With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is ri...With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is rigid and cannot be easily adapted to the dynamic Web environment.To address these challenges,the geographic information service composition(GISC) problem as a sequential decision-making task is modeled.In addition,the Markov decision process(MDP),as a universal model for the planning problem of agents,is used to describe the GISC problem.Then,to achieve self-adaptivity and optimization in a dynamic environment,a novel approach that integrates Monte Carlo tree search(MCTS) and a temporal-difference(TD) learning algorithm is proposed.The concrete services of abstract services are determined with optimal policies and adaptive capability at runtime,based on the environment and the status of component services.The simulation experiment is performed to demonstrate the effectiveness and efficiency through learning quality and performance.展开更多
In this study, to meet the development and application requirements for high-strength and hightoughness energetic structural materials, a representative volume element of a TA15 matrix embedded with a TaZrNb sphere wa...In this study, to meet the development and application requirements for high-strength and hightoughness energetic structural materials, a representative volume element of a TA15 matrix embedded with a TaZrNb sphere was designed and fabricated via diffusion bonding. The mechanisms of the microstructural evolution of the TaZrNb/TA15 interface were investigated via SEM, EBSD, EDS, and XRD.Interface mechanical property tests and in-situ tensile tests were conducted on the sphere-containing structure, and an equivalent tensile-strength model was established for the structure. The results revealed that the TA15 titanium alloy and joint had high density and no pores or cracks. The thickness of the planar joint was approximately 50-60 μm. The average tensile and shear strengths were 767 MPa and 608 MPa, respectively. The thickness of the spherical joint was approximately 60 μm. The Zr and Nb elements in the joint diffused uniformly and formed strong bonds with Ti without forming intermetallic compounds. The interface exhibited submicron grain refinement and a concave-convex interlocking structure. The tensile fracture surface primarily exhibited intergranular fracture combined with some transgranular fracture, which constituted a quasi-brittle fracture mode. The shear fracture surface exhibited brittle fracture with regular arrangements of furrows. Internal fracture occurred along the spherical interface, as revealed by advanced in-situ X-ray microcomputed tomography. The experimental results agreed well with the theoretical predictions, indicating that the high-strength interface contributes to the overall strength and toughness of the sphere-containing structure.展开更多
Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adep...Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adept at leveraging a modest amount of labeled data.Nonetheless,most prevailing SSL segmentation methods for medical images either rely on the single consistency training method or directly fine-tune SSL methods designed for natural images.In this paper,we propose an innovative semi-supervised method called multi-consistency training(MCT)for medical image segmentation.Our approach transcends the constraints of prior methodologies by considering consistency from a dual perspective:output consistency across different up-sampling methods and output consistency of the same data within the same network under various perturbations to the intermediate features.We design distinct semi-supervised loss regression methods for these two types of consistencies.To enhance the application of our MCT model,we also develop a dedicated decoder as the core of our neural network.Thorough experiments were conducted on the polyp dataset and the dental dataset,rigorously compared against other SSL methods.Experimental results demonstrate the superiority of our approach,achieving higher segmentation accuracy.Moreover,comprehensive ablation studies and insightful discussion substantiate the efficacy of our approach in navigating the intricacies of medical image segmentation.展开更多
基金Supported by the National Natural Science Foundation of China(No.41971356,41671400,41701446)National Key Research and Development Program of China(No.2017YFB0503600,2018YFB0505500)Hubei Province Natural Science Foundation of China(No.2017CFB277)。
文摘With the complexity of the composition process and the rapid growth of candidate services,realizing optimal or near-optimal service composition is an urgent problem.Currently,the static service composition chain is rigid and cannot be easily adapted to the dynamic Web environment.To address these challenges,the geographic information service composition(GISC) problem as a sequential decision-making task is modeled.In addition,the Markov decision process(MDP),as a universal model for the planning problem of agents,is used to describe the GISC problem.Then,to achieve self-adaptivity and optimization in a dynamic environment,a novel approach that integrates Monte Carlo tree search(MCTS) and a temporal-difference(TD) learning algorithm is proposed.The concrete services of abstract services are determined with optimal policies and adaptive capability at runtime,based on the environment and the status of component services.The simulation experiment is performed to demonstrate the effectiveness and efficiency through learning quality and performance.
基金supported by the National Natural Science Foundation of China(Grant No.12372351).
文摘In this study, to meet the development and application requirements for high-strength and hightoughness energetic structural materials, a representative volume element of a TA15 matrix embedded with a TaZrNb sphere was designed and fabricated via diffusion bonding. The mechanisms of the microstructural evolution of the TaZrNb/TA15 interface were investigated via SEM, EBSD, EDS, and XRD.Interface mechanical property tests and in-situ tensile tests were conducted on the sphere-containing structure, and an equivalent tensile-strength model was established for the structure. The results revealed that the TA15 titanium alloy and joint had high density and no pores or cracks. The thickness of the planar joint was approximately 50-60 μm. The average tensile and shear strengths were 767 MPa and 608 MPa, respectively. The thickness of the spherical joint was approximately 60 μm. The Zr and Nb elements in the joint diffused uniformly and formed strong bonds with Ti without forming intermetallic compounds. The interface exhibited submicron grain refinement and a concave-convex interlocking structure. The tensile fracture surface primarily exhibited intergranular fracture combined with some transgranular fracture, which constituted a quasi-brittle fracture mode. The shear fracture surface exhibited brittle fracture with regular arrangements of furrows. Internal fracture occurred along the spherical interface, as revealed by advanced in-situ X-ray microcomputed tomography. The experimental results agreed well with the theoretical predictions, indicating that the high-strength interface contributes to the overall strength and toughness of the sphere-containing structure.
基金the Innovation Program of Shanghai Industrial Synergy(No.XTCX-KJ-2023-2-12)。
文摘Medical image segmentation is a crucial task in clinical applications.However,obtaining labeled data for medical images is often challenging.This has led to the appeal of semi-supervised learning(SSL),a technique adept at leveraging a modest amount of labeled data.Nonetheless,most prevailing SSL segmentation methods for medical images either rely on the single consistency training method or directly fine-tune SSL methods designed for natural images.In this paper,we propose an innovative semi-supervised method called multi-consistency training(MCT)for medical image segmentation.Our approach transcends the constraints of prior methodologies by considering consistency from a dual perspective:output consistency across different up-sampling methods and output consistency of the same data within the same network under various perturbations to the intermediate features.We design distinct semi-supervised loss regression methods for these two types of consistencies.To enhance the application of our MCT model,we also develop a dedicated decoder as the core of our neural network.Thorough experiments were conducted on the polyp dataset and the dental dataset,rigorously compared against other SSL methods.Experimental results demonstrate the superiority of our approach,achieving higher segmentation accuracy.Moreover,comprehensive ablation studies and insightful discussion substantiate the efficacy of our approach in navigating the intricacies of medical image segmentation.
