Chemical reactivity towards electron transfer is captured by the Fukui function.However,this is not well defined when the system or its ions have degenerate or pseudo-degenerate ground states.In such a case,the first-...Chemical reactivity towards electron transfer is captured by the Fukui function.However,this is not well defined when the system or its ions have degenerate or pseudo-degenerate ground states.In such a case,the first-order chemical response is not independent of the perturbation and the correct response has to be computed using the mathematical formalism of perturbation theory for degenerate states.Spatialpseudo-degeneracy is ubiquitous in nanostructures with high symmetry and totally extended systems.Given the size of these systems,using degenerate-state perturbation theory is impractical because it requires the calculation of many excited states.Here we present an alternative to compute the chemical response of extended systems using models of local softness in terms of the local density of states.The local softness is approximately equal to the density of states at the Fermi level.However,such approximation leaves out the contribution of inner states.In order to include and weight the contribution of the states around the Fermi level,a model inspired by the long-range behavior of the local softness is presented.Single wall capped carbon nanotubes(SWCCNT) illustrate the limitation of the frontier orbital theory in extended systems.Thus,we have used a C360 SWCCNT to test the proposed model and how it compares with available models based on the local density of states.Interestingly,a simple Hü ckel approximation captures the main features of chemical response of these systems.Our results suggest that density-of-states models of the softness along simple tight binding Hamiltonians could be used to explore the chemical reactivity of more complex system,such a surfaces and nanoparticles.展开更多
Inspired by the tremendous achievements of meta-learning in various fields,this paper proposes the local quadratic embedding learning(LQEL)algorithm for regression problems based on metric learning and neural networks...Inspired by the tremendous achievements of meta-learning in various fields,this paper proposes the local quadratic embedding learning(LQEL)algorithm for regression problems based on metric learning and neural networks(NNs).First,Mahalanobis metric learning is improved by optimizing the global consistency of the metrics between instances in the input and output space.Then,we further prove that the improved metric learning problem is equivalent to a convex programming problem by relaxing the constraints.Based on the hypothesis of local quadratic interpolation,the algorithm introduces two lightweight NNs;one is used to learn the coefficient matrix in the local quadratic model,and the other is implemented for weight assignment for the prediction results obtained from different local neighbors.Finally,the two sub-mod els are embedded in a unified regression framework,and the parameters are learned by means of a stochastic gradient descent(SGD)algorithm.The proposed algorithm can make full use of the information implied in target labels to find more reliable reference instances.Moreover,it prevents the model degradation caused by sensor drift and unmeasurable variables by modeling variable differences with the LQEL algorithm.Simulation results on multiple benchmark datasets and two practical industrial applications show that the proposed method outperforms several popular regression methods.展开更多
In order to solve the problem that the existing meshless models are of high computational complexity and are difficult to express the biomechanical characteristics of real soft tissue, a local high-resolution deformat...In order to solve the problem that the existing meshless models are of high computational complexity and are difficult to express the biomechanical characteristics of real soft tissue, a local high-resolution deformation model of soft tissue based on element-free Galerkin method is proposed. The proposed model applies an element-free Galerkin method to establish the model, and integrates Kelvin viscoelastic model and adjustment function to simulate nonlinear viscoelasticity of soft tissue. Meanwhile, a local high-resolution algorithm is applied to sample and render the deformed region of the model to reduce the computational complexity. To verify the effectiveness of the model,liver and brain tumor deformation simulation experiments are carried out. The experimental results show that compared with the existing meshless models, the proposed model well reflects the biomechanical characteristics of soft tissue, and is of high authenticity, which can provide better visual feedback to users while reducing computational cost.展开更多
针对软刚臂系泊系统铰节点在服役过程中出现的疲劳损伤问题,提出一种基于原型监测和局部密度双向聚类算法(Bidirectional Clustering Algorithm based on Local Density,BCALoD)的疲劳寿命计算方法。采用BCALoD算法对获得的船体六自由...针对软刚臂系泊系统铰节点在服役过程中出现的疲劳损伤问题,提出一种基于原型监测和局部密度双向聚类算法(Bidirectional Clustering Algorithm based on Local Density,BCALoD)的疲劳寿命计算方法。采用BCALoD算法对获得的船体六自由度进行工况分类,运用多体动力学将运动数据转算为受力时程,将其作为铰节点疲劳寿命分析的载荷谱。采用Abaqus软件建立各铰节点有限元模型以计算热点应力,结合Miner线性疲劳累积损伤理论和雨流计数方法计算疲劳寿命。进一步分析评估基于实测数据的铰节点疲劳设计指标,指出该FPSO软刚臂上铰节点的疲劳寿命不足以支持其完成服役,且各铰节点难以统一维护和更换。本研究可为在役软刚臂系泊系统的疲劳寿命计算提供一种新的载荷处理方法,为未来海洋平台的设计提供参考。展开更多
