It was tried to prepare the microcapsules containing grape polyphenol with the spray drying method followed by the layer-by-layer method. As grape polyphenol was water soluble, the spray drying method was adopted to o...It was tried to prepare the microcapsules containing grape polyphenol with the spray drying method followed by the layer-by-layer method. As grape polyphenol was water soluble, the spray drying method was adopted to obtain the higher content. As the shell material of the first microcapsules prepared by the spray drying method, palmitic acid with the melting point of 60°C was adopted in order to prevent grape polyphenol from dissolution into water. As the shell material of the second microcapsules prepared by the layer-by-layer method, chitosan was used to coat the first microcapsules and to give the microcapsules alcohol resistance. In the experiment, the spray drying conditions such as the inlet temperature and the spraying pressure, the oil soluble surfactant species and the chitosan concentration were changed. The mean diameters of microcapsules could be controlled in the range from 5 μm to 35 μm by changing the spraying pressure and the inlet temperature. The yield of microcapsules and the microencapsulation efficiency over 50% could be obtained under the conditions of P = 1.0 kgf/cm2 and Tin = 100°C. Furthermore, the microencapsulation efficiency could be increased by adding the oil soluble surfactant with the larger HLB value. Coating with chitosan could considerably increase alcohol resistance.展开更多
This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior.Physical models involving deformation,such as collisions,vibrations,and penetration,are devel-...This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior.Physical models involving deformation,such as collisions,vibrations,and penetration,are devel-oped using the material point method.To reduce the computational cost of Monte Carlo simulations,response surface models are created as surrogate models for the material point system to approximate its dynamic behavior.An adaptive randomized greedy algorithm is employed to construct a sparse polynomial chaos expansion model with a fixed order,effectively balancing the accuracy and computational efficiency of the surrogate model.Based on the sparse polynomial chaos expansion,sensitivity analysis is conducted using the global finite difference and Sobol methods.Several examples of structural dynamics are provided to demonstrate the effectiveness of the proposed method in addressing structural dynamics problems.展开更多
Based on the greedy randomized Kaczmarz(GRK)method,we propose a multi-step greedy Kaczmarz method for solving large-scale consistent linear systems,utilizing multi-step projection techniques.Its convergence is proved ...Based on the greedy randomized Kaczmarz(GRK)method,we propose a multi-step greedy Kaczmarz method for solving large-scale consistent linear systems,utilizing multi-step projection techniques.Its convergence is proved when the linear system is consistent.Numerical experiments demonstrate that the proposed method is effective and more efficient than several existing classical Kaczmarz methods.展开更多
Non-contact debris removal methods are fuel-efficient in a single operation compared to contact-based strategies as spacecraft don’t need to match debris velocity.To comprehensively analyze this scheme,maneuvering sc...Non-contact debris removal methods are fuel-efficient in a single operation compared to contact-based strategies as spacecraft don’t need to match debris velocity.To comprehensively analyze this scheme,maneuvering schemes for maximum debris removal with minimum fuel consumption,including task assignment,sequence planning,and trajectory planning,must be formulated.The coupling between variables’dimensions and optimization results in task assignment poses challenges,as debris removal is repetitive and uncertain,leading to a vast search space.This paper proposes a novel Greedy Randomized Adaptive Search Procedure with Large Neighborhood and Crossover Mechanisms(GRASP-LNCM)to address this problem.The hybrid dynamic iteration mechanism improves computational efficiency and enhances the optimality of results.The model innovatively considers unsuccessful single removal by using a quantitative method to assess removal percentage.In addition,to improve the efficiency of sequence and trajectory planning,a Suboptimal Search Algorithm(SSA)based on the Lambert property and accelerated Multi-Revolution Lambert Problem(MRLP)solving strategy is established.Finally,a real Iridium-33 debris removal mission is studied.The simulation demonstrates that the proposed algorithm achieves state-of-the-art performance in several typical scenarios.Compared to the contact-based scheme,the new one is simpler,saving more fuel under certain conditions.展开更多
The reduced basis methods (RBM) have been demonstrated as a promising numerical technique for statics problems and are extended to structural dynamic problems in this paper. Direct step-by-step integration and mode su...The reduced basis methods (RBM) have been demonstrated as a promising numerical technique for statics problems and are extended to structural dynamic problems in this paper. Direct step-by-step integration and mode superposition are the most widely used methods in the field of the finite element analysis of structural dynamic response and solid mechanics. Herein these two methods are both transformed into reduced forms according to the proposed reduced basis methods. To generate a reduced surrogate model with small size, a greedy algorithm is suggested to construct sample set and reduced basis space adaptively in a prescribed training parameter space. For mode superposition method, the reduced basis space comprises the truncated eigenvectors from generalized eigenvalue problem associated with selected sample parameters. The reduced generalized eigenvalue problem is obtained by the projection of original generalized eigenvalue problem onto the reduced basis space. In the situation of direct integration, the solutions of the original increment formulation corresponding to the sample set are extracted to construct the reduced basis space. The reduced increment formulation is formed by the same method as mode superposition method. Numerical example is given in Section 5 to validate the efficiency of the presented reduced basis methods for structural dynamic problems.展开更多
