Traumatic brain injury causes permanent cell death and can lead to long-term cognitive dysfunction,with no available treatments to repair the damaged brain tissue.Methods to track and understand traumatic brain injury...Traumatic brain injury causes permanent cell death and can lead to long-term cognitive dysfunction,with no available treatments to repair the damaged brain tissue.Methods to track and understand traumatic brain injury in humans are severely limited by the inaccessibility of living brain tissue,creating a need for in vitro model systems to study cellular mechanisms of degeneration and regeneration following injury.Here we describe methods to establish a 3D human brain tissue model,consisting of a silk-collagen composite scaffold seeded with human neurons,astrocytes,and microglia,to study neuro-regeneration after traumatic brain injury.Step-by-step fabrication,injury,and analytical assessments of the 3D“triculture”system are described.Using this tissue model system,we demonstrate that glial cells promote regeneration of neuronal networks within the injury site over several weeks post-injury.Further,we found that regenerating networks in the 3D triculture tissues did not secrete early markers of neurodegenerative disease,but displayed signs of excitatory/inhibitory imbalance,suggesting that pro-regenerative treatments for traumatic brain injury in the future may need to direct cell differentiation to promote proper function.The mechanical stability of this model system enables physiologically relevant impact injury and long-term culture capability,while its modular design enables modification of cell contents,extracellular matrix composition,and scaffold properties.This adaptability could allow the integration of patient-derived cells and genetic modifications to bridge research and clinical applications focused on personalized targeted therapies.This in vitro system provides a valuable platform for accelerating therapeutic advancements in traumatic brain injury and neurodegenerative disorders,ultimately improving patient outcomes.展开更多
Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frame...Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.展开更多
This study introduces a novel method for reconstructing the 3D model of aluminum foam using cross-sectional sequence images.Combining precision milling and image acquisition,high-qual-ity cross-sectional images are ob...This study introduces a novel method for reconstructing the 3D model of aluminum foam using cross-sectional sequence images.Combining precision milling and image acquisition,high-qual-ity cross-sectional images are obtained.Pore structures are segmented by the U-shaped network(U-Net)neural network integrated with the Canny edge detection operator,ensuring accurate pore delineation and edge extraction.The trained U-Net achieves 98.55%accuracy.The 2D data are superimposed and processed into 3D point clouds,enabling reconstruction of the pore structure and aluminum skeleton.Analysis of pore 01 shows the cross-sectional area initially increases,and then decreases with milling depth,with a uniform point distribution of 40 per layer.The reconstructed model exhibits a porosity of 77.5%,with section overlap rates between the 2D pore segmentation and the reconstructed model exceeding 96%,confirming high fidelity.Equivalent sphere diameters decrease with size,averaging 1.95 mm.Compression simulations reveal that the stress-strain curve of the 3D reconstruction model of aluminum foam exhibits fluctuations,and the stresses in the reconstruction model concentrate on thin cell walls,leading to localized deformations.This method accurately restores the aluminum foam’s complex internal structure,improving reconstruction preci-sion and simulation reliability.The approach offers a cost-efficient,high-precision technique for optimizing material performance in engineering applications.展开更多
In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integr...In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.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 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 ...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.展开更多
Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering.Rock mass structure characterization is the basic work of rock mechanics research and the important con...Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering.Rock mass structure characterization is the basic work of rock mechanics research and the important content of numerical simulation.A new 3-dimensional rough discrete fracture network(RDFN3D)model and its modeling method based on the Weierstrass-Mandelbrot(W-M)function were presented in this paper.The RDFN3D model,which improves and unifies the modelling methods for the complex structural planes,has been realized.The influence of fractal dimension,amplitude,and surface precision on the modeling parameters of RDFN3D was discussed.The reasonable W-M parameters suitable for the roughness coefficient of JRC were proposed,and the relationship between the mathematical model and the joint characterization was established.The RDFN3D together with the smooth 3-dimensional discrete fracture network(DFN3D)models were successfully exported to the drawing exchange format,which will provide a wide application in numerous numerical simulation codes including both the continuous and discontinuous methods.The numerical models were discussed using the COMSOL Multiphysics code and the 3-dimensional particle flow code,respectively.The reliability of the RDFN3D model was preliminarily discussed and analyzed.The roughness and spatial connectivity of the fracture networks have a dominant effect on the fluid flow patterns.The research results can provide a new geological model and analysis model for numerical simulation and engineering analysis of jointed rock mass.展开更多
