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
We present a new algorithm for the fast expansion of rational numbers into continued fractions. This algorithm permits to compute the complete set of integer Euler numbers of the sophisticate tree graph manifolds, whi...We present a new algorithm for the fast expansion of rational numbers into continued fractions. This algorithm permits to compute the complete set of integer Euler numbers of the sophisticate tree graph manifolds, which we used to simulate the coupling constant hierarchy for the universe with five fundamental interactions. Moreover, we can explicitly compute the integer Laplacian block matrix associated with any tree plumbing graph. This matrix coincides up to sign with the integer linking matrix (the main topological invariant) of the graph manifold corresponding to the plumbing graph. The need for a special algorithm appeared during computations of these topological invariants of complicated graph manifolds since there emerged a set of special rational numbers (fractions) with huge numerators and denominators;for these rational numbers, the ordinary methods of expansion in continued fraction became unusable.展开更多
This part II-C of our work completes the factorizational theory of asymptotic expansions in the real domain. Here we present two algorithms for constructing canonical factorizations of a disconjugate operator starting...This part II-C of our work completes the factorizational theory of asymptotic expansions in the real domain. Here we present two algorithms for constructing canonical factorizations of a disconjugate operator starting from a basis of its kernel which forms a Chebyshev asymptotic scale at an endpoint. These algorithms arise quite naturally in our asymptotic context and prove very simple in special cases and/or for scales with a small numbers of terms. All the results in the three Parts of this work are well illustrated by a class of asymptotic scales featuring interesting properties. Examples and counterexamples complete the exposition.展开更多
A creepy photoelectric endoscopy system with good performance is studied, and anexpansion and correction algorithm for a compressed photoelectric image with serious geometricdistortion is presented. The algorithm can ...A creepy photoelectric endoscopy system with good performance is studied, and anexpansion and correction algorithm for a compressed photoelectric image with serious geometricdistortion is presented. The algorithm can not only correct the geometric distortion, but alsorestore the gray-level distribution by means of ternary convolution algorithm. The details andthe outline in the image are very clear. It is proved to be of high performance in practice.展开更多
铁路光传送网络是高速铁路地面基础设施的神经中枢,为避免网络故障给铁路运营带来巨大损失,重点研究光传送网P-Cycle(Pre-configured Cycle)保护技术,提出在圈扩展时以所有候选圈上未保护工作容量的方差、冗余度两个指标为比较标准的RVP...铁路光传送网络是高速铁路地面基础设施的神经中枢,为避免网络故障给铁路运营带来巨大损失,重点研究光传送网P-Cycle(Pre-configured Cycle)保护技术,提出在圈扩展时以所有候选圈上未保护工作容量的方差、冗余度两个指标为比较标准的RVPA(Redundancy and Variance Based P-Cycle Construction Algorithm)算法。圈扩展的过程中,算法将选择方差与冗余度能同时满足条件的候选圈作为本轮扩展圈,有效限制了完成保护的P-Cycle圈个数;圈扩展停止条件中,当UPL与参数M、冗余度的大小关系满足条件时,则停止圈扩展,从而限制圈上节点数,使圈个数与圈长度得到有效均衡;在仿真过程中,利用泛欧网络拓扑COST239对RVPA算法进行仿真,并对比分析不同M值下的性能。仿真结果表明,在相同空闲资源与待保护工作容量设定下,参数M取0.5时效果最优,并且RVPA算法的保护容量效率、所需圈的个数、算法整体耗时、总冗余度均优于已有的POCA(P-Cycle Optimization Configuration Heuristic Algorithm)算法。展开更多
In this paper,the problem of high mobility channel estimation in the Long-Term Evolution for Railway(LTE-R)communication system is investigated.By using a Basis Expansion Model(BEM),the channel impulse response is mod...In this paper,the problem of high mobility channel estimation in the Long-Term Evolution for Railway(LTE-R)communication system is investigated.By using a Basis Expansion Model(BEM),the channel impulse response is modeled as the sum of several basis functions multiplied by coefficients.By estimating the basis function coefficients,the fast time-varying channel can be approximated.In order to reduce the estimation error resulting from the high frequency basis function,the Generalized Complex Exponential BEM(GCE-BEM)is modified to form an Improved GCE-BEM(IGCE-BEM)by adding a correction coefficient to the basis function.Moreover,an Improved Baseline Tilting(IBT)method is proposed to reduce the Gibbs effect.In addition,linear interpolation,Gauss interpolation,and three-order Hermite interpolation are adopted to obtain the channel impulse response at non pilot locations based on the channel estimation results at pilot positions.The simulation results show that the IGCE-BEM outperforms the CE-BEM and GCE-BEM in terms of the Normalized Mean Squared Error(NMSE).The IB T method is better than the BT method in reducing the Gibbs effect.In addition,combined with the IBT,the IGCE-BEM also has low NMSE under high moving speed and high noise power.The performance of the threeorder Hermite interpolation method is higher than that of the linear interpolation and Gauss interpolation approaches.展开更多
