This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods.The study focuses on the reconstruction of a 3D n...This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods.The study focuses on the reconstruction of a 3D nose model tailored for applications in healthcare and cosmetic surgery.The approach leverages advanced image processing techniques,3D Morphable Models(3DMM),and deformation techniques to overcome the limita-tions of deep learning models,particularly addressing the interpretability issues commonly encountered in medical applications.The proposed method estimates the 3D coordinates of landmark points using a 3D structure estimation algorithm.Sub-landmarks are extracted through image processing techniques and interpolation.The initial surface is generated using a 3DMM,though its accuracy remains limited.To enhance precision,deformation techniques are applied,utilizing the coordinates of 76 identified landmarks and sub-landmarks.The resulting 3D nose model is constructed based on algorithmic methods and pre-marked landmarks.Evaluation of the 3D model is conducted by comparing landmark distances and shape similarity with expert-determined ground truth on 30 Vietnamese volunteers aged 18 to 47,all of whom were either preparing for or required nasal surgery.Experimental results demonstrate a strong agreement between the reconstructed 3D model and the ground truth.The method achieved a mean landmark distance error of 0.631 mm and a shape error of 1.738 mm,demonstrating its potential for medical applications.展开更多
Recognizing discontinuities within rock masses is a critical aspect of rock engineering.The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected ...Recognizing discontinuities within rock masses is a critical aspect of rock engineering.The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected from rock outcrops.In response,we propose a workflow that balances accuracy and efficiency to extract discontinuities from massive point clouds.The proposed method employs voxel filtering to downsample point clouds,constructs a point cloud topology using K-d trees,utilizes principal component analysis to calculate the point cloud normals,and employs the pointwise clustering(PWC)algorithm to extract discontinuities from rock outcrop point clouds.This method provides information on the location and orientation(dip direction and dip angle)of the discontinuities,and the modified whale optimization algorithm(MWOA)is utilized to identify major discontinuity sets and their average orientations.Performance evaluations based on three real cases demonstrate that the proposed method significantly reduces computational time costs without sacrificing accuracy.In particular,the method yields more reasonable extraction results for discontinuities with certain undulations.The presented approach offers a novel tool for efficiently extracting discontinuities from large-scale point clouds.展开更多
The modeling of crack growth in three-dimensional(3D)space poses significant challenges in rock mechanics due to the complex numerical computation involved in simulating crack propagation and interaction in rock mater...The modeling of crack growth in three-dimensional(3D)space poses significant challenges in rock mechanics due to the complex numerical computation involved in simulating crack propagation and interaction in rock materials.In this study,we present a novel approach that introduces a 3D numerical manifold method(3D-NMM)with a geometric kernel to enhance computational efficiency.Specifically,the maximum tensile stress criterion is adopted as a crack growth criterion to achieve strong discontinuous crack growth,and a local crack tracking algorithm and an angle correction technique are incorporated to address minor limitations of the algorithm in a 3D model.The implementation of the program is carried out in Python,using object-oriented programming in two independent modules:a calculation module and a crack module.Furthermore,we propose feasible improvements to enhance the performance of the algorithm.Finally,we demonstrate the feasibility and effectiveness of the enhanced algorithm in the 3D-NMM using four numerical examples.This study establishes the potential of the 3DNMM,combined with the local tracking algorithm,for accurately modeling 3D crack propagation in brittle rock materials.展开更多
This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discre...This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discrete derivatives and introducing logistics-related constraints.Optional consideration of the rotation of the cargoes was made to further enhance the optimality of the solutions,if possible to be physically implemented.Evaluation metrics were developed for accurate evaluation and enhancement of the algorithm’s ability to efficiently utilize the loading space and provide a high level of dynamic stability.Experimental results demonstrate the extensive robustness of the proposed algorithm to the diversity of cargoes present in Business-to-Consumer environments.This study contributes practical advancements in both cargo loading optimization and automation of the logistics industry,with potential applications in last-mile delivery services,warehousing,and supply chain management.展开更多