The method of building 3D model was discussed at first.Aiming at the feature of mine vacant place,a method to build the 3D vacant place model based on multi TIN(triangular irregular network)was put forward,and the dat...The method of building 3D model was discussed at first.Aiming at the feature of mine vacant place,a method to build the 3D vacant place model based on multi TIN(triangular irregular network)was put forward,and the data structure and visualization of vacant place were discussed.Then some crucial technologies of realizing function in 3D-GIS were proposed.In addition,the software about special 3D mapping and assaying was introduced.展开更多
With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming a...With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.展开更多
In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technolog...In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technology and BIM(Building Information Modeling)model was discussed.Focused on the efficient acquisition of building geometric information using the fast-developing 3D point cloud technology,an improved deep learning-based 3D point cloud recognition method was proposed.The method optimised the network structure based on RandLA-Net to adapt to the large-scale point cloud processing requirements,while the semantic and instance features of the point cloud were integrated to significantly improve the recognition accuracy and provide a precise basis for BIM model remodeling.In addition,a visual BIM model generation system was developed,which systematically transformed the point cloud recognition results into BIM component parameters,automatically constructed BIM models,and promoted the open sharing and secondary development of models.The research results not only effectively promote the automation process of converting 3D point cloud data to refined BIM models,but also provide important technical support for promoting building informatisation and accelerating the construction of smart cities,showing a wide range of application potential and practical value.展开更多
Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock propert...Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock properties.Given the multiscale characteristics of rock pore structures,direct three-dimensional imaging at sub-micrometer and nanometer scales is typically infeasible.This study introduces a method for reconstructing porous media using multidimensional data,which combines one-dimensional pore structure parameters with two-dimensional images to reconstruct three-dimensional models.The pore network model(PNM)is stochastically reconstructed using one-dimensional parameters,and a generative adversarial network(GAN)is utilized to equip the PNM with pore morphologies derived from two-dimensional images.The digital rocks generated by this method possess excellent controllability.Using Berea sandstone and Grosmont carbonate samples,we performed digital rock reconstructions based on PNM extracted by the maximum ball algorithm and compared them with stochastically reconstructed PNM.Pore structure parameters,permeability,and formation factors were calculated.The results show that the generated samples exhibit good consistency with real samples in terms of pore morphology,pore structure,and physical properties.Furthermore,our method effectively supplements the micropores not captured in CT images,demonstrating its potential in multiscale carbonate samples.Thus,the proposed reconstruction method is promising for advancing porous media property research.展开更多
In this paper,3D-GIS reconstruction and interpolation approach,additional virtual borehole technology and BP network technology are used to explore the concealed ore body.The virtual borehole has same function as real...In this paper,3D-GIS reconstruction and interpolation approach,additional virtual borehole technology and BP network technology are used to explore the concealed ore body.The virtual borehole has same function as reality borehole due to the multi-information check and validation in3D geological body models,and the BP network technology is used to calculate the maximum of orebody depth with copper grade of borehole samples.展开更多
Vision-based technologies have been extensively applied for on-street parking space sensing,aiming at providing timely and accurate information for drivers and improving daily travel convenience.However,it faces great...Vision-based technologies have been extensively applied for on-street parking space sensing,aiming at providing timely and accurate information for drivers and improving daily travel convenience.However,it faces great challenges as a partial visualization regularly occurs owing to occlusion from static or dynamic objects or a limited perspective of camera.This paper presents an imagery-based framework to infer parking space status by generating 3D bounding box of the vehicle.A specially designed convolutional neural network based on ResNet and feature pyramid network is proposed to overcome challenges from partial visualization and occlusion.It predicts 3D box candidates on multi-scale feature maps with five different 3D anchors,which generated by clustering diverse scales of ground truth box according to different vehicle templates in the source data set.Subsequently,vehicle distribution map is constructed jointly from the coordinates of vehicle box and artificially segmented parking spaces,where the normative degree of parked vehicle is calculated by computing the intersection over union between vehicle’s box and parking space edge.In space status inference,to further eliminate mutual vehicle interference,three adjacent spaces are combined into one unit and then a multinomial logistic regression model is trained to refine the status of the unit.Experiments on KITTI benchmark and Shanghai road show that the proposed method outperforms most monocular approaches in 3D box regression and achieves satisfactory accuracy in space status inference.展开更多
