Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index ...Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.展开更多
In dynamic test,sampling rate is high and noise is strong,so a signal sparse decomposition method based on Gabor dictionary is put forward.This method iteratively decomposes the signal with the matching pursuit(MP)alg...In dynamic test,sampling rate is high and noise is strong,so a signal sparse decomposition method based on Gabor dictionary is put forward.This method iteratively decomposes the signal with the matching pursuit(MP)algorithm and takes the coherence ratio of the threshold as a condition of iteration termination.Standard MP algorithm is time-consuming,thus an adaptive genetic algorithm is introduced to MP method,which makes computation speed accelerate effectively.Experimental results indicate that this method not only can effectively remove high-frequency noise but also can compress the signal greatly.展开更多
To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individua...To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.展开更多
The real-time identification of dynamic parameters is importantfor the control system of spacecraft. The eigensystme realizationalgorithm (ERA) is currently the typical method for such applica-tion. In order to identi...The real-time identification of dynamic parameters is importantfor the control system of spacecraft. The eigensystme realizationalgorithm (ERA) is currently the typical method for such applica-tion. In order to identify the dynamic parameter of spacecraftrapidly and accurately, an accelerated ERA with a partial singularvalues decomposition (PSVD) algorithm is presented. In the PSVD, theHankel matrix is reduced to dual diagonal form first, and thentransformed into a tridiagonal matrix.展开更多
Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model base...Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.展开更多
Gesture recognition utilizing flexible strain sensors is a highly valuable technology widely applied in human-machine interfaces.However,achieving rapid detection of subtle motions and timely processing of dynamic sig...Gesture recognition utilizing flexible strain sensors is a highly valuable technology widely applied in human-machine interfaces.However,achieving rapid detection of subtle motions and timely processing of dynamic signals remain a challenge for sensors.Here,highly resilient and durable ionogels are developed by introducing micro-scale incompatible phases in macroscopic homogeneous polymeric network.The compatible network disperses in conductive ionic liquid to form highly resilient and stretchable skeleton,while incompatible phase forms hydrogen bonds to dissipate energy thus strengthening the ionogels.The ionogels-derived strain sensors show highly sensitivity,fast response time(<10 ms),low detection limit(~50μm),and remarkable durability(>5000 cycles),allowing for precise monitoring of human motions.More importantly,a self-adaptive recognition program empowered by deep-learning algorithms is designed to compensate for sensors,creating a comprehensive system capable of dynamic gesture recognition.This system can comprehensively analyze both the temporal and spatial features of sensor data,enabling deeper understanding of the dynamic process underlying gestures.The system accurately classifies 10 hand gestures across five participants with impressive accuracy of 93.66%.Moreover,it maintains robust recognition performance without the need for further training even when different sensors or subjects are involved.This technological breakthrough paves the way for intuitive and seamless interaction between humans and machines,presenting significant opportunities in diverse applications,such as human-robot interaction,virtual reality control,and assistive devices for the disabled individuals.展开更多
The spatial domain of Molecular Dynamics simulations is usually a regular box that can be easily divided in subdomains for parallel processing.Recent efforts aimed at simulating complex biological systems,like the blo...The spatial domain of Molecular Dynamics simulations is usually a regular box that can be easily divided in subdomains for parallel processing.Recent efforts aimed at simulating complex biological systems,like the blood flow inside arteries,require the execution of Parallel Molecular Dynamics(PMD)in vessels that have,by nature,an irregular shape.In those cases,the geometry of the domain becomes an additional input parameter that directly influences the outcome of the simulation.In this paper we discuss the problems due to the parallelization of MD in complex geometries and show an efficient and general method to perform MD in irregular domains.展开更多