A variation of the direct Taylor expansion algorithm is suggested and applied to several linear and nonlinear differential equations of interest in physics and engineering, and the results are compared with those obta...A variation of the direct Taylor expansion algorithm is suggested and applied to several linear and nonlinear differential equations of interest in physics and engineering, and the results are compared with those obtained from other algorithms. It is shown that the suggested algorithm competes strongly with other existing algorithms, both in accuracy and ease of application, while demanding a shorter computation time.展开更多
Investigating flexibility and stability boosting transmission expansion planning(TEP)methods can increase the renewable energy(RE)consumption of the power systems.In this study,we propose a bi-level TEP method for vol...Investigating flexibility and stability boosting transmission expansion planning(TEP)methods can increase the renewable energy(RE)consumption of the power systems.In this study,we propose a bi-level TEP method for voltage-source-converter-based direct current(VSC-DC),focusing on flexibility and stability enhancement.First,we established the TEP framework of VSC-DC,by introducing the evaluation indices to quantify the power system flexibility and stability.Subsequently,we propose a bi-level VSC-DC TEP model:the upper-level model acquires the optimal VSC-DC planning scheme by using the improved moth flame optimization(IMFO)algorithm,and the lower-level model evaluates the flexibility through time-series production simulation.Finally,we applied the proposedVSC-DC TEPmethod to the modified IEEE-24 and IEEE-39 test systems,and obtained the optimalVSCDC planning schemes.The results verified that the proposed method can achieve excellent RE curtailment with high flexibility and stability.Furthermore,the well-designed IMFO algorithm outperformed the traditional particle swarm optimization(PSO)and moth flame optimization(MFO)algorithms,confirming the effectiveness of the proposed approach.展开更多
This study presents a novel hybrid topology optimization and mold design framework that integrates process fitting,runner system optimization,and structural analysis to significantly enhance the performance of injecti...This study presents a novel hybrid topology optimization and mold design framework that integrates process fitting,runner system optimization,and structural analysis to significantly enhance the performance of injection-molded parts.At its core,the framework employs a greedy algorithm that generates runner systems based on adjacency and shortest path principles,leading to improvements in both mechanical strength and material efficiency.The design optimization is validated through a series of rigorous experimental tests,including three-point bending and torsion tests performed on key-socket frames,ensuring that the optimized designs meet practical performance requirements.A critical innovation of the framework is the development of the Adjacent Element Temperature-Driven Prestress Algorithm(AETDPA),which refines the prediction of mechanical failure and strength fitting.This algorithm has been shown to deliver mesh-independent accuracy,thereby enhancing the reliability of simulation results across various design iterations.The framework’s adaptability is further demonstrated by its ability to adjust optimization methods based on the unique geometry of each part,thus accelerating the overall design process while ensuring struc-tural integrity.In addition to its immediate applications in injection molding,the study explores the potential extension of this framework to metal additive manufacturing,opening new avenues for its use in advanced manufacturing technologies.Numerical simulations,including finite element analysis,support the experimental findings and confirm that the optimized designs provide a balanced combination of strength,durability,and efficiency.Furthermore,the integration challenges with existing injection molding practices are addressed,underscoring the framework’s scalability and industrial relevance.Overall,this hybrid topology optimization framework offers a computationally efficient and robust solution for advanced manufacturing applications,promising significant improvements in design efficiency,cost-effectiveness,and product performance.Future work will focus on further enhancing algorithm robustness and exploring additional applications across diverse manufacturing processes.展开更多
