We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training ph...We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.展开更多
A novel memory efficient path metric update is proposed for Maximum A Posteriori(MAP) decoder of turbo codes to reduce the memory requirement of state metric information calcu-lation. For MAP decoder,the same memory c...A novel memory efficient path metric update is proposed for Maximum A Posteriori(MAP) decoder of turbo codes to reduce the memory requirement of state metric information calcu-lation. For MAP decoder,the same memory can be shared by the forward and backward metrics with this metric update scheme. The forward and backward metrics update can be performed at the same time. And all of the extrinsic information can be calculated at the end of metric update. Therefore,the latency and area in the implementation will be reduced with the proposed metric update method.展开更多
The location of model errors in a stiffness matrix by using test data has been investigated by the others.The present paper deals with the problem of updating stiffness elements in the erroneous areas. Firstly,a model...The location of model errors in a stiffness matrix by using test data has been investigated by the others.The present paper deals with the problem of updating stiffness elements in the erroneous areas. Firstly,a model that bears relation to erroneous elements only is derived.This model is termed local errors model,which reduces orders and computational loads compared with global stiffness matrix. Secondly,an inverse eigenvalue method is used to update model errors. The results of a numerical experiment demonstrate that the method is quite effective.展开更多
The optimal matrix method and optimal elemental method used to update finite element models may not provide accurate results.This situation occurs when the test modal model is incomplete,as is often the case in practi...The optimal matrix method and optimal elemental method used to update finite element models may not provide accurate results.This situation occurs when the test modal model is incomplete,as is often the case in practice.An improved optimal elemental method is presented that defines a new objective function,and as a byproduct,circumvents the need for mass normalized modal shapes,which are also not readily available in practice.To solve the group of nonlinear equations created by the improved optimal method,the Lagrange multiplier method and Matlab function fmincon are employed.To deal with actual complex structures, the float-encoding genetic algorithm(FGA)is introduced to enhance the capability of the improved method.Two examples,a 7- degree of freedom(DOF)mass-spring system and a 53-DOF planar frame,respectively,are updated using the improved method. The example results demonstrate the advantages of the improved method over existing optimal methods,and show that the genetic algorithm is an effective way to update the models used for actual complex structures.展开更多
A real-time channel flood forecast model was developed to simulate channel flow in plain rivers based on the dynamic wave theory. Taking into consideration channel shape differences along the channel, a roughness upda...A real-time channel flood forecast model was developed to simulate channel flow in plain rivers based on the dynamic wave theory. Taking into consideration channel shape differences along the channel, a roughness updating technique was developed using the Kalman filter method to update Manning's roughness coefficient at each time step of the calculation processes. Channel shapes were simplified as rectangles, triangles, and parabolas, and the relationships between hydraulic radius and water depth were developed for plain rivers. Based on the relationship between the Froude number and the inertia terms of the momentum equation in the Saint-Venant equations, the relationship between Manning's roughness coefficient and water depth was obtained. Using the channel of the Huaihe River from Wangjiaba to Lutaizi stations as a case, to test the performance and rationality of the present flood routing model, the original hydraulic model was compared with the developed model. Results show that the stage hydrographs calculated by the developed flood routing model with the updated Manning's roughness coefficient have a good agreement with the observed stage hydrographs. This model performs better than the original hydraulic model.展开更多
In recent years, it has shown that a generalized thresholding algorithm is useful for inverse problems with sparsity constraints. The generalized thresholding minimizes the non-convex p-norm based function with p <...In recent years, it has shown that a generalized thresholding algorithm is useful for inverse problems with sparsity constraints. The generalized thresholding minimizes the non-convex p-norm based function with p < 1, and it penalizes small coefficients over a wider range meanwhile applies less bias to the larger coefficients.In this work, on the basis of two-level Bregman method with dictionary updating(TBMDU), we use the modified thresholding to minimize the non-convex function and propose the generalized TBMDU(GTBMDU) algorithm.The experimental results on magnetic resonance(MR) image simulations and real MR data, under a variety of sampling trajectories and acceleration factors, consistently demonstrate that the proposed algorithm can efficiently reconstruct the MR images and present advantages over the previous soft thresholding approaches.展开更多