Vulnerability assessment is a systematic process to identify security gaps in the design and evaluation of physical protection systems.Adversarial path planning is a widely used method for identifying potential vulner...Vulnerability assessment is a systematic process to identify security gaps in the design and evaluation of physical protection systems.Adversarial path planning is a widely used method for identifying potential vulnerabilities and threats to the security and resilience of critical infrastructures.However,achieving efficient path optimization in complex large-scale three-dimensional(3D)scenes remains a significant challenge for vulnerability assessment.This paper introduces a novel A^(*)-algorithmic framework for 3D security modeling and vulnerability assessment.Within this framework,the 3D facility models were first developed in 3ds Max and then incorporated into Unity for A^(*)heuristic pathfinding.The A^(*)-heuristic pathfinding algorithm was implemented with a geometric probability model to refine the detection and distance fields and achieve a rational approximation of the cost to reach the goal.An admissible heuristic is ensured by incorporating the minimum probability of detection(P_(D)^(min))and diagonal distance to estimate the heuristic function.The 3D A^(*)heuristic search was demonstrated using a hypothetical laboratory facility,where a comparison was also carried out between the A^(*)and Dijkstra algorithms for optimal path identification.Comparative results indicate that the proposed A^(*)-heuristic algorithm effectively identifies the most vulnerable adversarial pathfinding with high efficiency.Finally,the paper discusses hidden phenomena and open issues in efficient 3D pathfinding for security applications.展开更多
This paper proposes a gradient conformal design technique to modify the multi-directional stiffness characteristics of 3D printed chiral metamaterials,using various airfoil shapes.The method ensures the integrity of c...This paper proposes a gradient conformal design technique to modify the multi-directional stiffness characteristics of 3D printed chiral metamaterials,using various airfoil shapes.The method ensures the integrity of chiral cell nodal circles while improving load transmission efficiency and enhancing manufacturing precision for 3D printing applications.A parametric design framework,integrating finite element analysis and optimization modules,is developed to enhance the wing’s multidirectional stiffness.The optimization process demonstrates that the distribution of chiral structural ligaments and nodal circles significantly affects wing deformation.The stiffness gradient optimization results reveal a variation of over 78%in tail stiffness performance between the best and worst parameter combinations.Experimental outcomes suggest that this strategy can develop metamaterials with enhanced deformability,offering a promising approach for designing morphing wings.展开更多
Historical architecture is an important carrier of cultural and historical heritage in a country and region,and its protection and restoration work plays a crucial role in the inheritance of cultural heritage.However,...Historical architecture is an important carrier of cultural and historical heritage in a country and region,and its protection and restoration work plays a crucial role in the inheritance of cultural heritage.However,the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human factors.Therefore,an artificial intelligence repair technology based on three-dimensional(3D)point cloud(PC)reconstruction and generative adversarial networks(GANs)was proposed to improve the precision and efficiency of repair work.First,in-depth research on the principles and algorithms of 3D PC data processing and GANs should be conducted.Second,a digital restoration frameworkwas constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning processes.The experimental results showed that the errors in the restoration of palace buildings,defense walls,pagodas,altars,temples,and mausoleums were 0.17,0.12,0.13,0.11,and 0.09,respectively.The technique can significantly reduce the error while maintaining the high-precision repair effect.This technology with artificial intelligence as the core has excellent accuracy and stability in the digital restoration.It provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.展开更多
This study presents a novel hybrid topology optimization and mold design framework that integrates process fitting,runner system optimization,and structural analysis to significantly enhance the performance of injecti...This study presents a novel hybrid topology optimization and mold design framework that integrates process fitting,runner system optimization,and structural analysis to significantly enhance the performance of injection-molded parts.At its core,the framework employs a greedy algorithm that generates runner systems based on adjacency and shortest path principles,leading to improvements in both mechanical strength and material efficiency.The design optimization is validated through a series of rigorous experimental tests,including three-point bending and torsion tests performed on key-socket frames,ensuring that the optimized designs meet practical performance requirements.A critical innovation of the framework is the development of the Adjacent Element Temperature-Driven Prestress Algorithm(AETDPA),which refines the prediction of mechanical failure and strength fitting.This algorithm has been shown to deliver mesh-independent accuracy,thereby enhancing the reliability of simulation results across various design iterations.The framework’s adaptability is further demonstrated by its ability to adjust optimization methods based on the unique geometry of each part,thus accelerating the overall design process while ensuring struc-tural integrity.In addition to its immediate applications in injection molding,the