During the past decade, increasing attention has been given to the development of meshless methods using radial basis functions for the numerical solution of Partial Differential Equations （PDEs）. A level set method...During the past decade, increasing attention has been given to the development of meshless methods using radial basis functions for the numerical solution of Partial Differential Equations （PDEs）. A level set method is a promising design tool for tracking, modelling and simulating the motion of free boundaries in fluid mechanics, combustion, computer animation and image processing. In the conventional level set methods, the level set equation is solved to evolve the interface using a capturing Eulerian approach. The solving procedure requires an appropriate choice of the upwind schemes, reinitialization, etc. Our goal is to include Multiquadric Radial Basis Functions （MQ RBFs） into the level set method to construct a more efficient approach and stabilize the solution process with the adaptive greedy algorithm. This paper presents an alternative approach to the conventional level set methods for solving moving-boundary problems. The solution was compared to the solution calculated by the exact explicit lime integration scheme. The examples show that MQ RBFs and adaptive greedy algorithm is a very promising calculation scheme.展开更多
The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more prominent.The K-anonymity algorithm is an eff...The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more prominent.The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’privacy by anonymizing big data.However,the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability.In addition,ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced.Based on this,we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data,while guaranteeing improved data usability.Specifically,we construct a new information loss function based on the information quantity theory.Considering that different quasi-identification attributes have different impacts on sensitive attributes,we set weights for each quasi-identification attribute when designing the information loss function.In addition,to reduce information loss,we improve K-anonymity in two ways.First,we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms,i.e.,greedy algorithm and 2-means clustering algorithm.In addition,we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of mass.Meanwhile,we design the K-anonymity algorithm of this scheme based on the constructed information loss function,the improved 2-means clustering algorithm,and the greedy algorithm,which reduces the information loss.Finally,we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss.展开更多
文摘It was tried to prepare the microcapsules containing grape polyphenol with the spray drying method followed by the layer-by-layer method. As grape polyphenol was water soluble, the spray drying method was adopted to obtain the higher content. As the shell material of the first microcapsules prepared by the spray drying method, palmitic acid with the melting point of 60°C was adopted in order to prevent grape polyphenol from dissolution into water. As the shell material of the second microcapsules prepared by the layer-by-layer method, chitosan was used to coat the first microcapsules and to give the microcapsules alcohol resistance. In the experiment, the spray drying conditions such as the inlet temperature and the spraying pressure, the oil soluble surfactant species and the chitosan concentration were changed. The mean diameters of microcapsules could be controlled in the range from 5 μm to 35 μm by changing the spraying pressure and the inlet temperature. The yield of microcapsules and the microencapsulation efficiency over 50% could be obtained under the conditions of P = 1.0 kgf/cm2 and Tin = 100°C. Furthermore, the microencapsulation efficiency could be increased by adding the oil soluble surfactant with the larger HLB value. Coating with chitosan could considerably increase alcohol resistance.
基金support from the National Natural Science Foundation of China(Grant Nos.52174123&52274222).
文摘This paper presents a framework for constructing surrogate models for sensitivity analysis of structural dynamics behavior.Physical models involving deformation,such as collisions,vibrations,and penetration,are devel-oped using the material point method.To reduce the computational cost of Monte Carlo simulations,response surface models are created as surrogate models for the material point system to approximate its dynamic behavior.An adaptive randomized greedy algorithm is employed to construct a sparse polynomial chaos expansion model with a fixed order,effectively balancing the accuracy and computational efficiency of the surrogate model.Based on the sparse polynomial chaos expansion,sensitivity analysis is conducted using the global finite difference and Sobol methods.Several examples of structural dynamics are provided to demonstrate the effectiveness of the proposed method in addressing structural dynamics problems.
基金supported by the National Natural Science Foundation of China(No.12071149)the Science and Technology Commission of Shanghai Municipality of China(No.22DZ2229014)the National Key R&D Program of China(No.2022YFA1004403).
文摘Based on the greedy randomized Kaczmarz(GRK)method,we propose a multi-step greedy Kaczmarz method for solving large-scale consistent linear systems,utilizing multi-step projection techniques.Its convergence is proved when the linear system is consistent.Numerical experiments demonstrate that the proposed method is effective and more efficient than several existing classical Kaczmarz methods.