The quality assessment and prediction becomes one of the most critical requirements for improving reliability, efficiency and safety of laser welding. Accurate and efficient model to perform non-destructive quality es...The quality assessment and prediction becomes one of the most critical requirements for improving reliability, efficiency and safety of laser welding. Accurate and efficient model to perform non-destructive quality estimation is an essential part of this assessment. This paper presents a structured and comprehensive approach developed to design an effective artificial neural network based model for weld bead geometry prediction and control in laser welding of galvanized steel in butt joint configurations. The proposed approach examines laser welding parameters and conditions known to have an influence on geometric characteristics of the welds and builds a weld quality prediction model step by step. The modelling procedure begins by examining, through structured experimental investigations and exhaustive 3D modelling and simulation efforts, the direct and the interaction effects of laser welding parameters such as laser power, welding speed, fibre diameter and gap, on the weld bead geometry (i.e. depth of penetration and bead width). Using these results and various statistical tools, various neural network based prediction models are developed and evaluated. The results demonstrate that the proposed approach can effectively lead to a consistent model able to accurately and reliably provide an appropriate prediction of weld bead geometry under variable welding conditions.展开更多
Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an eff...Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an efficient method to solve this problem.Because of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development phase.Methods In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single image.The method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial network.First,a 3D cloud shape is mapped into a unique hidden space using the proposed autoencoder.Then,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered images.To train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus models.These cumulus clouds were rendered under different lighting parameters.Results The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing approaches.Furthermore,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction model.Conclusion The proposed autoencoder network learns the latent space of 3D cumulus cloud shapes.The presented reconstruction architecture models a cloud from a single image.Experiments demonstrated the effectiveness of the two models.展开更多
Based on a control grid network and in combination with a remote total station and digital camera,the distribution of steel nodes and deflection curve of a steel grid structure can be obtained easily.The measurement r...Based on a control grid network and in combination with a remote total station and digital camera,the distribution of steel nodes and deflection curve of a steel grid structure can be obtained easily.The measurement result shows that this method is effective and utilitarian.展开更多
Accurate and real-time monitoring true leaf area index(LAI)is an essential for assessing crop growth status and predicting yields.Conventional LAI inversion approaches have been constrained by insufficient data repres...Accurate and real-time monitoring true leaf area index(LAI)is an essential for assessing crop growth status and predicting yields.Conventional LAI inversion approaches have been constrained by insufficient data represen-tativeness and environmental variability,particularly when applied across interannual variations and different phenological stages.This study presented a novel methodology integrating three-dimensional radiative transfer modeling(3D RTM)with knowledge-guided deep learning to address these limitations.We developed a knowledge-guided convolutional neural network(KGCNN)architecture incorporating 3D canopy structural physics,enhanced through transfer learning(TL)techniques for cross-temporal adaptation.The KGCNN model was initially pre-trained on synthetic datasets generated by the large-scale remote sensing scattering model(LESS),followed by domain-specific fine-tuning using 2021 field measurements,and culminating in cross-year validation with 2022-2023 datasets.Our results demonstrated significant improvements over conventional ap-proaches,with the 3D RTM-based KGCNN achieving superior performance compared to 1D RTM implementations(PROSAIL+CNN+TL).Specially,for the 2022 dataset,the overall R^(2) increased by 0.27 and RMSE decreased by 2.46;for the 2023 dataset,the overall RMSE decreased by 1.62,compared to the PROSAIL+TL method.Our method(3D RTM+KGCNN+TL)delivered superior LAI retrieval accuracy on the two-year datasets compared to LSTM+TL,RNN+TL,and 3D RTM+RF models.This study also introduced an effective 3D scene modeling strategy that integrates scenarios representing the measured data range with additional synthetic scenes gener-ated through random combinations of structural parameters.By incorporating detailed 3D crop structural in-formation into the KGCNN network and fine-tuning the model with measured data,the approach significantly enhanced the model's adaptability to varying data distributions across different years and growth stages.This approach thus improved both the accuracy and stability of true LAI retrieval.展开更多
基金supported by funding from the U.S.Department of Defense,Nos.W911NF-23-1-0276,W81XWH2211065the NIH,No.P41EB027062(all to DLK).
