discuss the components,types,and distinctions of various Li DAR measurement systems.Then,we review state-of-the-art methods and provide a detailed examination of road segmentation and feature extraction.Furthermore,we systematically introduce point cloud-based 3D modeling methods,namely,parametric modeling and surface reconstruction.Parameters and rules are used to define model components based on geometric and non-geometric information,whereas surface modeling is conducted through individual faces within its geometry.Finally,we discuss and summarize future research directions in this field.This review can assist researchers in enhancing existing approaches and developing new techniques for road modeling based on Li DAR point clouds.

UAV-based transient electromagnetic 3D forward modeling and inversion and analysis of exploration capability

Unmanned aerial vehicle transient electromagnetic(UAV-TEM)is a novel airborne exploration method that offers advantages such as low cost,simple operation,high exploration efficiency and suitability for near-surface exploration in complex terrain areas.To improve the accuracy of data interpretation in this method,the authors conducted a systematic three-dimensional(3D)forward modeling and inversion of the UAV-TEM.This study utilized the finite element method based on unstructured tetrahedral elements and employed the second-order backward Euler method for time discretization.This allowed for accurate 3D modeling and accounted for the effects of complex terrain.Based on these,the influence characteristics of flight altitudes and the sizes,burial depths,and resistivities of anomalies are compared and analyzed to explore the UAV-TEM systems’exploration capability.Lastly,four typical geoelectrical models of landslides are designed,and the inversion method based on the Gauss-Newton optimization method is used to image the landslide models and analyze the imaging effect of the UAV-TEM method on landslide geohazards.Numerical results showed that UAV-TEM could have better exploration resolution and fine imaging of nearsurface structures,providing important technical support for monitoring,early warning,and preventing landslides and other geological hazards.

3D time-domain forward modeling of airborne transient electromagnetism considering superparamagnetic effect

The superparamagnetic effect arises from the superparamagnetism exhibited by a multitude of nano-sized magnetic mineral particles under an external electric field.This phenomenon manifests in transient electromagnetic data primarily as a deceleration in the attenuation rate of late-stage signals,a characteristic difficult to discern directly from airborne transient electromagnetic signals,consequently leading to significant misinterpretations of subterranean electrical structures.This study embarks on 3D forward modeling of airborne electromagnetic responses in the frequency domain,accounting for the superparamagnetic effect,utilizing an unstructured finite element method.Superparamagnetic responses in the time domain were obtained through frequency-time conversion.This investigation explores the influence of various parameters-such as magnetic susceptibility,time constants,and flight altitude-on the superparamagnetic effect by examining the response characteristics of typical targets.Findings indicate that in its late stages,the superparamagnetic effect can induce a relative anomaly of up to 300%.There is a positive correlation between magnetic susceptibility and the strength of the superparamagnetic effect.The influence of the time constant's upper and lower limits on the superparamagnetic effect is minimal;however,the range between these limits significantly affects the effect,showing a negative correlation with its intensity.Higher flight altitudes weaken the superparamagnetic signal.The impact is most pronounced when superparamagnetic minerals are shallowly buried,effectively shielding the underlying geology with the characteristics of a good conductivity anomaly,but this effect diminishes with greater depth.The insights from this study provide a theoretical framework for a deeper understanding of the superparamagnetic effect in transient electromagnetic signals and for more accurate interpretations of subterranean geological and electrical structures.

Geostatistical seismic inversion and 3D modelling of metric flow units,porosity and permeability in Brazilian presalt reservoir

Flow units(FU)rock typing is a common technique for characterizing reservoir flow behavior,producing reliable porosity and permeability estimation even in complex geological settings.However,the lateral extrapolation of FU away from the well into the whole reservoir grid is commonly a difficult task and using the seismic data as constraints is rarely a subject of study.This paper proposes a workflow to generate numerous possible 3D volumes of flow units,porosity and permeability below the seismic resolution limit,respecting the available seismic data at larger scales.The methodology is used in the Mero Field,a Brazilian presalt carbonate reservoir located in the Santos Basin,who presents a complex and heterogenic geological setting with different sedimentological processes and diagenetic history.We generated metric flow units using the conventional core analysis and transposed to the well log data.Then,given a Markov chain Monte Carlo algorithm,the seismic data and the well log statistics,we simulated acoustic impedance,decametric flow units(DFU),metric flow units(MFU),porosity and permeability volumes in the metric scale.The aim is to estimate a minimum amount of MFU able to calculate realistic scenarios porosity and permeability scenarios,without losing the seismic lateral control.In other words,every porosity and permeability volume simulated produces a synthetic seismic that match the real seismic of the area,even in the metric scale.The achieved 3D results represent a high-resolution fluid flow reservoir modelling considering the lateral control of the seismic during the process and can be directly incorporated in the dynamic characterization workflow.