This paper shows that the alternating direction method can be used to solve the structured inverse quadratic eigenvalue problem with symmetry, positive semi-definiteness and sparsity requirements. The results of numer...This paper shows that the alternating direction method can be used to solve the structured inverse quadratic eigenvalue problem with symmetry, positive semi-definiteness and sparsity requirements. The results of numerical examples show that the proposed method works well.展开更多
Most of exiting model updating methods based on the substructure matrices did not consider the effect of model reduction process on model updating which led to the updating results could not become more and more accur...Most of exiting model updating methods based on the substructure matrices did not consider the effect of model reduction process on model updating which led to the updating results could not become more and more accurate with the improvement of the model reduction precision and the convergence rate was greatly reduced.In order to solve this problem,this paper analyses the basic reason about this problem,and proposes an improved model updating method of reduced-models,named as improved reduced cross-model cross-mode(IRCMCM) method.The proposed method eliminates the disadvantageous effect by adding a correction term to the model updating formula and employing an iterative process.The results obtained by the referenced method and IRCMCM method are compared by numerical examples of satellite's plates,which indicate the model updating results are more accurate by using the proposed method,and the model updating precision becomes better with the precision of the model reduction upgraded and the convergence rate is improved to a large extent at the same time.展开更多
A fluid-structure interaction approach is proposed in this paper based onNon-Ordinary State-Based Peridynamics(NOSB-PD)and Updated Lagrangian Particle Hydrodynamics(ULPH)to simulate the fluid-structure interaction pro...A fluid-structure interaction approach is proposed in this paper based onNon-Ordinary State-Based Peridynamics(NOSB-PD)and Updated Lagrangian Particle Hydrodynamics(ULPH)to simulate the fluid-structure interaction problem with large geometric deformation and material failure and solve the fluid-structure interaction problem of Newtonian fluid.In the coupled framework,the NOSB-PD theory describes the deformation and fracture of the solid material structure.ULPH is applied to describe the flow of Newtonian fluids due to its advantages in computational accuracy.The framework utilizes the advantages of NOSB-PD theory for solving discontinuous problems and ULPH theory for solving fluid problems,with good computational stability and robustness.A fluidstructure coupling algorithm using pressure as the transmission medium is established to deal with the fluidstructure interface.The dynamic model of solid structure and the PD-ULPH fluid-structure interaction model involving large deformation are verified by numerical simulations.The results agree with the analytical solution,the available experimental data,and other numerical results.Thus,the accuracy and effectiveness of the proposed method in solving the fluid-structure interaction problem are demonstrated.The fluid-structure interactionmodel based on ULPH and NOSB-PD established in this paper provides a new idea for the numerical solution of fluidstructure interaction and a promising approach for engineering design and experimental prediction.展开更多
The problem of correcting simultaneously mass and stiffness matrices of finite element model of undamped structural systems using vibration tests is considered in this paper.The desired matrix properties,including sat...The problem of correcting simultaneously mass and stiffness matrices of finite element model of undamped structural systems using vibration tests is considered in this paper.The desired matrix properties,including satisfaction of the characteristic equation,symmetry,positive semidefiniteness and sparsity,are imposed as side constraints to form the optimal matrix pencil approximation problem.Using partial Lagrangian multipliers,we transform the nonlinearly constrained optimization problem into an equivalent matrix linear variational inequality,develop a proximal point-like method for solving the matrix linear variational inequality,and analyze its global convergence.Numerical results are included to illustrate the performance and application of the proposed method.展开更多