文摘Traumatic brain injury causes permanent cell death and can lead to long-term cognitive dysfunction,with no available treatments to repair the damaged brain tissue.Methods to track and understand traumatic brain injury in humans are severely limited by the inaccessibility of living brain tissue,creating a need for in vitro model systems to study cellular mechanisms of degeneration and regeneration following injury.Here we describe methods to establish a 3D human brain tissue model,consisting of a silk-collagen composite scaffold seeded with human neurons,astrocytes,and microglia,to study neuro-regeneration after traumatic brain injury.Step-by-step fabrication,injury,and analytical assessments of the 3D“triculture”system are described.Using this tissue model system,we demonstrate that glial cells promote regeneration of neuronal networks within the injury site over several weeks post-injury.Further,we found that regenerating networks in the 3D triculture tissues did not secrete early markers of neurodegenerative disease,but displayed signs of excitatory/inhibitory imbalance,suggesting that pro-regenerative treatments for traumatic brain injury in the future may need to direct cell differentiation to promote proper function.The mechanical stability of this model system enables physiologically relevant impact injury and long-term culture capability,while its modular design enables modification of cell contents,extracellular matrix composition,and scaffold properties.This adaptability could allow the integration of patient-derived cells and genetic modifications to bridge research and clinical applications focused on personalized targeted therapies.This in vitro system provides a valuable platform for accelerating therapeutic advancements in traumatic brain injury and neurodegenerative disorders,ultimately improving patient outcomes.
基金supported by the National Natural Science Foundation of China(Grant No.72161034).
文摘Human motion modeling is a core technology in computer animation,game development,and humancomputer interaction.In particular,generating natural and coherent in-between motion using only the initial and terminal frames remains a fundamental yet unresolved challenge.Existing methods typically rely on dense keyframe inputs or complex prior structures,making it difficult to balance motion quality and plausibility under conditions such as sparse constraints,long-term dependencies,and diverse motion styles.To address this,we propose a motion generation framework based on a frequency-domain diffusion model,which aims to better model complex motion distributions and enhance generation stability under sparse conditions.Our method maps motion sequences to the frequency domain via the Discrete Cosine Transform(DCT),enabling more effective modeling of low-frequency motion structures while suppressing high-frequency noise.A denoising network based on self-attention is introduced to capture long-range temporal dependencies and improve global structural awareness.Additionally,a multi-objective loss function is employed to jointly optimize motion smoothness,pose diversity,and anatomical consistency,enhancing the realism and physical plausibility of the generated sequences.Comparative experiments on the Human3.6M and LaFAN1 datasets demonstrate that our method outperforms state-of-the-art approaches across multiple performance metrics,showing stronger capabilities in generating intermediate motion frames.This research offers a new perspective and methodology for human motion generation and holds promise for applications in character animation,game development,and virtual interaction.
基金supported by the Key Research and DevelopmentPlan in Shanxi Province of China(No.201803D421045)the Natural Science Foundation of Shanxi Province(No.2021-0302-123104)。
文摘This study introduces a novel method for reconstructing the 3D model of aluminum foam using cross-sectional sequence images.Combining precision milling and image acquisition,high-qual-ity cross-sectional images are obtained.Pore structures are segmented by the U-shaped network(U-Net)neural network integrated with the Canny edge detection operator,ensuring accurate pore delineation and edge extraction.The trained U-Net achieves 98.55%accuracy.The 2D data are superimposed and processed into 3D point clouds,enabling reconstruction of the pore structure and aluminum skeleton.Analysis of pore 01 shows the cross-sectional area initially increases,and then decreases with milling depth,with a uniform point distribution of 40 per layer.The reconstructed model exhibits a porosity of 77.5%,with section overlap rates between the 2D pore segmentation and the reconstructed model exceeding 96%,confirming high fidelity.Equivalent sphere diameters decrease with size,averaging 1.95 mm.Compression simulations reveal that the stress-strain curve of the 3D reconstruction model of aluminum foam exhibits fluctuations,and the stresses in the reconstruction model concentrate on thin cell walls,leading to localized deformations.This method accurately restores the aluminum foam’s complex internal structure,improving reconstruction preci-sion and simulation reliability.The approach offers a cost-efficient,high-precision technique for optimizing material performance in engineering applications.