Cellular interplay to 3D in vitro microphysiological disease model:cell patterning microbiota-gut-brain axis

The microbiota-gut-brain axis(MGBA)has emerged as a key prospect in the bidirectional communication between two major organ systems:the brain and the gut.Homeostasis between the two organ systems allows the body to function without disease,whereas dysbiosis has long-standing evidence of etiopathological conditions.The most common communication paths are the microbial release of metabolites,soluble neurotransmitters,and immune cells.However,each pathway is intertwined with a complex one.With the emergence of in vitro models and the popularity of three-dimensional(3D)cultures and Transwells,engineering has become easier for the scientific understanding of neurodegenerative diseases.This paper briefly retraces the possible communication pathways between the gut microbiome and the brain.It further elaborates on three major diseases:autism spectrum disorder,Parkinson’s disease,and Alzheimer’s disease,which are prevalent in children and the elderly.These diseases also decrease patients’quality of life.Hence,understanding them more deeply with respect to current advances in in vitro modeling is crucial for understanding the diseases.Remodeling of MGBA in the laboratory uses many molecular technologies and biomaterial advances.Spheroids and organoids provide a more realistic picture of the cell and tissue structure than monolayers.Combining them with the Transwell system offers the advantage of compartmentalizing the two systems(apical and basal)while allowing physical and chemical cues between them.Cutting-edge technologies,such as bioprinting and microfluidic chips,might be the future of in vitro modeling,as they provide dynamicity.

Three-dimensional Modeling of Ore-forming Elements and Mineralization Prognosis for the Yechangping Mo Deposit,Henan Province,China

Three-dimensional geochemical modeling of ore-forming elements is crucial for predicting deep mineralization.This approach provides key information for the quantitative prediction of deep mineral localization,three-dimensional fine interpolation,analysis of spatial distribution patterns,and extraction of quantitative mineral-seeking markers.The Yechangping molybdenum(Mo)deposit is a significant and extensive porphyry-skarn deposit in the East Qinling-Dabie Mo polymetallic metallogenic belt at the southern margin of the North China Block.Abundant borehole data on oreforming elements underpin deep geochemical predictions.The methodology includes the following steps:(1)Threedimensional geological modeling of the deposit was established.(2)Correlation,cluster,and factor analyses post delineation of mineralization stages and determination of mineral generation sequence to identify(Cu,Pb,Zn,Ag)and(Mo,W,mfe)assemblages.(3)A three-dimensional geochemical block model was constructed for Mo,W,mfe,Cu,Zn,Pb,and Ag using the ordinary kriging method,and the variational function was developed.(4)Spatial distribution and enrichment characteristics analysis of ore-forming elements are performed to extract geological information,employing the variogram and w(Cu+Pb+Zn+Ag)/w(Mo+W)as predictive indicators.(5)Identifying the western,northwestern,and southwestern areas of the mine with limited mineralization potential,contrasted by the northeastern and southeastern areas favorable for mineral exploration.

Cavity 3D modeling and correlative techniques based on cavity monitoring

According to the mining method for Dongguashan Copper Mine and Tongkeng Mine in China, and with the help of the cavity monitoring system（CMS） and mining software Surpac, the 3D cavity models were established exactly. A series of correlative techniques for calculating stope over-excavation and under-excavation, stope dilution and ore loss rates, and the blasting design of the pillar with complicated irregular boundaries were developed. These techniques were applied in Dongguashan Copper Mine and Tongkeng Mine successfully. Using these techniques, the dilution rates of stopes 52-2^#, 52-6^#, 52-8^#and 52-10^# of Dongguashan Copper Mine are calculated to be 2.12%, 8.46%, 12-67% and 10.68%, respectively, and the ore loss rates of stopes 52-6^# and 5-8^# are 4.41% and 3.70%, severally. Furthermore, according to the design accomplished by the technique for a pillar of Tongkeng Mine with irregular boundary, the volume, total length of boreholes and the dynamite quantity of the pillar are computed to be 1.2 ×10^4 m^3, 2.98 km and 10.97 t, correspondingly.