针对非整数阶盲移频干扰生成的欺骗干扰效果单一,假目标分布规律等问题,提出了一种基于阶梯波盲移频的线性调频(Linear Frequency Modulation,LFM)雷达干扰。设计了均匀阶梯波和非均匀阶梯波两种移频方法,推导了基于阶梯波盲移频的LFM...针对非整数阶盲移频干扰生成的欺骗干扰效果单一,假目标分布规律等问题,提出了一种基于阶梯波盲移频的线性调频(Linear Frequency Modulation,LFM)雷达干扰。设计了均匀阶梯波和非均匀阶梯波两种移频方法,推导了基于阶梯波盲移频的LFM雷达干扰数学模型。该技术引入阶梯波函数对系统阶数进行调制,在系统延时不变的情况下,可以实现精确位置的欺骗假目标群或固定范围内的随机假目标。通过调整阶梯波函数的各项参数,可以完成对假目标位置、幅度和数量的控制。仿真验证表明,采用均匀阶梯波函数可以实现特定位置的密集假目标,采用非均匀阶梯波函数能够在特定范围内生成随机的假目标。对调频斜率捷变的雷达有着较强的干扰能力,具有较好的工程应用意义。展开更多
Multilayer networks,as an important branch of network science,have become a powerful tool for revealing and analyzing the internal structures of complex systems.Within these networks,community detection is particularl...Multilayer networks,as an important branch of network science,have become a powerful tool for revealing and analyzing the internal structures of complex systems.Within these networks,community detection is particularly crucial,as it assists in uncovering hidden patterns within the network.We construct a seed node selection method based on the local structural characteristics of network nodes and,by integrating deep learning methods,establish a local information expansion strategy.This approach effectively identifies and expands community boundaries,developing a novel multilayer network community detection algorithm—the Layered Information Expansion Detection Algorithm(LIEDA).Its exceptional performance has been experimentally verified using multiple real-world datasets.Compared with existing technologies,the LIEDA has considerable accuracy,stability,and adaptability advantages.Compared with various popular benchmark algorithms,the model has substantially improved multiple evaluation metrics across several authoritative public and synthetic datasets.展开更多
Active constellation expansion(ACE) and iterative clipping and filtering(ICF) are simple and effective techniques for reducing the peak-to-average ratio(PAPR) in coherent optical orthogonal frequency division multiple...Active constellation expansion(ACE) and iterative clipping and filtering(ICF) are simple and effective techniques for reducing the peak-to-average ratio(PAPR) in coherent optical orthogonal frequency division multiplexing(CO-OFDM) systems, but effective PAPR suppression requires a lot of iterations. To overcome this shortcoming, a joint algorithm based on improved active constellation expansion(IACE) and ICF(IACE-ICF) is proposed. The simulation results show that at the complementary cumulative distribution function(CCDF) of 10-4, the PAPR of IACE-ICF(G=4, iter=4) algorithm is optimized by 1.507 d B, 1.13 d B and 0.204 d B compared with that of the IACE, ICF(iter=4) and ICF-IACE(G=4, iter=4) algorithms, respectively. Meanwhile, when the bit error rate(BER) is 10-3, the optical signal to noise ratio(OSNR) of the proposed scheme is optimized by 2.04 d B, 1.75 d B and 1.4 d B compared with that of clipping, ICF(iter=4) and ICF-IACE(G=4, iter=4) algorithms, respectively. On the other hand, the proposed scheme can reduce the number of complex multiplications by 14.29% and complex additions by 28.57% compared with the ICF(iter=14) scheme.展开更多
It takes more time and is easier to fall into the local minimum value when using the traditional full-supervised learning algorithm to train RBFNN. Therefore, the paper proposes one algorithm to determine the RBFNN’s...It takes more time and is easier to fall into the local minimum value when using the traditional full-supervised learning algorithm to train RBFNN. Therefore, the paper proposes one algorithm to determine the RBFNN’s data center based on the improvement density method. First it uses the improved density method to select RBFNN’s data center, and calculates the expansion constant of each center, then only trains the network weight with the gradient descent method. To compare this method with full-supervised gradient descent method, the time not only has obvious reduction (including to choose data center’s time by density method), but also obtains better classification results when using the data set in UCI to carry on the test to the network.展开更多
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.