基金the management of Sierra Rutile Company for providing the drillhole dataset used in this studythe Japanese Ministry of Education Science and Technology (MEXT) Scholarship for academic funding
文摘In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs, In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades, The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could oroduce optimum block model for mine design.
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.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.300102278402)。
文摘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.
基金This work was financially supported by the National Key R&D Program of China(No.2021YFC2900500)the National Natural Science Foundation of China(Nos.52074020 and 42202306)+2 种基金the Open Fund of State Key Laboratory of Water Resource Protection and Utilization in Coal Mining(No.WPUKFJJ2019-06)the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities)(No.FRF-IDRY-21001)the Natural Science Foundation of Jiangsu Province,China(No.BK20200993).
文摘Structure plane is one of the important factors affecting the stability and failure mode of rock mass engineering.Rock mass structure characterization is the basic work of rock mechanics research and the important content of numerical simulation.A new 3-dimensional rough discrete fracture network(RDFN3D)model and its modeling method based on the Weierstrass-Mandelbrot(W-M)function were presented in this paper.The RDFN3D model,which improves and unifies the modelling methods for the complex structural planes,has been realized.The influence of fractal dimension,amplitude,and surface precision on the modeling parameters of RDFN3D was discussed.The reasonable W-M parameters suitable for the roughness coefficient of JRC were proposed,and the relationship between the mathematical model and the joint characterization was established.The RDFN3D together with the smooth 3-dimensional discrete fracture network(DFN3D)models were successfully exported to the drawing exchange format,which will provide a wide application in numerous numerical simulation codes including both the continuous and discontinuous methods.The numerical models were discussed using the COMSOL Multiphysics code and the 3-dimensional particle flow code,respectively.The reliability of the RDFN3D model was preliminarily discussed and analyzed.The roughness and spatial connectivity of the fracture networks have a dominant effect on the fluid flow patterns.The research results can provide a new geological model and analysis model for numerical simulation and engineering analysis of jointed rock mass.
文摘The method of building 3D model was discussed at first.Aiming at the feature of mine vacant place,a method to build the 3D vacant place model based on multi TIN(triangular irregular network)was put forward,and the data structure and visualization of vacant place were discussed.Then some crucial technologies of realizing function in 3D-GIS were proposed.In addition,the software about special 3D mapping and assaying was introduced.
文摘With the widespread application of deep learning in the field of computer vision,gradually allowing medical image technology to assist doctors in making diagnoses has great practical and research significance.Aiming at the shortcomings of the traditional U-Net model in 3D spatial information extraction,model over-fitting,and low degree of semantic information fusion,an improved medical image segmentation model has been used to achieve more accurate segmentation of medical images.In this model,we make full use of the residual network(ResNet)to solve the over-fitting problem.In order to process and aggregate data at different scales,the inception network is used instead of the traditional convolutional layer,and the dilated convolution is used to increase the receptive field.The conditional random field(CRF)can complete the contour refinement work.Compared with the traditional 3D U-Net network,the segmentation accuracy of the improved liver and tumor images increases by 2.89%and 7.66%,respectively.As a part of the image processing process,the method in this paper not only can be used for medical image segmentation,but also can lay the foundation for subsequent image 3D reconstruction work.
文摘In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technology and BIM(Building Information Modeling)model was discussed.Focused on the efficient acquisition of building geometric information using the fast-developing 3D point cloud technology,an improved deep learning-based 3D point cloud recognition method was proposed.The method optimised the network structure based on RandLA-Net to adapt to the large-scale point cloud processing requirements,while the semantic and instance features of the point cloud were integrated to significantly improve the recognition accuracy and provide a precise basis for BIM model remodeling.In addition,a visual BIM model generation system was developed,which systematically transformed the point cloud recognition results into BIM component parameters,automatically constructed BIM models,and promoted the open sharing and secondary development of models.The research results not only effectively promote the automation process of converting 3D point cloud data to refined BIM models,but also provide important technical support for promoting building informatisation and accelerating the construction of smart cities,showing a wide range of application potential and practical value.