Natural convection is a heat transfer mechanism driven by temperature or density differences,leading to fluid motion without external influence.It occurs in various natural and engineering phenomena,influencing heat t...Natural convection is a heat transfer mechanism driven by temperature or density differences,leading to fluid motion without external influence.It occurs in various natural and engineering phenomena,influencing heat transfer,climate,and fluid mixing in industrial processes.This work aims to use the Updated Lagrangian Particle Hydrodynamics(ULPH)theory to address natural convection problems.The Navier-Stokes equation is discretized using second-order nonlocal differential operators,allowing a direct solution of the Laplace operator for temperature in the energy equation.Various numerical simulations,including cases such as natural convection in square cavities and two concentric cylinders,were conducted to validate the reliability of the model.The results demonstrate that the proposed model exhibits excellent accuracy and performance,providing a promising and effective numerical approach for natural convection problems.展开更多
In this paper, we prove the local and Supcrlinear convergence theorem of the column-updating method for n>2. This is an oped problem for the convergene theory of the column-updating method given by Martinez in the ...In this paper, we prove the local and Supcrlinear convergence theorem of the column-updating method for n>2. This is an oped problem for the convergene theory of the column-updating method given by Martinez in the Intcrnational Conference of the NATO-ASI (Italy, 1994).展开更多
Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real...Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real-time automated damage detection method by developing a theory-assisted adaptive mutiagent twin delayed deep deterministic(TA2-MATD3)policy gradient algorithm.First,the theoretical framework of reinforcement-learning-driven damage detection is established.To address the disadvantages of traditional mutiagent twin delayed deep deterministic(MATD3)method,the theory-assisted mechanism and the adaptive experience playback mechanism are introduced.Moreover,a historical residential house built in 1889 was taken as an example,using its 12-month structural health monitoring data.TA2-MATD3 was compared with existing damage detection methods in terms of the convergence ratio,online computing efficiency,and damage detection accuracy.The results show that the computational efficiency of TA2-MATD3 is approximately 117–160 times that of the traditional methods.The convergence ratio of damage detection on the training set is approximately 97%,and that on the test set is in the range of 86.2%–91.9%.In addition,the main apparent damages found in the field survey were identified by TA2-MATD3.The results indicate that the proposed method can significantly improve the online computing efficiency and damage detection accuracy.This research can provide novel perspectives for the use of reinforcement learning methods to conduct damage detection in online structural health monitoring.展开更多
Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequen...Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequently changes its antigenicity through rapid mutations,leading to decreased vaccine efficacy or even failure.To improve vaccine effectiveness,it is necessary to monitor antigenic variation and update vaccine strains when significant antigenic variation occurs(Perofsky and Nelson,2020;Malik et al.,2024).展开更多
The development of digital twins for geotechnical structures necessitates the real-time updates of threedimensional(3D)virtual models(e.g.numerical finite element method(FEM)model)to accurately predict time-varying ge...The development of digital twins for geotechnical structures necessitates the real-time updates of threedimensional(3D)virtual models(e.g.numerical finite element method(FEM)model)to accurately predict time-varying geotechnical responses(e.g.consolidation settlement)in a 3D spatial domain.However,traditional 3D numerical model updating approaches are computationally prohibitive and therefore difficult to update the 3D responses in real time.To address these challenges,this study proposes a novel machine learning framework called sparse dictionary learning(T-3D-SDL)for real-time updating of time-varying 3D geotechnical responses.In T-3D-SDL,a concerned dataset(e.g.time-varying 3D settlement)is approximated as a linear superposition of dictionary atoms generated from 3D random FEM analyses.Field monitoring data are then used to identify non-trivial atoms and estimate their weights within a Bayesian framework for model updating and prediction.The proposed approach enables the real-time update of temporally varying settlements with a high 3D spatial resolution and quantified uncertainty as field monitoring data evolve.The proposed approach is illustrated using an embankment construction project.The results show that the proposed approach effectively improves settlement predictions along temporal and 3D spatial dimensions,with minimal latency(e.g.within minutes),as monitoring data appear.In addition,the proposed approach requires only a reasonably small number of 3D FEM model evaluations,avoids the use of widely adopted yet often criticized surrogate models,and effectively addresses the limitations(e.g.computational inefficiency)of existing 3D model updating approaches.展开更多