文摘We present a new algorithm for the fast expansion of rational numbers into continued fractions. This algorithm permits to compute the complete set of integer Euler numbers of the sophisticate tree graph manifolds, which we used to simulate the coupling constant hierarchy for the universe with five fundamental interactions. Moreover, we can explicitly compute the integer Laplacian block matrix associated with any tree plumbing graph. This matrix coincides up to sign with the integer linking matrix (the main topological invariant) of the graph manifold corresponding to the plumbing graph. The need for a special algorithm appeared during computations of these topological invariants of complicated graph manifolds since there emerged a set of special rational numbers (fractions) with huge numerators and denominators;for these rational numbers, the ordinary methods of expansion in continued fraction became unusable.
文摘This part II-C of our work completes the factorizational theory of asymptotic expansions in the real domain. Here we present two algorithms for constructing canonical factorizations of a disconjugate operator starting from a basis of its kernel which forms a Chebyshev asymptotic scale at an endpoint. These algorithms arise quite naturally in our asymptotic context and prove very simple in special cases and/or for scales with a small numbers of terms. All the results in the three Parts of this work are well illustrated by a class of asymptotic scales featuring interesting properties. Examples and counterexamples complete the exposition.
文摘A creepy photoelectric endoscopy system with good performance is studied, and anexpansion and correction algorithm for a compressed photoelectric image with serious geometricdistortion is presented. The algorithm can not only correct the geometric distortion, but alsorestore the gray-level distribution by means of ternary convolution algorithm. The details andthe outline in the image are very clear. It is proved to be of high performance in practice.
文摘铁路光传送网络是高速铁路地面基础设施的神经中枢,为避免网络故障给铁路运营带来巨大损失,重点研究光传送网P-Cycle(Pre-configured Cycle)保护技术,提出在圈扩展时以所有候选圈上未保护工作容量的方差、冗余度两个指标为比较标准的RVPA(Redundancy and Variance Based P-Cycle Construction Algorithm)算法。圈扩展的过程中,算法将选择方差与冗余度能同时满足条件的候选圈作为本轮扩展圈,有效限制了完成保护的P-Cycle圈个数;圈扩展停止条件中,当UPL与参数M、冗余度的大小关系满足条件时,则停止圈扩展,从而限制圈上节点数,使圈个数与圈长度得到有效均衡;在仿真过程中,利用泛欧网络拓扑COST239对RVPA算法进行仿真,并对比分析不同M值下的性能。仿真结果表明,在相同空闲资源与待保护工作容量设定下,参数M取0.5时效果最优,并且RVPA算法的保护容量效率、所需圈的个数、算法整体耗时、总冗余度均优于已有的POCA(P-Cycle Optimization Configuration Heuristic Algorithm)算法。
基金the National Natural Science Foundation of China(No.U1405251,No.61401100,No.61601126,and No.61571129)the Natural Science Foundation of Fujian Province(No.2015J05122).