基金supported by the Shandong Provincial Natural Science Foundation(ZR2024MD116)National Natural Science Foundation of China(Grant Nos.42174143,42004098)Technology Innovation Leading Program of Shaanxi(No.2024 ZC-YYDP-27).
文摘Subsurface rocks,as complex porous media,exhibit multiscale pore structures and intricate physical properties.Digital rock physics technology has become increasingly influential in the study of subsurface rock properties.Given the multiscale characteristics of rock pore structures,direct three-dimensional imaging at sub-micrometer and nanometer scales is typically infeasible.This study introduces a method for reconstructing porous media using multidimensional data,which combines one-dimensional pore structure parameters with two-dimensional images to reconstruct three-dimensional models.The pore network model(PNM)is stochastically reconstructed using one-dimensional parameters,and a generative adversarial network(GAN)is utilized to equip the PNM with pore morphologies derived from two-dimensional images.The digital rocks generated by this method possess excellent controllability.Using Berea sandstone and Grosmont carbonate samples,we performed digital rock reconstructions based on PNM extracted by the maximum ball algorithm and compared them with stochastically reconstructed PNM.Pore structure parameters,permeability,and formation factors were calculated.The results show that the generated samples exhibit good consistency with real samples in terms of pore morphology,pore structure,and physical properties.Furthermore,our method effectively supplements the micropores not captured in CT images,demonstrating its potential in multiscale carbonate samples.Thus,the proposed reconstruction method is promising for advancing porous media property research.
文摘In this paper,3D-GIS reconstruction and interpolation approach,additional virtual borehole technology and BP network technology are used to explore the concealed ore body.The virtual borehole has same function as reality borehole due to the multi-information check and validation in3D geological body models,and the BP network technology is used to calculate the maximum of orebody depth with copper grade of borehole samples.
基金This work was supported in part by National Natural Science Foundation of China(No.51805312)in part by Shanghai Sailing Program(No.18YF1409400)+2 种基金in part by Training and Funding Program of Shanghai College young teachers(No.ZZGCD15102)in part by Scientific Research Project of Shanghai University of Engineering Science(No.2016-19)in part by the Shanghai University of Engineering Science Innovation Fund for Graduate Students(No.18KY0613).
文摘Vision-based technologies have been extensively applied for on-street parking space sensing,aiming at providing timely and accurate information for drivers and improving daily travel convenience.However,it faces great challenges as a partial visualization regularly occurs owing to occlusion from static or dynamic objects or a limited perspective of camera.This paper presents an imagery-based framework to infer parking space status by generating 3D bounding box of the vehicle.A specially designed convolutional neural network based on ResNet and feature pyramid network is proposed to overcome challenges from partial visualization and occlusion.It predicts 3D box candidates on multi-scale feature maps with five different 3D anchors,which generated by clustering diverse scales of ground truth box according to different vehicle templates in the source data set.Subsequently,vehicle distribution map is constructed jointly from the coordinates of vehicle box and artificially segmented parking spaces,where the normative degree of parked vehicle is calculated by computing the intersection over union between vehicle’s box and parking space edge.In space status inference,to further eliminate mutual vehicle interference,three adjacent spaces are combined into one unit and then a multinomial logistic regression model is trained to refine the status of the unit.Experiments on KITTI benchmark and Shanghai road show that the proposed method outperforms most monocular approaches in 3D box regression and achieves satisfactory accuracy in space status inference.