Parametric dynamical systems are widely used to model physical systems,but their numerical simulation can be computationally demanding due to nonlinearity,long-time simulation,and multi-query requirements.Model reduct...Parametric dynamical systems are widely used to model physical systems,but their numerical simulation can be computationally demanding due to nonlinearity,long-time simulation,and multi-query requirements.Model reduction methods aim to reduce computation complexity and improve simulation efficiency.However,traditional model reduction methods are inefficient for parametric dynamical systems with nonlinear structures.To address this challenge,we propose an adaptive method based on local dynamic mode decomposition(DMD)to construct an efficient and reliable surrogate model.We propose an improved greedy algorithm to generate the atoms setΘbased on a sequence of relatively small training sets,which could reduce the effect of large training set.At each enrichment step,we construct a local sub-surrogate model using the Taylor expansion and DMD,resulting in the ability to predict the state at any time without solving the original dynamical system.Moreover,our method provides the best approximation almost everywhere over the parameter domain with certain smoothness assumptions,thanks to the gradient information.At last,three concrete examples are presented to illustrate the effectiveness of the proposed method.展开更多
基金Project(61273187)supported by the National Natural Science Foundation of ChinaProject(61321003)supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China
文摘Region partition(RP) is the key technique to the finite element parallel computing(FEPC),and its performance has a decisive influence on the entire process of analysis and computation.The performance evaluation index of RP method for the three-dimensional finite element model(FEM) has been given.By taking the electric field of aluminum reduction cell(ARC) as the research object,the performance of two classical RP methods,which are Al-NASRA and NGUYEN partition(ANP) algorithm and the multi-level partition(MLP) method,has been analyzed and compared.The comparison results indicate a sound performance of ANP algorithm,but to large-scale models,the computing time of ANP algorithm increases notably.This is because the ANP algorithm determines only one node based on the minimum weight and just adds the elements connected to the node into the sub-region during each iteration.To obtain the satisfied speed and the precision,an improved dynamic self-adaptive ANP(DSA-ANP) algorithm has been proposed.With consideration of model scale,complexity and sub-RP stage,the improved algorithm adaptively determines the number of nodes and selects those nodes with small enough weight,and then dynamically adds these connected elements.The proposed algorithm has been applied to the finite element analysis(FEA) of the electric field simulation of ARC.Compared with the traditional ANP algorithm,the computational efficiency of the proposed algorithm has been shortened approximately from 260 s to 13 s.This proves the superiority of the improved algorithm on computing time performance.
文摘In dynamic test,sampling rate is high and noise is strong,so a signal sparse decomposition method based on Gabor dictionary is put forward.This method iteratively decomposes the signal with the matching pursuit(MP)algorithm and takes the coherence ratio of the threshold as a condition of iteration termination.Standard MP algorithm is time-consuming,thus an adaptive genetic algorithm is introduced to MP method,which makes computation speed accelerate effectively.Experimental results indicate that this method not only can effectively remove high-frequency noise but also can compress the signal greatly.
基金Project(2013CB733600) supported by the National Basic Research Program of ChinaProject(21176073) supported by the National Natural Science Foundation of China+2 种基金Project(20090074110005) supported by Doctoral Fund of Ministry of Education of ChinaProject(NCET-09-0346) supported by Program for New Century Excellent Talents in University of ChinaProject(09SG29) supported by "Shu Guang", China
文摘To solve dynamic optimization problem of chemical process (CPDOP), a hybrid differential evolution algorithm, which is integrated with Alopex and named as Alopex-DE, was proposed. In Alopex-DE, each original individual has its own symbiotic individual, which consists of control parameters. Differential evolution operator is applied for the original individuals to search the global optimization solution. Alopex algorithm is used to co-evolve the symbiotic individuals during the original individual evolution and enhance the fitness of the original individuals. Thus, control parameters are self-adaptively adjusted by Alopex to obtain the real-time optimum values for the original population. To illustrate the whole performance of Alopex-DE, several varietal DEs were applied to optimize 13 benchmark functions. The results show that the whole performance of Alopex-DE is the best. Further, Alopex-DE was applied to solve 4 typical CPDOPs, and the effect of the discrete time degree on the optimization solution was analyzed. The satisfactory result is obtained.
文摘The real-time identification of dynamic parameters is importantfor the control system of spacecraft. The eigensystme realizationalgorithm (ERA) is currently the typical method for such applica-tion. In order to identify the dynamic parameter of spacecraftrapidly and accurately, an accelerated ERA with a partial singularvalues decomposition (PSVD) algorithm is presented. In the PSVD, theHankel matrix is reduced to dual diagonal form first, and thentransformed into a tridiagonal matrix.
文摘Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.
基金supported by the National Key Research and Development Program of China(No.2021YFA1401103)the National Natural Science Foundation of China(Nos.61825403,61921005,and 82370520).