文摘In this paper,the problem of high mobility channel estimation in the Long-Term Evolution for Railway(LTE-R)communication system is investigated.By using a Basis Expansion Model(BEM),the channel impulse response is modeled as the sum of several basis functions multiplied by coefficients.By estimating the basis function coefficients,the fast time-varying channel can be approximated.In order to reduce the estimation error resulting from the high frequency basis function,the Generalized Complex Exponential BEM(GCE-BEM)is modified to form an Improved GCE-BEM(IGCE-BEM)by adding a correction coefficient to the basis function.Moreover,an Improved Baseline Tilting(IBT)method is proposed to reduce the Gibbs effect.In addition,linear interpolation,Gauss interpolation,and three-order Hermite interpolation are adopted to obtain the channel impulse response at non pilot locations based on the channel estimation results at pilot positions.The simulation results show that the IGCE-BEM outperforms the CE-BEM and GCE-BEM in terms of the Normalized Mean Squared Error(NMSE).The IB T method is better than the BT method in reducing the Gibbs effect.In addition,combined with the IBT,the IGCE-BEM also has low NMSE under high moving speed and high noise power.The performance of the threeorder Hermite interpolation method is higher than that of the linear interpolation and Gauss interpolation approaches.
文摘A variation of the direct Taylor expansion algorithm is suggested and applied to several linear and nonlinear differential equations of interest in physics and engineering, and the results are compared with those obtained from other algorithms. It is shown that the suggested algorithm competes strongly with other existing algorithms, both in accuracy and ease of application, while demanding a shorter computation time.
基金supported by the Science and Technology Project of Central China Branch of State Grid Corporation of China under Grant 52140023000T.
文摘Investigating flexibility and stability boosting transmission expansion planning(TEP)methods can increase the renewable energy(RE)consumption of the power systems.In this study,we propose a bi-level TEP method for voltage-source-converter-based direct current(VSC-DC),focusing on flexibility and stability enhancement.First,we established the TEP framework of VSC-DC,by introducing the evaluation indices to quantify the power system flexibility and stability.Subsequently,we propose a bi-level VSC-DC TEP model:the upper-level model acquires the optimal VSC-DC planning scheme by using the improved moth flame optimization(IMFO)algorithm,and the lower-level model evaluates the flexibility through time-series production simulation.Finally,we applied the proposedVSC-DC TEPmethod to the modified IEEE-24 and IEEE-39 test systems,and obtained the optimalVSCDC planning schemes.The results verified that the proposed method can achieve excellent RE curtailment with high flexibility and stability.Furthermore,the well-designed IMFO algorithm outperformed the traditional particle swarm optimization(PSO)and moth flame optimization(MFO)algorithms,confirming the effectiveness of the proposed approach.
文摘This study presents a novel hybrid topology optimization and mold design framework that integrates process fitting,runner system optimization,and structural analysis to significantly enhance the performance of injection-molded parts.At its core,the framework employs a greedy algorithm that generates runner systems based on adjacency and shortest path principles,leading to improvements in both mechanical strength and material efficiency.The design optimization is validated through a series of rigorous experimental tests,including three-point bending and torsion tests performed on key-socket frames,ensuring that the optimized designs meet practical performance requirements.A critical innovation of the framework is the development of the Adjacent Element Temperature-Driven Prestress Algorithm(AETDPA),which refines the prediction of mechanical failure and strength fitting.This algorithm has been shown to deliver mesh-independent accuracy,thereby enhancing the reliability of simulation results across various design iterations.The framework’s adaptability is further demonstrated by its ability to adjust optimization methods based on the unique geometry of each part,thus accelerating the overall design process while ensuring struc-tural integrity.In addition to its immediate applications in injection molding,the study explores the potential extension of this framework to metal additive manufacturing,opening new avenues for its use in advanced manufacturing technologies.Numerical simulations,including finite element analysis,support the experimental findings and confirm that the optimized designs provide a balanced combination of strength,durability,and efficiency.Furthermore,the integration challenges with existing injection molding practices are addressed,underscoring the framework’s scalability and industrial relevance.Overall,this hybrid topology optimization framework offers a computationally efficient and robust solution for advanced manufacturing applications,promising significant improvements in design efficiency,cost-effectiveness,and product performance.Future work will focus on further enhancing algorithm robustness and exploring additional applications across diverse manufacturing processes.
