An auxiliary principle technique to study a class of generalized set-valued strongly nonlinear mixed variational-like inequalities is extended. The existence and uniqueness of the solution of the auxiliary problem for...An auxiliary principle technique to study a class of generalized set-valued strongly nonlinear mixed variational-like inequalities is extended. The existence and uniqueness of the solution of the auxiliary problem for the generalized set-valued strongly nonlinear mixed variational-like inequalities are proved, a novel and innovative three-step iterative algorithm to compute approximate solution is constructed, and the existence of the solution of the generalized set-valued strongly nonlinear mixed variational-like inequality is shown using the auxiliary principle iterative sequences generated by the algorithm technique. The convergence of three-step is also proved.展开更多
The purpose of this paper is to investigate some sufficient and necessary conditions for three-step Ishikawa iterative sequences with error terms for uniformly quasi-Lipschitzian mappings to converge to fixed points. ...The purpose of this paper is to investigate some sufficient and necessary conditions for three-step Ishikawa iterative sequences with error terms for uniformly quasi-Lipschitzian mappings to converge to fixed points. Our results extend and improve the recent ones announced by Liu [3,4], Xu and Noor [5], and many others.展开更多
Iterative Learning Control （ILC） captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control e...Iterative Learning Control （ILC） captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control engineering. Presently, most ILC algorithms still follow the original ideas of ARIMOTO, in which the iterative-learning-rate is composed by the control error with its derivative and integral values. This kind of algorithms will result in inevitable problems such as huge computation, big storage capacity for algorithm data, and also weak robust. In order to resolve these problems, an improved iterative learning control algorithm with fixed step is proposed here which breaks the primary thought of ARIMOTO. In this algorithm, the control step is set only according to the value of the control error, which could enormously reduce the computation and storage size demanded, also improve the robust of the algorithm by not using the differential coefficient of the iterative learning error. In this paper, the convergence conditions of this proposed fixed step iterative learning algorithm is theoretically analyzed and testified. Then the algorithm is tested through simulation researches on a time-variant object with randomly set disturbance through calculation of step threshold value, algorithm robustness testing,and evaluation of the relation between convergence speed and step size. Finally the algorithm is validated on a valve-serving-cylinder system of a joint robot with time-variant parameters. Experiment results demonstrate the stability of the algorithm and also the relationship between step value and convergence rate. Both simulation and experiment testify the feasibility and validity of the new algorithm proposed here. And it is worth to noticing that this algorithm is simple but with strong robust after improvements, which provides new ideas to the research of iterative learning control algorithms.展开更多
Large eddy simulation (LES) explicitly calculates the large-scale vortex field and parameterizes the small-scale vortices.In this study,LES and κ-ε models were developed for a specific geometrical configuration of b...Large eddy simulation (LES) explicitly calculates the large-scale vortex field and parameterizes the small-scale vortices.In this study,LES and κ-ε models were developed for a specific geometrical configuration of backward-facing step (BFS).The simulation results were validated with particle image velocimetry (PIV) measurements and direct numerical simulation (DNS).This LES simulation was carried out with a Reynolds number of 9000 in a pressurized water tunnel with an expansion ratio of 2.00.The results indicate that the LES model can reveal largescale vortex motion although with a larger grid-cell size.However,the LES model tends to overestimate the top wall separation and the Reynolds stress components for the BFS flow simulation without a sufficiently fine grid.Overall,LES is a potential tool for simulating separated flow controlled by large-scale vortices.展开更多
针对从基于压缩超快成像(Compressed Ultrafast Photography,CUP)的任意反射面速度干涉仪(Velocity Interferometer System for Any Reflector,VISAR)中获得的压缩图像中重构出冲击波二维条纹图像的问题,提出一种基于卡尔曼滤波的双约...针对从基于压缩超快成像(Compressed Ultrafast Photography,CUP)的任意反射面速度干涉仪(Velocity Interferometer System for Any Reflector,VISAR)中获得的压缩图像中重构出冲击波二维条纹图像的问题,提出一种基于卡尔曼滤波的双约束图像重构算法。该算法首先基于条纹图像具有的稀疏性和平滑性,将问题转化为基于小波与全变分双先验约束的优化问题,然后,考虑到实际成像的噪声问题,采用加权卡尔曼滤波对图像已有信息进行预测和调整,最后将卡尔曼滤波引入二步迭代阈值算法的迭代过程中,进而求解该双约束优化问题,实现压缩图像的精确重构。在大噪声仿真实验中,该算法重构图像的峰值信噪比和结构相似度分别提高了4.8 dB和14.81%,显著提高了图像重构质量。在实际实验中,该算法重构出了清晰的冲击波条纹图像,且将冲击波速度最大相对误差降低了9.57%和平均相对误差降低了2.2%,验证了该算法的可行性。展开更多
Binary wolf pack algorithm (BWPA) is a kind of intelligence algorithm which can solve combination optimization problems in discrete spaces.Based on BWPA, an improved binary wolf pack algorithm (AIBWPA) can be proposed...Binary wolf pack algorithm (BWPA) is a kind of intelligence algorithm which can solve combination optimization problems in discrete spaces.Based on BWPA, an improved binary wolf pack algorithm (AIBWPA) can be proposed by adopting adaptive step length and improved update strategy of wolf pack. AIBWPA is applied to 10 classic 0-1 knapsack problems and compared with BWPA, DPSO, which proves that AIBWPA has higher optimization accuracy and better computational robustness. AIBWPA makes the parameters simple, protects the population diversity and enhances the global convergence.展开更多
In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear ...In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear growth term to derive the search direction,and by introducing new technical results and selecting suitable parameters,we prove that the iteration bound of the algorithm is as good as best-known polynomial complexity of interior-point methods.Furthermore,numerical results illustrate the efficiency of the proposed method.展开更多
基金Project supported by the National Natural Science Foundation of China (No.10472061)
文摘An auxiliary principle technique to study a class of generalized set-valued strongly nonlinear mixed variational-like inequalities is extended. The existence and uniqueness of the solution of the auxiliary problem for the generalized set-valued strongly nonlinear mixed variational-like inequalities are proved, a novel and innovative three-step iterative algorithm to compute approximate solution is constructed, and the existence of the solution of the generalized set-valued strongly nonlinear mixed variational-like inequality is shown using the auxiliary principle iterative sequences generated by the algorithm technique. The convergence of three-step is also proved.