文摘Gesture recognition utilizing flexible strain sensors is a highly valuable technology widely applied in human-machine interfaces.However,achieving rapid detection of subtle motions and timely processing of dynamic signals remain a challenge for sensors.Here,highly resilient and durable ionogels are developed by introducing micro-scale incompatible phases in macroscopic homogeneous polymeric network.The compatible network disperses in conductive ionic liquid to form highly resilient and stretchable skeleton,while incompatible phase forms hydrogen bonds to dissipate energy thus strengthening the ionogels.The ionogels-derived strain sensors show highly sensitivity,fast response time(<10 ms),low detection limit(~50μm),and remarkable durability(>5000 cycles),allowing for precise monitoring of human motions.More importantly,a self-adaptive recognition program empowered by deep-learning algorithms is designed to compensate for sensors,creating a comprehensive system capable of dynamic gesture recognition.This system can comprehensively analyze both the temporal and spatial features of sensor data,enabling deeper understanding of the dynamic process underlying gestures.The system accurately classifies 10 hand gestures across five participants with impressive accuracy of 93.66%.Moreover,it maintains robust recognition performance without the need for further training even when different sensors or subjects are involved.This technological breakthrough paves the way for intuitive and seamless interaction between humans and machines,presenting significant opportunities in diverse applications,such as human-robot interaction,virtual reality control,and assistive devices for the disabled individuals.
文摘The spatial domain of Molecular Dynamics simulations is usually a regular box that can be easily divided in subdomains for parallel processing.Recent efforts aimed at simulating complex biological systems,like the blood flow inside arteries,require the execution of Parallel Molecular Dynamics(PMD)in vessels that have,by nature,an irregular shape.In those cases,the geometry of the domain becomes an additional input parameter that directly influences the outcome of the simulation.In this paper we discuss the problems due to the parallelization of MD in complex geometries and show an efficient and general method to perform MD in irregular domains.
基金support by National Key R&D Program of China(No.2021YFA1001300)National Natural Science Foundation of China(Nos.12288101,12271150,12101216)+2 种基金the Hunan Provincial Natural Science Foundation of China(No.2022JJ40030)the Jiangsu Provincial Natural Science Foundation of China(No.BK20230346)Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(No.NY222063).
文摘Parametric dynamical systems are widely used to model physical systems,but their numerical simulation can be computationally demanding due to nonlinearity,long-time simulation,and multi-query requirements.Model reduction methods aim to reduce computation complexity and improve simulation efficiency.However,traditional model reduction methods are inefficient for parametric dynamical systems with nonlinear structures.To address this challenge,we propose an adaptive method based on local dynamic mode decomposition(DMD)to construct an efficient and reliable surrogate model.We propose an improved greedy algorithm to generate the atoms setΘbased on a sequence of relatively small training sets,which could reduce the effect of large training set.At each enrichment step,we construct a local sub-surrogate model using the Taylor expansion and DMD,resulting in the ability to predict the state at any time without solving the original dynamical system.Moreover,our method provides the best approximation almost everywhere over the parameter domain with certain smoothness assumptions,thanks to the gradient information.At last,three concrete examples are presented to illustrate the effectiveness of the proposed method.
文摘提出了一种新颖的基于本征正交分解(proper orthogonal decomposition,POD)的多重多级子结构方法.该方法在传统静凝聚(将内部自由度降阶至边界主自由度)的基础上,引入了两级独立的POD降阶.首先,构建低阶振动模态和基于POD的高阶近似模态共同作为降阶基底,分别用于近似静凝聚中的数值基函数(约束模态)和缩减后的内部动力学行为,以显著降低存储需求.其次,也是本方法实现子结构高效拼接的关键,即对所有子结构的边界降阶模态施加奇异值分解(singular value decomposition,SVD),从而生成一组公共的正交界面基底.该基底确保了所有子结构的边界变形能在同一线性空间内表达,极大简化了组装过程并提升了计算速度.此外,还探讨了针对复杂拓扑边界的降阶处理办法,以及如何消除刚体模态对应的零特征值对计算稳定性的影响.通过对算法复杂度的定量分析表明,本方法在空间和时间复杂度上均优于传统子结构法.最后的数值算例证实,方法的计算精度和效率随着所采用正交基数量的增加而稳定提升,展现了其良好的收敛性与可靠性.