To overcome the default of single search tendency, the ants in the colony are divided into several sub-groups. The ants in different subgroups have different trail information and expectation coefficients. The simulat...To overcome the default of single search tendency, the ants in the colony are divided into several sub-groups. The ants in different subgroups have different trail information and expectation coefficients. The simulated annealing method is introduced to the algorithm. Through setting the temperature changing with the iterations, after each turn of tours, the solution set obtained by the ants is taken as the candidate set. The update set is obtained by adding the solutions in the candidate set to the previous update set with the probability determined by the temperature. The solutions in the candidate set are used to update the trail information. In each turn of updating, the current best solution is also used to enhance the trail information on the current best route. The trail information is reset when the algorithm is in stagnation state. The computer experiments demonstrate that the proposed algorithm has higher stability and convergence speed.展开更多
The imaging speed is a bottleneck for magnetic resonance imaging( MRI) since it appears. To alleviate this difficulty,a novel graph regularized sparse coding method for highly undersampled MRI reconstruction( GSCMRI) ...The imaging speed is a bottleneck for magnetic resonance imaging( MRI) since it appears. To alleviate this difficulty,a novel graph regularized sparse coding method for highly undersampled MRI reconstruction( GSCMRI) was proposed. The graph regularized sparse coding showed the potential in maintaining the geometrical information of the data. In this study, it was incorporated with two-level Bregman iterative procedure that updated the data term in outer-level and learned dictionary in innerlevel. Moreover,the graph regularized sparse coding and simple dictionary updating stages derived by the inner minimization made the proposed algorithm converge in few iterations, meanwhile achieving superior reconstruction performance. Extensive experimental results have demonstrated GSCMRI can consistently recover both real-valued MR images and complex-valued MR data efficiently,and outperform the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.展开更多
基金supported by the Natural Science Research Project of Colleges and Universities in Anhui Province (No.KJ2021A0479)the Science Research Program of Anhui University of Finance and Economics (No.ACKYC22082)。
文摘We present a gain adaptive tuning method for fiber Raman amplifier(FRA) using two-stage neural networks(NNs) and double weights updates. After training the connection weights of two-stage NNs separately in training phase, the connection weights of the unified NN are updated again in verification phase according to error between the predicted and target gains to eliminate the inherent error of the NNs. The simulation results show that the mean of root mean square error(RMSE) and maximum error of gains are 0.131 d B and 0.281 d B, respectively. It shows that the method can realize adaptive adjustment function of FRA gain with high accuracy.
文摘A novel memory efficient path metric update is proposed for Maximum A Posteriori(MAP) decoder of turbo codes to reduce the memory requirement of state metric information calcu-lation. For MAP decoder,the same memory can be shared by the forward and backward metrics with this metric update scheme. The forward and backward metrics update can be performed at the same time. And all of the extrinsic information can be calculated at the end of metric update. Therefore,the latency and area in the implementation will be reduced with the proposed metric update method.
文摘The location of model errors in a stiffness matrix by using test data has been investigated by the others.The present paper deals with the problem of updating stiffness elements in the erroneous areas. Firstly,a model that bears relation to erroneous elements only is derived.This model is termed local errors model,which reduces orders and computational loads compared with global stiffness matrix. Secondly,an inverse eigenvalue method is used to update model errors. The results of a numerical experiment demonstrate that the method is quite effective.
基金The China Hi-Tech R&D Program(863 Program) Project Number 2001AA602023
文摘The optimal matrix method and optimal elemental method used to update finite element models may not provide accurate results.This situation occurs when the test modal model is incomplete,as is often the case in practice.An improved optimal elemental method is presented that defines a new objective function,and as a byproduct,circumvents the need for mass normalized modal shapes,which are also not readily available in practice.To solve the group of nonlinear equations created by the improved optimal method,the Lagrange multiplier method and Matlab function fmincon are employed.To deal with actual complex structures, the float-encoding genetic algorithm(FGA)is introduced to enhance the capability of the improved method.Two examples,a 7- degree of freedom(DOF)mass-spring system and a 53-DOF planar frame,respectively,are updated using the improved method. The example results demonstrate the advantages of the improved method over existing optimal methods,and show that the genetic algorithm is an effective way to update the models used for actual complex structures.
