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
Flexible electronics face critical challenges in achieving monolithic three-dimensional(3D)integration,including material compatibility,structural stability,and scalable fabrication methods.Inspired by the tactile sen...Flexible electronics face critical challenges in achieving monolithic three-dimensional(3D)integration,including material compatibility,structural stability,and scalable fabrication methods.Inspired by the tactile sensing mechanism of the human skin,we have developed a flexible monolithic 3D-integrated tactile sensing system based on a holey MXene paste,where each vertical one-body unit simultaneously functions as a microsupercapacitor and pressure sensor.The in-plane mesopores of MXene significantly improve ion accessibility,mitigate the self-stacking of nanosheets,and allow the holey MXene to multifunctionally act as a sensing material,an active electrode,and a conductive interconnect,thus drastically reducing the interface mismatch and enhancing the mechanical robustness.Furthermore,we fabricate a large-scale device using a blade-coating and stamping method,which demonstrates excellent mechanical flexibility,low-power consumption,rapid response,and stable long-term operation.As a proof-of-concept application,we integrate our sensing array into a smart access control system,leveraging deep learning to accurately identify users based on their unique pressing behaviors.This study provides a promising approach for designing highly integrated,intelligent,and flexible electronic systems for advanced human-computer interactions and personalized electronics.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
为对风力机叶片损伤状态进行有效检测,提出一种基于改进YOLO-v3算法的风力机叶片表面损伤检测识别技术。根据风力机叶片损伤区域特点,对网络中锚框(anchor)的尺度进行调整优化;在特征提取网络后引入基于注意力机制的挤压与激励网络(sque...为对风力机叶片损伤状态进行有效检测,提出一种基于改进YOLO-v3算法的风力机叶片表面损伤检测识别技术。根据风力机叶片损伤区域特点,对网络中锚框(anchor)的尺度进行调整优化;在特征提取网络后引入基于注意力机制的挤压与激励网络(squeeze and excitation networks,SENet)结构,使YOLO-v3算法更加关注与目标相关的特征通道,提升网络性能。结果表明,改进后算法的平均精度为84.42%,较原YOLO-v3算法提升了6.14%,检测时间减少了21 ms,改进后的YOLO-v3算法能较好地识别出风力机叶片表面损伤。展开更多
针对嵌入式眼-机交互技术中所采用的传统眼行为识别方法准确率低、速度慢等问题,并结合所研制眼机交互系统硬件特点及应用场景,提出一种基于改进YOLO-v3的眼机交互模型。该模型通过去除13×13特征分辨率的检测模块、增加浅层网络的...针对嵌入式眼-机交互技术中所采用的传统眼行为识别方法准确率低、速度慢等问题,并结合所研制眼机交互系统硬件特点及应用场景,提出一种基于改进YOLO-v3的眼机交互模型。该模型通过去除13×13特征分辨率的检测模块、增加浅层网络的层数以及采用K-means聚类算法选取初始先验框,提高了网络像素特征提取细粒度并加快了检测速度,进而结合人眼特征参数提取方法和眼行为识别算法,构建出了眼机交互模型并进行实验。实验结果表明,该模型对不同眼行为的识别率达91.30%,改进的YOLO-v3网络的平均检测准确率(mean average precision,mAP)为99.9%,识别速度达22.8 FPS,相比原YOLO-v3方法检测时间缩短了11.4%。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
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 the National Natural Science Foundation of China(52272177,12204010)the Foundation for the Introduction of High-Level Talents of Anhui University(S020118002/097)+1 种基金the University Synergy Innovation Program of Anhui Province(GXXT-2023-066)the Scientific Research Project of Anhui Provincial Higher Education Institution(2023AH040008)。
文摘Flexible electronics face critical challenges in achieving monolithic three-dimensional(3D)integration,including material compatibility,structural stability,and scalable fabrication methods.Inspired by the tactile sensing mechanism of the human skin,we have developed a flexible monolithic 3D-integrated tactile sensing system based on a holey MXene paste,where each vertical one-body unit simultaneously functions as a microsupercapacitor and pressure sensor.The in-plane mesopores of MXene significantly improve ion accessibility,mitigate the self-stacking of nanosheets,and allow the holey MXene to multifunctionally act as a sensing material,an active electrode,and a conductive interconnect,thus drastically reducing the interface mismatch and enhancing the mechanical robustness.Furthermore,we fabricate a large-scale device using a blade-coating and stamping method,which demonstrates excellent mechanical flexibility,low-power consumption,rapid response,and stable long-term operation.As a proof-of-concept application,we integrate our sensing array into a smart access control system,leveraging deep learning to accurately identify users based on their unique pressing behaviors.This study provides a promising approach for designing highly integrated,intelligent,and flexible electronic systems for advanced human-computer interactions and personalized electronics.
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.
文摘为对风力机叶片损伤状态进行有效检测,提出一种基于改进YOLO-v3算法的风力机叶片表面损伤检测识别技术。根据风力机叶片损伤区域特点,对网络中锚框(anchor)的尺度进行调整优化;在特征提取网络后引入基于注意力机制的挤压与激励网络(squeeze and excitation networks,SENet)结构,使YOLO-v3算法更加关注与目标相关的特征通道,提升网络性能。结果表明,改进后算法的平均精度为84.42%,较原YOLO-v3算法提升了6.14%,检测时间减少了21 ms,改进后的YOLO-v3算法能较好地识别出风力机叶片表面损伤。
文摘针对嵌入式眼-机交互技术中所采用的传统眼行为识别方法准确率低、速度慢等问题,并结合所研制眼机交互系统硬件特点及应用场景,提出一种基于改进YOLO-v3的眼机交互模型。该模型通过去除13×13特征分辨率的检测模块、增加浅层网络的层数以及采用K-means聚类算法选取初始先验框,提高了网络像素特征提取细粒度并加快了检测速度,进而结合人眼特征参数提取方法和眼行为识别算法,构建出了眼机交互模型并进行实验。实验结果表明,该模型对不同眼行为的识别率达91.30%,改进的YOLO-v3网络的平均检测准确率(mean average precision,mAP)为99.9%,识别速度达22.8 FPS,相比原YOLO-v3方法检测时间缩短了11.4%。
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.