基金co-supported by the National Natural Science Foundation of China(Nos.U23B6001,62273118,12150008)the Fundamental Research Funds for the Central Universities,China(No.2023FRFK02043)+1 种基金the Natural Science Foundation of Heilongjiang Province,China(No.LH2022F023)China Aerospace Science and Technology Corporation Youth Talent Support Program.
文摘Non-contact debris removal methods are fuel-efficient in a single operation compared to contact-based strategies as spacecraft don’t need to match debris velocity.To comprehensively analyze this scheme,maneuvering schemes for maximum debris removal with minimum fuel consumption,including task assignment,sequence planning,and trajectory planning,must be formulated.The coupling between variables’dimensions and optimization results in task assignment poses challenges,as debris removal is repetitive and uncertain,leading to a vast search space.This paper proposes a novel Greedy Randomized Adaptive Search Procedure with Large Neighborhood and Crossover Mechanisms(GRASP-LNCM)to address this problem.The hybrid dynamic iteration mechanism improves computational efficiency and enhances the optimality of results.The model innovatively considers unsuccessful single removal by using a quantitative method to assess removal percentage.In addition,to improve the efficiency of sequence and trajectory planning,a Suboptimal Search Algorithm(SSA)based on the Lambert property and accelerated Multi-Revolution Lambert Problem(MRLP)solving strategy is established.Finally,a real Iridium-33 debris removal mission is studied.The simulation demonstrates that the proposed algorithm achieves state-of-the-art performance in several typical scenarios.Compared to the contact-based scheme,the new one is simpler,saving more fuel under certain conditions.
文摘The reduced basis methods (RBM) have been demonstrated as a promising numerical technique for statics problems and are extended to structural dynamic problems in this paper. Direct step-by-step integration and mode superposition are the most widely used methods in the field of the finite element analysis of structural dynamic response and solid mechanics. Herein these two methods are both transformed into reduced forms according to the proposed reduced basis methods. To generate a reduced surrogate model with small size, a greedy algorithm is suggested to construct sample set and reduced basis space adaptively in a prescribed training parameter space. For mode superposition method, the reduced basis space comprises the truncated eigenvectors from generalized eigenvalue problem associated with selected sample parameters. The reduced generalized eigenvalue problem is obtained by the projection of original generalized eigenvalue problem onto the reduced basis space. In the situation of direct integration, the solutions of the original increment formulation corresponding to the sample set are extracted to construct the reduced basis space. The reduced increment formulation is formed by the same method as mode superposition method. Numerical example is given in Section 5 to validate the efficiency of the presented reduced basis methods for structural dynamic problems.
文摘During the past decade, increasing attention has been given to the development of meshless methods using radial basis functions for the numerical solution of Partial Differential Equations （PDEs）. A level set method is a promising design tool for tracking, modelling and simulating the motion of free boundaries in fluid mechanics, combustion, computer animation and image processing. In the conventional level set methods, the level set equation is solved to evolve the interface using a capturing Eulerian approach. The solving procedure requires an appropriate choice of the upwind schemes, reinitialization, etc. Our goal is to include Multiquadric Radial Basis Functions （MQ RBFs） into the level set method to construct a more efficient approach and stabilize the solution process with the adaptive greedy algorithm. This paper presents an alternative approach to the conventional level set methods for solving moving-boundary problems. The solution was compared to the solution calculated by the exact explicit lime integration scheme. The examples show that MQ RBFs and adaptive greedy algorithm is a very promising calculation scheme.
基金Foundation of National Natural Science Foundation of China(62202118)Scientific and Technological Research Projects from Guizhou Education Department([2023]003)+1 种基金Guizhou Provincial Department of Science and Technology Hundred Levels of Innovative Talents Project(GCC[2023]018)Top Technology Talent Project from Guizhou Education Department([2022]073).
文摘The development of technologies such as big data and blockchain has brought convenience to life,but at the same time,privacy and security issues are becoming more and more prominent.The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users’privacy by anonymizing big data.However,the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability.In addition,ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced.Based on this,we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data,while guaranteeing improved data usability.Specifically,we construct a new information loss function based on the information quantity theory.Considering that different quasi-identification attributes have different impacts on sensitive attributes,we set weights for each quasi-identification attribute when designing the information loss function.In addition,to reduce information loss,we improve K-anonymity in two ways.First,we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms,i.e.,greedy algorithm and 2-means clustering algorithm.In addition,we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of mass.Meanwhile,we design the K-anonymity algorithm of this scheme based on the constructed information loss function,the improved 2-means clustering algorithm,and the greedy algorithm,which reduces the information loss.Finally,we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss.