文摘The quality assessment and prediction becomes one of the most critical requirements for improving reliability, efficiency and safety of laser welding. Accurate and efficient model to perform non-destructive quality estimation is an essential part of this assessment. This paper presents a structured and comprehensive approach developed to design an effective artificial neural network based model for weld bead geometry prediction and control in laser welding of galvanized steel in butt joint configurations. The proposed approach examines laser welding parameters and conditions known to have an influence on geometric characteristics of the welds and builds a weld quality prediction model step by step. The modelling procedure begins by examining, through structured experimental investigations and exhaustive 3D modelling and simulation efforts, the direct and the interaction effects of laser welding parameters such as laser power, welding speed, fibre diameter and gap, on the weld bead geometry (i.e. depth of penetration and bead width). Using these results and various statistical tools, various neural network based prediction models are developed and evaluated. The results demonstrate that the proposed approach can effectively lead to a consistent model able to accurately and reliably provide an appropriate prediction of weld bead geometry under variable welding conditions.
基金the National Key R&D Program of China(2017YFB1002702).
文摘Background Cumulus clouds are important elements in creating virtual outdoor scenes.Modeling cumulus clouds that have a specific shape is difficult owing to the fluid nature of the cloud.Image-based modeling is an efficient method to solve this problem.Because of the complexity of cloud shapes,the task of modeling the cloud from a single image remains in the development phase.Methods In this study,a deep learning-based method was developed to address the problem of modeling 3D cumulus clouds from a single image.The method employs a three-dimensional autoencoder network that combines the variational autoencoder and the generative adversarial network.First,a 3D cloud shape is mapped into a unique hidden space using the proposed autoencoder.Then,the parameters of the decoder are fixed.A shape reconstruction network is proposed for use instead of the encoder part,and it is trained with rendered images.To train the presented models,we constructed a 3D cumulus dataset that included 2003D cumulus models.These cumulus clouds were rendered under different lighting parameters.Results The qualitative experiments showed that the proposed autoencoder method can learn more structural details of 3D cumulus shapes than existing approaches.Furthermore,some modeling experiments on rendering images demonstrated the effectiveness of the reconstruction model.Conclusion The proposed autoencoder network learns the latent space of 3D cumulus cloud shapes.The presented reconstruction architecture models a cloud from a single image.Experiments demonstrated the effectiveness of the two models.
文摘Based on a control grid network and in combination with a remote total station and digital camera,the distribution of steel nodes and deflection curve of a steel grid structure can be obtained easily.The measurement result shows that this method is effective and utilitarian.
基金supported by the National Key Research and Development Program of China(2021YFD2000102)the Natural Science Foundation of China(42371373)the Special Fund for Construction of Scientific and Technological Innovation Ability of Beijing Academy of Agriculture and Forestry Sciences(KJCX20230434).
文摘Accurate and real-time monitoring true leaf area index(LAI)is an essential for assessing crop growth status and predicting yields.Conventional LAI inversion approaches have been constrained by insufficient data represen-tativeness and environmental variability,particularly when applied across interannual variations and different phenological stages.This study presented a novel methodology integrating three-dimensional radiative transfer modeling(3D RTM)with knowledge-guided deep learning to address these limitations.We developed a knowledge-guided convolutional neural network(KGCNN)architecture incorporating 3D canopy structural physics,enhanced through transfer learning(TL)techniques for cross-temporal adaptation.The KGCNN model was initially pre-trained on synthetic datasets generated by the large-scale remote sensing scattering model(LESS),followed by domain-specific fine-tuning using 2021 field measurements,and culminating in cross-year validation with 2022-2023 datasets.Our results demonstrated significant improvements over conventional ap-proaches,with the 3D RTM-based KGCNN achieving superior performance compared to 1D RTM implementations(PROSAIL+CNN+TL).Specially,for the 2022 dataset,the overall R^(2) increased by 0.27 and RMSE decreased by 2.46;for the 2023 dataset,the overall RMSE decreased by 1.62,compared to the PROSAIL+TL method.Our method(3D RTM+KGCNN+TL)delivered superior LAI retrieval accuracy on the two-year datasets compared to LSTM+TL,RNN+TL,and 3D RTM+RF models.This study also introduced an effective 3D scene modeling strategy that integrates scenarios representing the measured data range with additional synthetic scenes gener-ated through random combinations of structural parameters.By incorporating detailed 3D crop structural in-formation into the KGCNN network and fine-tuning the model with measured data,the approach significantly enhanced the model's adaptability to varying data distributions across different years and growth stages.This approach thus improved both the accuracy and stability of true LAI retrieval.