The parallel 3D magnetotelluric forward modeling algorithm

The workload of the 3D magnetotelluric forward modeling algorithm is so large that the traditional serial algorithm costs an extremely large compute time. However, the 3D forward modeling algorithm can process the data in the frequency domain, which is very suitable for parallel computation. With the advantage of MPI and based on an analysis of the flow of the 3D magnetotelluric serial forward algorithm, we suggest the idea of parallel computation and apply it. Three theoretical models are tested and the execution efficiency is compared in different situations. The results indicate that the parallel 3D forward modeling computation is correct and the efficiency is greatly improved. This method is suitable for large size geophysical computations.

Building Information Modeling-Based Secondary Development System for 3D Modeling of Underground Pipelines

Underground pipeline networks constitute a major component of urban infrastructure,and thus,it is imperative to have an efficient mechanism to manage them.This study introduces a secondary development system to efficiently model underground pipeline networks,using the building information modeling(BIM)-based software Revit.The system comprises separate pipe point and tubulation models.Using a Revit application programming interface(API),the spatial position and attribute data of the pipe points are extracted from a pipeline database,and the corresponding tubulation data are extracted from a tubulation database.Using the Family class in Revit API,the cluster in the self-built library of pipe point is inserted into the spatial location and the attribute data is added;in the same way,all pipeline instances in the pipeline system are created.The extension and localization of the model accelerated the modeling speed.The system was then used in a real construction project.The expansion of the model database and rapid modeling made the application of BIM technology in three-dimensional visualization of underground pipeline networks more convenient.Furthermore,it has applications in pipeline engineering construction and management.

Research on 3D Distribution of Meandering River Sand Body Using Sedimentary Facies Method and 3D Geological Modeling

Sedimentary facies study is an important method in describing the property and distribution of reservoir. 3D geological modeling is a powerful tool in 3D characterization of geological bodies. By combining the sedimentary facies study with 3D geological modeling to generate 3D sedimentary facies model, the 3D geometry and distribution feature of sand bodies can be more accurately characterized, particularly in 3D view. In Liuchu oilfield of Jizhong depression, the Ed2IV formation was recognized as meandering river deposition facies and five sedimentary facies were identified, which include point bar sand, levee, channel margin, abandoned channel and floodplain. All the 24 sand body facies in Ed2IV were mapped and the 3D sedimentary facies model established based on 2D facies maps. The result shows that the 3D sedimentary facies model is well matched for the research result of sedimentary facies. Being an extension of traditional sedimentary study, the 3D sedimentary facies model can be used to describe the 3D geometry and distribution orders of a single sand body more reliably and more accurately.

An Approach to Computer Modeling of Geological Faults in 3D and an Application

3D geological modeling, one of the most important applications in geosciences of 3D GIS, forms the basis and is a prerequisite for visualized representation and analysis of 3D geological data. Computer modeling of geological faults in 3D is currently a topical research area. Structural modeling techniques of complex geological entities contain- ing reverse faults are discussed and a series of approaches are proposed. The geological concepts involved in computer modeling and visualization of geological fault in 3D are explained, the type of data of geological faults based on geo- logical exploration is analyzed, and a normative database format for geological faults is designed. Two kinds of model- ing approaches for faults are compared: a modeling technique of faults based on stratum recovery and a modeling tech- nique of faults based on interpolation in subareas. A novel approach, called the Unified Modeling Technique for stratum and fault, is presented to solve the puzzling problems of reverse faults, syn-sedimentary faults and faults terminated within geological models. A case study of a fault model of bed rock in the Beijing Olympic Green District is presented in order to show the practical result of this method. The principle and the process of computer modeling of geological faults in 3D are discussed and a series of applied technical proposals established. It strengthens our profound compre- hension of geological phenomena and the modeling approach, and establishes the basic techniques of 3D geological modeling for practical applications in the field of geosciences.