study explores the potential extension of this framework to metal additive manufacturing,opening new avenues for its use in advanced manufacturing technologies.Numerical simulations,including finite element analysis,support the experimental findings and confirm that the optimized designs provide a balanced combination of strength,durability,and efficiency.Furthermore,the integration challenges with existing injection molding practices are addressed,underscoring the framework’s scalability and industrial relevance.Overall,this hybrid topology optimization framework offers a computationally efficient and robust solution for advanced manufacturing applications,promising significant improvements in design efficiency,cost-effectiveness,and product performance.Future work will focus on further enhancing algorithm robustness and exploring additional applications across diverse manufacturing processes.展开更多
Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised m...Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.展开更多
Any malfunctions of the actuators of the robots have the potential to destroy the robot’s normal motion,and most of the current actuator fault diagnosis methods are difficult to meet the requirements of simplifying t...Any malfunctions of the actuators of the robots have the potential to destroy the robot’s normal motion,and most of the current actuator fault diagnosis methods are difficult to meet the requirements of simplifying the actuator modeling and solving the difficulty of fault data collection.To solve the problem of real-time diagnosis of actuator faults in the 3-PR(P)S parallel robot,the model of 3-PR(P)S parallel robot and data-driven-based method for the fault diagnosis are presented.Firstly,only the input-output relationship of the actuator is considered for modeling actuator faults,reducing the complexity of fault modeling and reducing the time consumption of parameter identification,thereby meeting the requirements of real-time diagnosis.A Simulink model of the electromechanical actuator(EMA)was constructed to analyze actuator faults.Then the short-term analysis method was employed for collecting the sample data of the slider position on the test platform of the EMA system and feature extraction.Training samples for neural networks are obtained.Furthermore,we optimized the Back Propagation(BP)neural network using the Dung Beetle Optimization Algorithm(DBO),which effectively resolved the weights and thresholds of the BP neural network.Compared to BP and Particle Swarm Optimization(PSO)-BP,the DBO-BP has better convergence,convergence rate,and the best-classifying quality.So,the classification for the different actuator faults is obviously improved.Finally,a fault diagnosis system was designed for the actuator of the 3-PR(P)S parallel robot,and the experimental results demonstrate that this system can detect actuator faults within 0.1 seconds.This work also provides the technical support for the fault-tolerant control of the 3-PR(P)S Parallel robot.展开更多
A novel algorithm of 3-D surface image registration is proposed. It makes use of the array information of 3-D points and takes vector/vertex-like features as the basis of the matching. That array information of 3-D po...A novel algorithm of 3-D surface image registration is proposed. It makes use of the array information of 3-D points and takes vector/vertex-like features as the basis of the matching. That array information of 3-D points can be easily obtained when capturing original 3-D images. The iterative least-mean-squared （LMS） algorithm is applied to optimizing adaptively the transformation matrix parameters. These can effectively improve the registration performance and hurry up the matching process. Experimental results show that it can reach a good subjective impression on aligned 3-D images. Although the algorithm focuses primarily on the human head model, it can also be used for other objects with small modifications.展开更多
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 dat...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.展开更多
With respect to the gamma spectrum, the energy resolution improves with increase in energy. The counts of full energy peak change with energy, and this approximately complies with the Gaussian distribution. This study...With respect to the gamma spectrum, the energy resolution improves with increase in energy. The counts of full energy peak change with energy, and this approximately complies with the Gaussian distribution. This study mainly examines a method to deconvolve the LaBr_3:Ce gamma spectrum with a detector response matrix constructing algorithm based on energy resolution calibration.In the algorithm, the full width at half maximum(FWHM)of full energy peak was calculated by the cubic spline interpolation algorithm and calibrated by a square root of a quadratic function that changes with the energy. Additionally, the detector response matrix was constructed to deconvolve the gamma spectrum. Furthermore, an improved SNIP algorithm was proposed to eliminate the background. In the experiment, several independent peaks of ^(152)Eu,^(137)Cs, and ^(60)Co sources were detected by a LaBr_3:Ce scintillator that were selected to calibrate the energy resolution. The Boosted Gold algorithm was applied to deconvolve the gamma spectrum. The results showed that the peak position difference between the experiment and the deconvolution was within ± 2 channels and the relative error of peak area was approximately within 0.96–6.74%. Finally, a ^(133) Ba spectrum was deconvolved to verify the efficiency and accuracy of the algorithm in unfolding the overlapped peaks.展开更多