文摘针对非整数阶盲移频干扰生成的欺骗干扰效果单一,假目标分布规律等问题,提出了一种基于阶梯波盲移频的线性调频(Linear Frequency Modulation,LFM)雷达干扰。设计了均匀阶梯波和非均匀阶梯波两种移频方法,推导了基于阶梯波盲移频的LFM雷达干扰数学模型。该技术引入阶梯波函数对系统阶数进行调制,在系统延时不变的情况下,可以实现精确位置的欺骗假目标群或固定范围内的随机假目标。通过调整阶梯波函数的各项参数,可以完成对假目标位置、幅度和数量的控制。仿真验证表明,采用均匀阶梯波函数可以实现特定位置的密集假目标,采用非均匀阶梯波函数能够在特定范围内生成随机的假目标。对调频斜率捷变的雷达有着较强的干扰能力,具有较好的工程应用意义。
基金supported by the National Natural Science Foundation of China(Nos.62272311 and 62102262).
文摘Multilayer networks,as an important branch of network science,have become a powerful tool for revealing and analyzing the internal structures of complex systems.Within these networks,community detection is particularly crucial,as it assists in uncovering hidden patterns within the network.We construct a seed node selection method based on the local structural characteristics of network nodes and,by integrating deep learning methods,establish a local information expansion strategy.This approach effectively identifies and expands community boundaries,developing a novel multilayer network community detection algorithm—the Layered Information Expansion Detection Algorithm(LIEDA).Its exceptional performance has been experimentally verified using multiple real-world datasets.Compared with existing technologies,the LIEDA has considerable accuracy,stability,and adaptability advantages.Compared with various popular benchmark algorithms,the model has substantially improved multiple evaluation metrics across several authoritative public and synthetic datasets.
基金supported by the Natural Science Foundation of Tianjin(No.18JCYBJC86300)。
文摘Active constellation expansion(ACE) and iterative clipping and filtering(ICF) are simple and effective techniques for reducing the peak-to-average ratio(PAPR) in coherent optical orthogonal frequency division multiplexing(CO-OFDM) systems, but effective PAPR suppression requires a lot of iterations. To overcome this shortcoming, a joint algorithm based on improved active constellation expansion(IACE) and ICF(IACE-ICF) is proposed. The simulation results show that at the complementary cumulative distribution function(CCDF) of 10-4, the PAPR of IACE-ICF(G=4, iter=4) algorithm is optimized by 1.507 d B, 1.13 d B and 0.204 d B compared with that of the IACE, ICF(iter=4) and ICF-IACE(G=4, iter=4) algorithms, respectively. Meanwhile, when the bit error rate(BER) is 10-3, the optical signal to noise ratio(OSNR) of the proposed scheme is optimized by 2.04 d B, 1.75 d B and 1.4 d B compared with that of clipping, ICF(iter=4) and ICF-IACE(G=4, iter=4) algorithms, respectively. On the other hand, the proposed scheme can reduce the number of complex multiplications by 14.29% and complex additions by 28.57% compared with the ICF(iter=14) scheme.
文摘It takes more time and is easier to fall into the local minimum value when using the traditional full-supervised learning algorithm to train RBFNN. Therefore, the paper proposes one algorithm to determine the RBFNN’s data center based on the improvement density method. First it uses the improved density method to select RBFNN’s data center, and calculates the expansion constant of each center, then only trains the network weight with the gradient descent method. To compare this method with full-supervised gradient descent method, the time not only has obvious reduction (including to choose data center’s time by density method), but also obtains better classification results when using the data set in UCI to carry on the test to the network.