Integrating deep learning and logging data analytics for lithofacies classification and 3D modeling of tight sandstone reservoirs

The lithofacies classification is essential for oil and gas reservoir exploration and development.The traditional method of lithofacies classification is based on"core calibration logging"and the experience of geologists.This approach has strong subjectivity,low efficiency,and high uncertainty.This uncertainty may be one of the key factors affecting the results of 3 D modeling of tight sandstone reservoirs.In recent years,deep learning,which is a cutting-edge artificial intelligence technology,has attracted attention from various fields.However,the study of deep-learning techniques in the field of lithofacies classification has not been sufficient.Therefore,this paper proposes a novel hybrid deep-learning model based on the efficient data feature-extraction ability of convolutional neural networks(CNN)and the excellent ability to describe time-dependent features of long short-term memory networks(LSTM)to conduct lithological facies-classification experiments.The results of a series of experiments show that the hybrid CNN-LSTM model had an average accuracy of 87.3%and the best classification effect compared to the CNN,LSTM or the three commonly used machine learning models(Support vector machine,random forest,and gradient boosting decision tree).In addition,the borderline synthetic minority oversampling technique(BSMOTE)is introduced to address the class-imbalance issue of raw data.The results show that processed data balance can significantly improve the accuracy of lithofacies classification.Beside that,based on the fine lithofacies constraints,the sequential indicator simulation method is used to establish a three-dimensional lithofacies model,which completes the fine description of the spatial distribution of tight sandstone reservoirs in the study area.According to this comprehensive analysis,the proposed CNN-LSTM model,which eliminates class imbalance,can be effectively applied to lithofacies classification,and is expected to improve the reality of the geological model for the tight sandstone reservoirs.

Alternative 3D Modeling Approaches Based on Complex Multi-Source Geological Data Interpretation

Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline(NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.

A Web-Based Approach for the Efficient Management of Massive Multi-source 3D Models

Effectively managing extensive,multi-source,and multi-level real-scene 3D models for responsive retrieval scheduling and rapid visualization in the Web environment is a significant challenge in the current development of real-scene 3D applications in China.In this paper,we address this challenge by reorganizing spatial and temporal information into a 3D geospatial grid.It introduces the Global 3D Geocoding System(G_(3)DGS),leveraging neighborhood similarity and uniqueness for efficient storage,retrieval,updating,and scheduling of these models.A combination of G_(3)DGS and non-relational databases is implemented,enhancing data storage scalability and flexibility.Additionally,a model detail management scheduling strategy(TLOD)based on G_(3)DGS and an importance factor T is designed.Compared with mainstream commercial and open-source platforms,this method significantly enhances the loadable capacity of massive multi-source real-scene 3D models in the Web environment by 33%,improves browsing efficiency by 48%,and accelerates invocation speed by 40%.

Advancing Wound Filling Extraction on 3D Faces:An Auto-Segmentation and Wound Face Regeneration Approach

Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications.In this paper,we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network.Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions.To achieve accurate segmentation,we conducted thorough experiments and selected a high-performing model from the trainedmodels.The selectedmodel demonstrates exceptional segmentation performance for complex 3D facial wounds.Furthermore,based on the segmentation model,we propose an improved approach for extracting 3D facial wound fillers and compare it to the results of the previous study.Our method achieved a remarkable accuracy of 0.9999993% on the test suite,surpassing the performance of the previous method.From this result,we use 3D printing technology to illustrate the shape of the wound filling.The outcomes of this study have significant implications for physicians involved in preoperative planning and intervention design.By automating facial wound segmentation and improving the accuracy ofwound-filling extraction,our approach can assist in carefully assessing and optimizing interventions,leading to enhanced patient outcomes.Additionally,it contributes to advancing facial reconstruction techniques by utilizing machine learning and 3D bioprinting for printing skin tissue implants.Our source code is available at https://github.com/SIMOGroup/WoundFilling3D.