The bimodulus material is a classical model to describe the elastic behavior of materials with tension-compression asymmetry.Due to the inherently nonlinear properties of bimodular materials,traditional iteration meth...The bimodulus material is a classical model to describe the elastic behavior of materials with tension-compression asymmetry.Due to the inherently nonlinear properties of bimodular materials,traditional iteration methods suffer from low convergence efficiency and poor adaptability for large-scale structures in engineering.In this paper,a novel 3D algorithm is established by complementing the three shear moduli of the constitutive equation in principal stress coordinates.In contrast to the existing 3D shear modulus constructed based on experience,in this paper the shear modulus is derived theoretically through a limit process.Then,a theoretically self-consistent complemented algorithm is established and implemented in ABAQUS via UMAT;its good stability and convergence efficiency are verified by using benchmark examples.Numerical analysis shows that the calculation error for bimodulus structures using the traditional linear elastic theory is large,which is not in line with reality.展开更多
The practical application of 3D inversion of gravity data requires a lot of computation time and storage space.To solve this problem,we present an integrated optimization algorithm with the following components:(1)tar...The practical application of 3D inversion of gravity data requires a lot of computation time and storage space.To solve this problem,we present an integrated optimization algorithm with the following components:(1)targeting high accuracy in the space domain and fast computation in the wavenumber domain,we design a fast 3D forward algorithm with high precision;and(2)taking advantage of the symmetry of the inversion matrix,the main calculation in gravity conjugate gradient inversion is decomposed into two forward calculations,thus optimizing the computational efficiency of 3D gravity inversion.We verify the calculation accuracy and efficiency of the optimization algorithm by testing various grid-number models through numerical simulation experiments.展开更多
The coronavirus 3C-like(3CL)protease,a cysteine protease,plays an important role in viral infection and immune escape.However,there is still a lack of effective tools for determining the cleavage sites of the 3CL prot...The coronavirus 3C-like(3CL)protease,a cysteine protease,plays an important role in viral infection and immune escape.However,there is still a lack of effective tools for determining the cleavage sites of the 3CL protease.This study systematically investigated the diversity of the cleavage sites of the coronavirus 3CL protease on the viral polyprotein,and found that the cleavage motif were highly conserved for viruses in the genera of Alphacoronavirus,Betacoronavirus and Gammacoronavirus.Strong residue preferences were observed at the neighboring positions of the cleavage sites.A random forest(RF)model was built to predict the cleavage sites of the coronavirus 3CL protease based on the representation of residues in cleavage motifs by amino acid indexes,and the model achieved an AUC of 0.96 in cross-validations.The RF model was further tested on an independent test dataset which were composed of cleavage sites on 99 proteins from multiple coronavirus hosts.It achieved an AUC of 0.95 and predicted correctly 80%of the cleavage sites.Then,1,352 human proteins were predicted to be cleaved by the 3CL protease by the RF model.These proteins were enriched in several GO terms related to the cytoskeleton,such as the microtubule,actin and tubulin.Finally,a webserver named 3CLP was built to predict the cleavage sites of the coronavirus 3CL protease based on the RF model.Overall,the study provides an effective tool for identifying cleavage sites of the 3CL protease and provides insights into the molecular mechanism underlying the pathogenicity of coronaviruses.展开更多
文摘This paper presents a novel method for reconstructing a highly accurate 3D nose model of the human from 2D images and pre-marked landmarks based on algorithmic methods.The study focuses on the reconstruction of a 3D nose model tailored for applications in healthcare and cosmetic surgery.The approach leverages advanced image processing techniques,3D Morphable Models(3DMM),and deformation techniques to overcome the limita-tions of deep learning models,particularly addressing the interpretability issues commonly encountered in medical applications.The proposed method estimates the 3D coordinates of landmark points using a 3D structure estimation algorithm.Sub-landmarks are extracted through image processing techniques and interpolation.The initial surface is generated using a 3DMM,though its accuracy remains limited.To enhance precision,deformation techniques are applied,utilizing the coordinates of 76 identified landmarks and sub-landmarks.The resulting 3D nose model is constructed based on algorithmic methods and pre-marked landmarks.Evaluation of the 3D model is conducted by comparing landmark distances and shape similarity with expert-determined ground truth on 30 Vietnamese volunteers aged 18 to 47,all of whom were either preparing for or required nasal surgery.Experimental results demonstrate a strong agreement between the reconstructed 3D model and the ground truth.The method achieved a mean landmark distance error of 0.631 mm and a shape error of 1.738 mm,demonstrating its potential for medical applications.