基金This work has been supported by FONDECYT grants 1140313 and 11150164. CC and PF acknowledge support by Financiamiento Basal para Centros Cientificos y Tecnologicos de Excelencia-FB0807, and project RC-130006 CILIS, granted by the Fondo de Innovacion para
文摘Chemical reactivity towards electron transfer is captured by the Fukui function.However,this is not well defined when the system or its ions have degenerate or pseudo-degenerate ground states.In such a case,the first-order chemical response is not independent of the perturbation and the correct response has to be computed using the mathematical formalism of perturbation theory for degenerate states.Spatialpseudo-degeneracy is ubiquitous in nanostructures with high symmetry and totally extended systems.Given the size of these systems,using degenerate-state perturbation theory is impractical because it requires the calculation of many excited states.Here we present an alternative to compute the chemical response of extended systems using models of local softness in terms of the local density of states.The local softness is approximately equal to the density of states at the Fermi level.However,such approximation leaves out the contribution of inner states.In order to include and weight the contribution of the states around the Fermi level,a model inspired by the long-range behavior of the local softness is presented.Single wall capped carbon nanotubes(SWCCNT) illustrate the limitation of the frontier orbital theory in extended systems.Thus,we have used a C360 SWCCNT to test the proposed model and how it compares with available models based on the local density of states.Interestingly,a simple Hü ckel approximation captures the main features of chemical response of these systems.Our results suggest that density-of-states models of the softness along simple tight binding Hamiltonians could be used to explore the chemical reactivity of more complex system,such a surfaces and nanoparticles.
基金supported by the National Key Research and Development Program of China(2016YFB0303401)the International(Regional)Cooperation and Exchange Project(61720106008)+1 种基金the National Science Fund for Distinguished Young Scholars(61725301)the Shanghai AI Lab。
文摘Inspired by the tremendous achievements of meta-learning in various fields,this paper proposes the local quadratic embedding learning(LQEL)algorithm for regression problems based on metric learning and neural networks(NNs).First,Mahalanobis metric learning is improved by optimizing the global consistency of the metrics between instances in the input and output space.Then,we further prove that the improved metric learning problem is equivalent to a convex programming problem by relaxing the constraints.Based on the hypothesis of local quadratic interpolation,the algorithm introduces two lightweight NNs;one is used to learn the coefficient matrix in the local quadratic model,and the other is implemented for weight assignment for the prediction results obtained from different local neighbors.Finally,the two sub-mod els are embedded in a unified regression framework,and the parameters are learned by means of a stochastic gradient descent(SGD)algorithm.The proposed algorithm can make full use of the information implied in target labels to find more reliable reference instances.Moreover,it prevents the model degradation caused by sensor drift and unmeasurable variables by modeling variable differences with the LQEL algorithm.Simulation results on multiple benchmark datasets and two practical industrial applications show that the proposed method outperforms several popular regression methods.
基金Supported by the National Natural Science Foundation of China(No.61502240,61502096,61304205,61773219)Natural Science Foundation of Jiangsu Province(No.BK20141002,BK20150634)
文摘In order to solve the problem that the existing meshless models are of high computational complexity and are difficult to express the biomechanical characteristics of real soft tissue, a local high-resolution deformation model of soft tissue based on element-free Galerkin method is proposed. The proposed model applies an element-free Galerkin method to establish the model, and integrates Kelvin viscoelastic model and adjustment function to simulate nonlinear viscoelasticity of soft tissue. Meanwhile, a local high-resolution algorithm is applied to sample and render the deformed region of the model to reduce the computational complexity. To verify the effectiveness of the model,liver and brain tumor deformation simulation experiments are carried out. The experimental results show that compared with the existing meshless models, the proposed model well reflects the biomechanical characteristics of soft tissue, and is of high authenticity, which can provide better visual feedback to users while reducing computational cost.
文摘针对软刚臂系泊系统铰节点在服役过程中出现的疲劳损伤问题,提出一种基于原型监测和局部密度双向聚类算法(Bidirectional Clustering Algorithm based on Local Density,BCALoD)的疲劳寿命计算方法。采用BCALoD算法对获得的船体六自由度进行工况分类,运用多体动力学将运动数据转算为受力时程,将其作为铰节点疲劳寿命分析的载荷谱。采用Abaqus软件建立各铰节点有限元模型以计算热点应力,结合Miner线性疲劳累积损伤理论和雨流计数方法计算疲劳寿命。进一步分析评估基于实测数据的铰节点疲劳设计指标,指出该FPSO软刚臂上铰节点的疲劳寿命不足以支持其完成服役,且各铰节点难以统一维护和更换。本研究可为在役软刚臂系泊系统的疲劳寿命计算提供一种新的载荷处理方法,为未来海洋平台的设计提供参考。