基金supported by the Special Fund for Public Welfare(Meteorology)of China(Grants No.GYHY201006037 and GYHY200906007)
文摘A real-time channel flood forecast model was developed to simulate channel flow in plain rivers based on the dynamic wave theory. Taking into consideration channel shape differences along the channel, a roughness updating technique was developed using the Kalman filter method to update Manning's roughness coefficient at each time step of the calculation processes. Channel shapes were simplified as rectangles, triangles, and parabolas, and the relationships between hydraulic radius and water depth were developed for plain rivers. Based on the relationship between the Froude number and the inertia terms of the momentum equation in the Saint-Venant equations, the relationship between Manning's roughness coefficient and water depth was obtained. Using the channel of the Huaihe River from Wangjiaba to Lutaizi stations as a case, to test the performance and rationality of the present flood routing model, the original hydraulic model was compared with the developed model. Results show that the stage hydrographs calculated by the developed flood routing model with the updated Manning's roughness coefficient have a good agreement with the observed stage hydrographs. This model performs better than the original hydraulic model.
基金the National Natural Science Foundation of China(Nos.6136200161365013 and 51165033)+3 种基金the Natural Science Foundation of Jiangxi Province(Nos.20132BAB211030 and 20122BAB211015)the Technology Foundation of Department of Education in Jiangxi Province(Nos.GJJ 13061 and GJJ14196)the National Postdoctoral Research Funds(No.2014M551867)the Jiangxi Advanced Projects for Postdoctoral Research Funds(No.2014KY02)
文摘In recent years, it has shown that a generalized thresholding algorithm is useful for inverse problems with sparsity constraints. The generalized thresholding minimizes the non-convex p-norm based function with p < 1, and it penalizes small coefficients over a wider range meanwhile applies less bias to the larger coefficients.In this work, on the basis of two-level Bregman method with dictionary updating(TBMDU), we use the modified thresholding to minimize the non-convex function and propose the generalized TBMDU(GTBMDU) algorithm.The experimental results on magnetic resonance(MR) image simulations and real MR data, under a variety of sampling trajectories and acceleration factors, consistently demonstrate that the proposed algorithm can efficiently reconstruct the MR images and present advantages over the previous soft thresholding approaches.
基金Supported by Youth Teacher Education and Research Funds of Fujian(Grant No.JAT170911).
文摘This paper shows that the alternating direction method can be used to solve the structured inverse quadratic eigenvalue problem with symmetry, positive semi-definiteness and sparsity requirements. The results of numerical examples show that the proposed method works well.
基金the National Natural Science Foundation of China (No. 10772113)
文摘Most of exiting model updating methods based on the substructure matrices did not consider the effect of model reduction process on model updating which led to the updating results could not become more and more accurate with the improvement of the model reduction precision and the convergence rate was greatly reduced.In order to solve this problem,this paper analyses the basic reason about this problem,and proposes an improved model updating method of reduced-models,named as improved reduced cross-model cross-mode(IRCMCM) method.The proposed method eliminates the disadvantageous effect by adding a correction term to the model updating formula and employing an iterative process.The results obtained by the referenced method and IRCMCM method are compared by numerical examples of satellite's plates,which indicate the model updating results are more accurate by using the proposed method,and the model updating precision becomes better with the precision of the model reduction upgraded and the convergence rate is improved to a large extent at the same time.
基金open foundation of the Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanicsthe Open Foundation of Hubei Key Laboratory of Engineering Structural Analysis and Safety Assessment.