基金supported by the National Natural Science Foundation of China(Grant No.42407232)the Sichuan Science and Technology Program(Grant No.2024NSFSC0826).
文摘Recognizing discontinuities within rock masses is a critical aspect of rock engineering.The development of remote sensing technologies has significantly enhanced the quality and quantity of the point clouds collected from rock outcrops.In response,we propose a workflow that balances accuracy and efficiency to extract discontinuities from massive point clouds.The proposed method employs voxel filtering to downsample point clouds,constructs a point cloud topology using K-d trees,utilizes principal component analysis to calculate the point cloud normals,and employs the pointwise clustering(PWC)algorithm to extract discontinuities from rock outcrop point clouds.This method provides information on the location and orientation(dip direction and dip angle)of the discontinuities,and the modified whale optimization algorithm(MWOA)is utilized to identify major discontinuity sets and their average orientations.Performance evaluations based on three real cases demonstrate that the proposed method significantly reduces computational time costs without sacrificing accuracy.In particular,the method yields more reasonable extraction results for discontinuities with certain undulations.The presented approach offers a novel tool for efficiently extracting discontinuities from large-scale point clouds.
基金supported by the National Natural Science Foundation of China(Grant Nos.42172312 and 52211540395)support from the Institut Universitaire de France(IUF).
文摘The modeling of crack growth in three-dimensional(3D)space poses significant challenges in rock mechanics due to the complex numerical computation involved in simulating crack propagation and interaction in rock materials.In this study,we present a novel approach that introduces a 3D numerical manifold method(3D-NMM)with a geometric kernel to enhance computational efficiency.Specifically,the maximum tensile stress criterion is adopted as a crack growth criterion to achieve strong discontinuous crack growth,and a local crack tracking algorithm and an angle correction technique are incorporated to address minor limitations of the algorithm in a 3D model.The implementation of the program is carried out in Python,using object-oriented programming in two independent modules:a calculation module and a crack module.Furthermore,we propose feasible improvements to enhance the performance of the algorithm.Finally,we demonstrate the feasibility and effectiveness of the enhanced algorithm in the 3D-NMM using four numerical examples.This study establishes the potential of the 3DNMM,combined with the local tracking algorithm,for accurately modeling 3D crack propagation in brittle rock materials.
基金supported by the BK21 FOUR funded by the Ministry of Education of Korea and National Research Foundation of Korea,a Korea Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure,and Transport(Grant 1615013176)IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD)grant funded by the Korea government(Ministry of Science and ICT)(RS-2024-00438411).
文摘This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discrete derivatives and introducing logistics-related constraints.Optional consideration of the rotation of the cargoes was made to further enhance the optimality of the solutions,if possible to be physically implemented.Evaluation metrics were developed for accurate evaluation and enhancement of the algorithm’s ability to efficiently utilize the loading space and provide a high level of dynamic stability.Experimental results demonstrate the extensive robustness of the proposed algorithm to the diversity of cargoes present in Business-to-Consumer environments.This study contributes practical advancements in both cargo loading optimization and automation of the logistics industry,with potential applications in last-mile delivery services,warehousing,and supply chain management.