Mesh representation matters:investigating the influence of different mesh features on perceptual and spatial fidelity of deep 3D morphable models

Background Deep 3D morphable models(deep 3DMMs)play an essential role in computer vision.They are used in facial synthesis,compression,reconstruction and animation,avatar creation,virtual try-on,facial recognition systems and medical imaging.These applications require high spatial and perceptual quality of synthesised meshes.Despite their significance,these models have not been compared with different mesh representations and evaluated jointly with point-wise distance and perceptual metrics.Methods We compare the influence of different mesh representation features to various deep 3DMMs on spatial and perceptual fidelity of the reconstructed meshes.This paper proves the hypothesis that building deep 3DMMs from meshes represented with global representations leads to lower spatial reconstruction error measured with L_(1) and L_(2) norm metrics and underperforms on perceptual metrics.In contrast,using differential mesh representations which describe differential surface properties yields lower perceptual FMPD and DAME and higher spatial fidelity error.The influence of mesh feature normalisation and standardisation is also compared and analysed from perceptual and spatial fidelity perspectives.Results The results presented in this paper provide guidance in selecting mesh representations to build deep 3DMMs accordingly to spatial and perceptual quality objectives and propose combinations of mesh representations and deep 3DMMs which improve either perceptual or spatial fidelity of existing methods.

On numerical modeling of low-head direct chill ingot caster for magnesium alloy AZ31

A comprehensive 3D turbulent CFD study has been carried out to simulate a Low-Head(LH)vertical Direct Chill(DC)rolling ingot caster for the common magnesium alloy AZ31.The model used in this study takes into account the coupled laminar/turbulent melt flow and solidification aspects of the process and is based on the control-volume finite-difference approach.Following the aluminum/magnesium DC casting industrial practices,the LH mold is taken as 30 mm with a hot top of 60 mm.The previously verified in-house code has been modified to model the present casting process.Important quantitative results are obtained for four casting speeds,for three inlet melt pouring temperatures(superheats)and for three metal-mold contact heat transfer coefficients for the steady state operational phase of the caster.The variable cooling water temperatures reported by the industry are considered for the primary and secondary cooling zones during the simulations.Specifically,the temperature and velocity fields,sump depth and sump profiles,mushy region thickness,solid shell thickness at the exit of the mold and axial temperature profiles at the center and at three strategic locations at the surface of the slab are presented and discussed.

3D modeling of geological anomalies based on segmentation of multiattribute fusion

3D modeling of geological bodies based on 3D seismic data is used to define the shape and volume of the bodies, which then can be directly applied to reservoir prediction, reserve estimation, and exploration. However, multiattributes are not effectively used in 3D modeling. To solve this problem, we propose a novel method for building of 3D model of geological anomalies based on the segmentation of multiattribute fusion. First, we divide the seismic attributes into edge- and region-based seismic attributes. Then, the segmentation model incorporating the edge- and region-based models is constructed within the levelset- based framework. Finally, the marching cubes algorithm is adopted to extract the zero level set based on the segmentation results and build the 3D model of the geological anomaly. Combining the edge-and region-based attributes to build the segmentation model, we satisfy the independence requirement and avoid the problem of insufficient data of single seismic attribute in capturing the boundaries of geological anomalies. We apply the proposed method to seismic data from the Sichuan Basin in southwestern China and obtain 3D models of caves and channels. Compared with 3D models obtained based on single seismic attributes, the results are better agreement with reality.

Numerical three-dimensional modeling of earthen dam piping failure

A physically-based numerical three-dimensional earthen dam piping failure model is developed for homogeneous and zoned soil dams.This model is an erosion model,coupled with force/moment equilibrium analyses.Orifice flow and two-dimensional(2D)shallow water equations(SWE)are solved to simulate dam break flows at different breaching stages.Erosion rates of different soils with different construction compaction efforts are calculated using corresponding erosion formulae.The dam's real shape,soil properties,and surrounding area are programmed.Large outer 2D-SWE grids are used to control upstream and downstream hydraulic conditions and control the boundary conditions of orifice flow,and inner 2D-SWE flow is used to scour soil and perform force/moment equilibrium analyses.This model is validated using the European Commission IMPACT(Investigation of Extreme Flood Processes and Uncertainty)Test#5 in Norway,Teton Dam failure in Idaho,USA,and Quail Creek Dike failure in Utah,USA.All calculated peak outflows are within 10%errors of observed values.Simulation results show that,for a V-shaped dam like Teton Dam,a piping breach location at the abutment tends to result in a smaller peak breach outflow than the piping breach location at the dam's center;and if Teton Dam had broken from its center for internal erosion,a peak outflow of 117851 m'/s,which is 81%larger than the peak outflow of 65120 m3/s released from its right abutment,would have been released from Teton Dam.A lower piping inlet elevation tends to cause a faster/earlier piping breach than a higher piping inlet elevation.