文摘A fluid-structure interaction approach is proposed in this paper based onNon-Ordinary State-Based Peridynamics(NOSB-PD)and Updated Lagrangian Particle Hydrodynamics(ULPH)to simulate the fluid-structure interaction problem with large geometric deformation and material failure and solve the fluid-structure interaction problem of Newtonian fluid.In the coupled framework,the NOSB-PD theory describes the deformation and fracture of the solid material structure.ULPH is applied to describe the flow of Newtonian fluids due to its advantages in computational accuracy.The framework utilizes the advantages of NOSB-PD theory for solving discontinuous problems and ULPH theory for solving fluid problems,with good computational stability and robustness.A fluidstructure coupling algorithm using pressure as the transmission medium is established to deal with the fluidstructure interface.The dynamic model of solid structure and the PD-ULPH fluid-structure interaction model involving large deformation are verified by numerical simulations.The results agree with the analytical solution,the available experimental data,and other numerical results.Thus,the accuracy and effectiveness of the proposed method in solving the fluid-structure interaction problem are demonstrated.The fluid-structure interactionmodel based on ULPH and NOSB-PD established in this paper provides a new idea for the numerical solution of fluidstructure interaction and a promising approach for engineering design and experimental prediction.
基金The work was supported by the National Natural Science Foundation of China(No.11571171)。
文摘The problem of correcting simultaneously mass and stiffness matrices of finite element model of undamped structural systems using vibration tests is considered in this paper.The desired matrix properties,including satisfaction of the characteristic equation,symmetry,positive semidefiniteness and sparsity,are imposed as side constraints to form the optimal matrix pencil approximation problem.Using partial Lagrangian multipliers,we transform the nonlinearly constrained optimization problem into an equivalent matrix linear variational inequality,develop a proximal point-like method for solving the matrix linear variational inequality,and analyze its global convergence.Numerical results are included to illustrate the performance and application of the proposed method.
基金support from the National Natural Science Foundations of China(Nos.11972267 and 11802214)the Open Foundation of the Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics and the Open Foundation of Hubei Key Laboratory of Engineering Structural Analysis and Safety Assessment.
文摘Natural convection is a heat transfer mechanism driven by temperature or density differences,leading to fluid motion without external influence.It occurs in various natural and engineering phenomena,influencing heat transfer,climate,and fluid mixing in industrial processes.This work aims to use the Updated Lagrangian Particle Hydrodynamics(ULPH)theory to address natural convection problems.The Navier-Stokes equation is discretized using second-order nonlocal differential operators,allowing a direct solution of the Laplace operator for temperature in the energy equation.Various numerical simulations,including cases such as natural convection in square cavities and two concentric cylinders,were conducted to validate the reliability of the model.The results demonstrate that the proposed model exhibits excellent accuracy and performance,providing a promising and effective numerical approach for natural convection problems.
基金supported by the Foundation for Innovative Research Groups of National Natural Science Foundation of China(No.51321065)the Foundation for Key Program of Natural Science Foundation of High Arch Dam(No.51339003)the National Basic Research Program of China(‘‘973’’Program,No.2013CB035904)
文摘In this paper, we prove the local and Supcrlinear convergence theorem of the column-updating method for n>2. This is an oped problem for the convergene theory of the column-updating method given by Martinez in the Intcrnational Conference of the NATO-ASI (Italy, 1994).
基金supported by National Key Research and Development Program of China(2023YFF0906100)National Natural Science Foundation of China(52408008)Key Research and Development Program of Jiangsu Province(BE2022833).
文摘Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real-time automated damage detection method by developing a theory-assisted adaptive mutiagent twin delayed deep deterministic(TA2-MATD3)policy gradient algorithm.First,the theoretical framework of reinforcement-learning-driven damage detection is established.To address the disadvantages of traditional mutiagent twin delayed deep deterministic(MATD3)method,the theory-assisted mechanism and the adaptive experience playback mechanism are introduced.Moreover,a historical residential house built in 1889 was taken as an example,using its 12-month structural health monitoring data.TA2-MATD3 was compared with existing damage detection methods in terms of the convergence ratio,online computing efficiency,and damage detection accuracy.The results show that the computational efficiency of TA2-MATD3 is approximately 117–160 times that of the traditional methods.The convergence ratio of damage detection on the training set is approximately 97%,and that on the test set is in the range of 86.2%–91.9%.In addition,the main apparent damages found in the field survey were identified by TA2-MATD3.The results indicate that the proposed method can significantly improve the online computing efficiency and damage detection accuracy.This research can provide novel perspectives for the use of reinforcement learning methods to conduct damage detection in online structural health monitoring.