基金supported by the fundings from 2024 Young Talents Program for Science and Technology Thinking Tanks(No.XMSB20240711041)2024 Student Research Program on Dynamic Simulation and Force-on-Force Exercise of Nuclear Security in 3D Interactive Environment Using Reinforcement Learning,Natural Science Foundation of Top Talent of SZTU(No.GDRC202407)+2 种基金Shenzhen Science and Technology Program(No.KCXFZ20240903092603005)Shenzhen Science and Technology Program(No.JCYJ20241202124703004)Shenzhen Science and Technology Program(No.KJZD20230923114117032)。
文摘Vulnerability assessment is a systematic process to identify security gaps in the design and evaluation of physical protection systems.Adversarial path planning is a widely used method for identifying potential vulnerabilities and threats to the security and resilience of critical infrastructures.However,achieving efficient path optimization in complex large-scale three-dimensional(3D)scenes remains a significant challenge for vulnerability assessment.This paper introduces a novel A^(*)-algorithmic framework for 3D security modeling and vulnerability assessment.Within this framework,the 3D facility models were first developed in 3ds Max and then incorporated into Unity for A^(*)heuristic pathfinding.The A^(*)-heuristic pathfinding algorithm was implemented with a geometric probability model to refine the detection and distance fields and achieve a rational approximation of the cost to reach the goal.An admissible heuristic is ensured by incorporating the minimum probability of detection(P_(D)^(min))and diagonal distance to estimate the heuristic function.The 3D A^(*)heuristic search was demonstrated using a hypothetical laboratory facility,where a comparison was also carried out between the A^(*)and Dijkstra algorithms for optimal path identification.Comparative results indicate that the proposed A^(*)-heuristic algorithm effectively identifies the most vulnerable adversarial pathfinding with high efficiency.Finally,the paper discusses hidden phenomena and open issues in efficient 3D pathfinding for security applications.
基金Supported by National Natural Science Foundation of China(Grant Nos.52075026 and 52192632)the Fundamental Research Funds for the Central Universities(Grant No.YWF-22-L-1119)。
文摘This paper proposes a gradient conformal design technique to modify the multi-directional stiffness characteristics of 3D printed chiral metamaterials,using various airfoil shapes.The method ensures the integrity of chiral cell nodal circles while improving load transmission efficiency and enhancing manufacturing precision for 3D printing applications.A parametric design framework,integrating finite element analysis and optimization modules,is developed to enhance the wing’s multidirectional stiffness.The optimization process demonstrates that the distribution of chiral structural ligaments and nodal circles significantly affects wing deformation.The stiffness gradient optimization results reveal a variation of over 78%in tail stiffness performance between the best and worst parameter combinations.Experimental outcomes suggest that this strategy can develop metamaterials with enhanced deformability,offering a promising approach for designing morphing wings.
基金supported by The Social Science Foundation of Fujian Province(Grant no.FJ2021B080)The 2023 Fujian Provincial Foreign Cooperation Science and Technology Plan Project(2023I0047)+3 种基金The 2022 Longyan Industry-University-Research Joint Innovation Project(2022LYF18001)The 2023 Fujian Natural Resources Science and Tech-nology Innovation Project(KY-060000-04-2023-2002)Open Project Fund of Hunan Provincial Key Laboratory for Remote Sensing Monitoring of Ecological Environment in Dongting Lake Area(Project No:DTH Key Lab.2023-04)The Construction Science and Technology Research and Development Project of Fujian Province,China(Grant no.2022-K-85).
文摘Historical architecture is an important carrier of cultural and historical heritage in a country and region,and its protection and restoration work plays a crucial role in the inheritance of cultural heritage.However,the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human factors.Therefore,an artificial intelligence repair technology based on three-dimensional(3D)point cloud(PC)reconstruction and generative adversarial networks(GANs)was proposed to improve the precision and efficiency of repair work.First,in-depth research on the principles and algorithms of 3D PC data processing and GANs should be conducted.Second,a digital restoration frameworkwas constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning processes.The experimental results showed that the errors in the restoration of palace buildings,defense walls,pagodas,altars,temples,and mausoleums were 0.17,0.12,0.13,0.11,and 0.09,respectively.The technique can significantly reduce the error while maintaining the high-precision repair effect.This technology with artificial intelligence as the core has excellent accuracy and stability in the digital restoration.It provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.