基金The author is thankful to the National Science Foundation of China for support through Grant 10171118
文摘The purpose of this paper is to investigate some sufficient and necessary conditions for three-step Ishikawa iterative sequences with error terms for uniformly quasi-Lipschitzian mappings to converge to fixed points. Our results extend and improve the recent ones announced by Liu [3,4], Xu and Noor [5], and many others.
基金supported by Specialized Research Fund for Doctoral Program of Higher Education of China (Grant No. 20091102120038)
文摘Iterative Learning Control （ILC） captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control engineering. Presently, most ILC algorithms still follow the original ideas of ARIMOTO, in which the iterative-learning-rate is composed by the control error with its derivative and integral values. This kind of algorithms will result in inevitable problems such as huge computation, big storage capacity for algorithm data, and also weak robust. In order to resolve these problems, an improved iterative learning control algorithm with fixed step is proposed here which breaks the primary thought of ARIMOTO. In this algorithm, the control step is set only according to the value of the control error, which could enormously reduce the computation and storage size demanded, also improve the robust of the algorithm by not using the differential coefficient of the iterative learning error. In this paper, the convergence conditions of this proposed fixed step iterative learning algorithm is theoretically analyzed and testified. Then the algorithm is tested through simulation researches on a time-variant object with randomly set disturbance through calculation of step threshold value, algorithm robustness testing,and evaluation of the relation between convergence speed and step size. Finally the algorithm is validated on a valve-serving-cylinder system of a joint robot with time-variant parameters. Experiment results demonstrate the stability of the algorithm and also the relationship between step value and convergence rate. Both simulation and experiment testify the feasibility and validity of the new algorithm proposed here. And it is worth to noticing that this algorithm is simple but with strong robust after improvements, which provides new ideas to the research of iterative learning control algorithms.
基金supported by the National Natural Science Foundation of China(Grant No.51379128)
文摘Large eddy simulation (LES) explicitly calculates the large-scale vortex field and parameterizes the small-scale vortices.In this study,LES and κ-ε models were developed for a specific geometrical configuration of backward-facing step (BFS).The simulation results were validated with particle image velocimetry (PIV) measurements and direct numerical simulation (DNS).This LES simulation was carried out with a Reynolds number of 9000 in a pressurized water tunnel with an expansion ratio of 2.00.The results indicate that the LES model can reveal largescale vortex motion although with a larger grid-cell size.However,the LES model tends to overestimate the top wall separation and the Reynolds stress components for the BFS flow simulation without a sufficiently fine grid.Overall,LES is a potential tool for simulating separated flow controlled by large-scale vortices.
文摘针对从基于压缩超快成像(Compressed Ultrafast Photography,CUP)的任意反射面速度干涉仪(Velocity Interferometer System for Any Reflector,VISAR)中获得的压缩图像中重构出冲击波二维条纹图像的问题,提出一种基于卡尔曼滤波的双约束图像重构算法。该算法首先基于条纹图像具有的稀疏性和平滑性,将问题转化为基于小波与全变分双先验约束的优化问题,然后,考虑到实际成像的噪声问题,采用加权卡尔曼滤波对图像已有信息进行预测和调整,最后将卡尔曼滤波引入二步迭代阈值算法的迭代过程中,进而求解该双约束优化问题,实现压缩图像的精确重构。在大噪声仿真实验中,该算法重构图像的峰值信噪比和结构相似度分别提高了4.8 dB和14.81%,显著提高了图像重构质量。在实际实验中,该算法重构出了清晰的冲击波条纹图像,且将冲击波速度最大相对误差降低了9.57%和平均相对误差降低了2.2%,验证了该算法的可行性。
文摘Binary wolf pack algorithm (BWPA) is a kind of intelligence algorithm which can solve combination optimization problems in discrete spaces.Based on BWPA, an improved binary wolf pack algorithm (AIBWPA) can be proposed by adopting adaptive step length and improved update strategy of wolf pack. AIBWPA is applied to 10 classic 0-1 knapsack problems and compared with BWPA, DPSO, which proves that AIBWPA has higher optimization accuracy and better computational robustness. AIBWPA makes the parameters simple, protects the population diversity and enhances the global convergence.
基金Supported by University Science Research Project of Anhui Province(2023AH052921)Outstanding Youth Talent Project of Anhui Province(gxyq2021254)。
文摘In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear growth term to derive the search direction,and by introducing new technical results and selecting suitable parameters,we prove that the iteration bound of the algorithm is as good as best-known polynomial complexity of interior-point methods.Furthermore,numerical results illustrate the efficiency of the proposed method.