基金upported by the Major Project of Guangzhou National Laboratory(GZNL2024A01002)National Key Plan for Scientific Research and Development of China(2022YFC2303802)+1 种基金National Natural Science Foundation of China(32170651&32370700)Hunan Provincial Natural Science Foundation of China(2024JJ2015).
文摘Dear Editor,Influenza viruses cause significant mortality and morbidity in humans.Vaccination is currently the most effective way to combat the virus(Perofsky and Nelson,2020).Unfortunately,the influenza virus frequently changes its antigenicity through rapid mutations,leading to decreased vaccine efficacy or even failure.To improve vaccine effectiveness,it is necessary to monitor antigenic variation and update vaccine strains when significant antigenic variation occurs(Perofsky and Nelson,2020;Malik et al.,2024).
基金supported by a grant from the Research Grant Council of Hong Kong Special Administrative Region(Project No.11207724).
文摘The development of digital twins for geotechnical structures necessitates the real-time updates of threedimensional(3D)virtual models(e.g.numerical finite element method(FEM)model)to accurately predict time-varying geotechnical responses(e.g.consolidation settlement)in a 3D spatial domain.However,traditional 3D numerical model updating approaches are computationally prohibitive and therefore difficult to update the 3D responses in real time.To address these challenges,this study proposes a novel machine learning framework called sparse dictionary learning(T-3D-SDL)for real-time updating of time-varying 3D geotechnical responses.In T-3D-SDL,a concerned dataset(e.g.time-varying 3D settlement)is approximated as a linear superposition of dictionary atoms generated from 3D random FEM analyses.Field monitoring data are then used to identify non-trivial atoms and estimate their weights within a Bayesian framework for model updating and prediction.The proposed approach enables the real-time update of temporally varying settlements with a high 3D spatial resolution and quantified uncertainty as field monitoring data evolve.The proposed approach is illustrated using an embankment construction project.The results show that the proposed approach effectively improves settlement predictions along temporal and 3D spatial dimensions,with minimal latency(e.g.within minutes),as monitoring data appear.In addition,the proposed approach requires only a reasonably small number of 3D FEM model evaluations,avoids the use of widely adopted yet often criticized surrogate models,and effectively addresses the limitations(e.g.computational inefficiency)of existing 3D model updating approaches.
基金Project supported by the National Natural Science Foundation of China (Grant No.50608069)
文摘To overcome the default of single search tendency, the ants in the colony are divided into several sub-groups. The ants in different subgroups have different trail information and expectation coefficients. The simulated annealing method is introduced to the algorithm. Through setting the temperature changing with the iterations, after each turn of tours, the solution set obtained by the ants is taken as the candidate set. The update set is obtained by adding the solutions in the candidate set to the previous update set with the probability determined by the temperature. The solutions in the candidate set are used to update the trail information. In each turn of updating, the current best solution is also used to enhance the trail information on the current best route. The trail information is reset when the algorithm is in stagnation state. The computer experiments demonstrate that the proposed algorithm has higher stability and convergence speed.
基金National Natural Science Foundations of China(Nos.61362001,61102043,61262084)Technology Foundations of Department of Education of Jiangxi Province,China(Nos.GJJ12006,GJJ14196)Natural Science Foundations of Jiangxi Province,China(Nos.20132BAB211030,20122BAB211015)
文摘The imaging speed is a bottleneck for magnetic resonance imaging( MRI) since it appears. To alleviate this difficulty,a novel graph regularized sparse coding method for highly undersampled MRI reconstruction( GSCMRI) was proposed. The graph regularized sparse coding showed the potential in maintaining the geometrical information of the data. In this study, it was incorporated with two-level Bregman iterative procedure that updated the data term in outer-level and learned dictionary in innerlevel. Moreover,the graph regularized sparse coding and simple dictionary updating stages derived by the inner minimization made the proposed algorithm converge in few iterations, meanwhile achieving superior reconstruction performance. Extensive experimental results have demonstrated GSCMRI can consistently recover both real-valued MR images and complex-valued MR data efficiently,and outperform the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.