文摘This study presents a novel hybrid topology optimization and mold design framework that integrates process fitting,runner system optimization,and structural analysis to significantly enhance the performance of injection-molded parts.At its core,the framework employs a greedy algorithm that generates runner systems based on adjacency and shortest path principles,leading to improvements in both mechanical strength and material efficiency.The design optimization is validated through a series of rigorous experimental tests,including three-point bending and torsion tests performed on key-socket frames,ensuring that the optimized designs meet practical performance requirements.A critical innovation of the framework is the development of the Adjacent Element Temperature-Driven Prestress Algorithm(AETDPA),which refines the prediction of mechanical failure and strength fitting.This algorithm has been shown to deliver mesh-independent accuracy,thereby enhancing the reliability of simulation results across various design iterations.The framework’s adaptability is further demonstrated by its ability to adjust optimization methods based on the unique geometry of each part,thus accelerating the overall design process while ensuring struc-tural integrity.In addition to its immediate applications in injection molding,the study explores the potential extension of this framework to metal additive manufacturing,opening new avenues for its use in advanced manufacturing technologies.Numerical simulations,including finite element analysis,support the experimental findings and confirm that the optimized designs provide a balanced combination of strength,durability,and efficiency.Furthermore,the integration challenges with existing injection molding practices are addressed,underscoring the framework’s scalability and industrial relevance.Overall,this hybrid topology optimization framework offers a computationally efficient and robust solution for advanced manufacturing applications,promising significant improvements in design efficiency,cost-effectiveness,and product performance.Future work will focus on further enhancing algorithm robustness and exploring additional applications across diverse manufacturing processes.
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R826),Princess Nourah Bint Abdulrahman University,Riyadh,Saudi ArabiaNorthern Border University,Saudi Arabia,for supporting this work through project number(NBU-CRP-2025-2933).
文摘Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.
文摘Any malfunctions of the actuators of the robots have the potential to destroy the robot’s normal motion,and most of the current actuator fault diagnosis methods are difficult to meet the requirements of simplifying the actuator modeling and solving the difficulty of fault data collection.To solve the problem of real-time diagnosis of actuator faults in the 3-PR(P)S parallel robot,the model of 3-PR(P)S parallel robot and data-driven-based method for the fault diagnosis are presented.Firstly,only the input-output relationship of the actuator is considered for modeling actuator faults,reducing the complexity of fault modeling and reducing the time consumption of parameter identification,thereby meeting the requirements of real-time diagnosis.A Simulink model of the electromechanical actuator(EMA)was constructed to analyze actuator faults.Then the short-term analysis method was employed for collecting the sample data of the slider position on the test platform of the EMA system and feature extraction.Training samples for neural networks are obtained.Furthermore,we optimized the Back Propagation(BP)neural network using the Dung Beetle Optimization Algorithm(DBO),which effectively resolved the weights and thresholds of the BP neural network.Compared to BP and Particle Swarm Optimization(PSO)-BP,the DBO-BP has better convergence,convergence rate,and the best-classifying quality.So,the classification for the different actuator faults is obviously improved.Finally,a fault diagnosis system was designed for the actuator of the 3-PR(P)S parallel robot,and the experimental results demonstrate that this system can detect actuator faults within 0.1 seconds.This work also provides the technical support for the fault-tolerant control of the 3-PR(P)S Parallel robot.
文摘A novel algorithm of 3-D surface image registration is proposed. It makes use of the array information of 3-D points and takes vector/vertex-like features as the basis of the matching. That array information of 3-D points can be easily obtained when capturing original 3-D images. The iterative least-mean-squared （LMS） algorithm is applied to optimizing adaptively the transformation matrix parameters. These can effectively improve the registration performance and hurry up the matching process. Experimental results show that it can reach a good subjective impression on aligned 3-D images. Although the algorithm focuses primarily on the human head model, it can also be used for other objects with small modifications.
基金This research is sponsored by the National Natural Science Foundation of China （No. 40374024）.
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.41374130 and 41604154)
文摘With respect to the gamma spectrum, the energy resolution improves with increase in energy. The counts of full energy peak change with energy, and this approximately complies with the Gaussian distribution. This study mainly examines a method to deconvolve the LaBr_3:Ce gamma spectrum with a detector response matrix constructing algorithm based on energy resolution calibration.In the algorithm, the full width at half maximum(FWHM)of full energy peak was calculated by the cubic spline interpolation algorithm and calibrated by a square root of a quadratic function that changes with the energy. Additionally, the detector response matrix was constructed to deconvolve the gamma spectrum. Furthermore, an improved SNIP algorithm was proposed to eliminate the background. In the experiment, several independent peaks of ^(152)Eu,^(137)Cs, and ^(60)Co sources were detected by a LaBr_3:Ce scintillator that were selected to calibrate the energy resolution. The Boosted Gold algorithm was applied to deconvolve the gamma spectrum. The results showed that the peak position difference between the experiment and the deconvolution was within ± 2 channels and the relative error of peak area was approximately within 0.96–6.74%. Finally, a ^(133) Ba spectrum was deconvolved to verify the efficiency and accuracy of the algorithm in unfolding the overlapped peaks.
基金the National Natural Science Foundation of China(Grant 51908071)Scientific Research Project of Education Department of Hunan Province(Grant 18C0194)Open Fund of Key Laboratory of Road Structure and Material of Ministry of Transport,Changsha University of Science&Technology(Grant kfi 170303).
文摘The bimodulus material is a classical model to describe the elastic behavior of materials with tension-compression asymmetry.Due to the inherently nonlinear properties of bimodular materials,traditional iteration methods suffer from low convergence efficiency and poor adaptability for large-scale structures in engineering.In this paper,a novel 3D algorithm is established by complementing the three shear moduli of the constitutive equation in principal stress coordinates.In contrast to the existing 3D shear modulus constructed based on experience,in this paper the shear modulus is derived theoretically through a limit process.Then,a theoretically self-consistent complemented algorithm is established and implemented in ABAQUS via UMAT;its good stability and convergence efficiency are verified by using benchmark examples.Numerical analysis shows that the calculation error for bimodulus structures using the traditional linear elastic theory is large,which is not in line with reality.
基金Financial support by the China Geological Survey Project(Nos.DD20190030,DD20190032)
文摘The practical application of 3D inversion of gravity data requires a lot of computation time and storage space.To solve this problem,we present an integrated optimization algorithm with the following components:(1)targeting high accuracy in the space domain and fast computation in the wavenumber domain,we design a fast 3D forward algorithm with high precision;and(2)taking advantage of the symmetry of the inversion matrix,the main calculation in gravity conjugate gradient inversion is decomposed into two forward calculations,thus optimizing the computational efficiency of 3D gravity inversion.We verify the calculation accuracy and efficiency of the optimization algorithm by testing various grid-number models through numerical simulation experiments.
基金supported by the National Key Plan for Scientific Research and Development of China(2016YFD0500300)National Natural Science Foundation of China(32170651)Hunan Provincial Natural Science Foundation of China(2020JJ3006)。
文摘The coronavirus 3C-like(3CL)protease,a cysteine protease,plays an important role in viral infection and immune escape.However,there is still a lack of effective tools for determining the cleavage sites of the 3CL protease.This study systematically investigated the diversity of the cleavage sites of the coronavirus 3CL protease on the viral polyprotein,and found that the cleavage motif were highly conserved for viruses in the genera of Alphacoronavirus,Betacoronavirus and Gammacoronavirus.Strong residue preferences were observed at the neighboring positions of the cleavage sites.A random forest(RF)model was built to predict the cleavage sites of the coronavirus 3CL protease based on the representation of residues in cleavage motifs by amino acid indexes,and the model achieved an AUC of 0.96 in cross-validations.The RF model was further tested on an independent test dataset which were composed of cleavage sites on 99 proteins from multiple coronavirus hosts.It achieved an AUC of 0.95 and predicted correctly 80%of the cleavage sites.Then,1,352 human proteins were predicted to be cleaved by the 3CL protease by the RF model.These proteins were enriched in several GO terms related to the cytoskeleton,such as the microtubule,actin and tubulin.Finally,a webserver named 3CLP was built to predict the cleavage sites of the coronavirus 3CL protease based on the RF model.Overall,the study provides an effective tool for identifying cleavage sites of the 3CL protease and provides insights into the molecular mechanism underlying the pathogenicity of coronaviruses.
