There are three difficult problems in the application of genetic algorithms, namely, the parameter control, the premature convergence and the deception problem.Based on genetic algorithm with varying population size, ...There are three difficult problems in the application of genetic algorithms, namely, the parameter control, the premature convergence and the deception problem.Based on genetic algorithm with varying population size, a self adaptive genetic algorithm called natural genetic algorithm (NGA) is proposed. This algorithm introduces the population size threshold and the immigrant concepts, and adopts dynamically changing parameters. The design and structure of NGA are discussed in this paper.The performance of NGA is also analyzed.展开更多
Variational mode decomposition(VMD) has been proved to be useful for extraction of fault-induced transients of rolling bearings. Multi-bandwidth mode manifold(Triple M, TM) is one variation of the VMD, which units mul...Variational mode decomposition(VMD) has been proved to be useful for extraction of fault-induced transients of rolling bearings. Multi-bandwidth mode manifold(Triple M, TM) is one variation of the VMD, which units multiple fault-related modes with different bandwidths by a nonlinear manifold learning algorithm named local tangent space alignment(LTSA). The merit of the TM method is that the bearing fault-induced transients extracted contain low level of in-band noise without optimization of the VMD parameters. However, the determination of the neighborhood size of the LTSA is time-consuming, and the extracted fault-induced transients may have the problem of asymmetry in the up-and-down direction. This paper aims to improve the efficiency and waveform symmetry of the TM method.Specifically, the multi-bandwidth modes consisting of the fault-related modes with different bandwidths are first obtained by repeating the recycling VMD(RVMD) method with different bandwidth balance parameters. Then, the LTSA algorithm is performed on the multi-bandwidth modes to extract their inherent manifold structure, in which the natural nearest neighbor(Triple N, TN) algorithm is adopted to efficiently and reasonably select the neighbors of each data point in the multi-bandwidth modes. Finally, a weight-based feature compensation strategy is designed to synthesize the low-dimensional manifold features to alleviate the asymmetry problem, resulting in a symmetric TM feature that can represent the real fault transient components. The major contribution of the improved TM method for bearing fault diagnosis is that the pure fault-induced transients are extracted efficiently and are symmetrical as the real. One simulation analysis and two experimental applications in bearing fault diagnosis validate the enhanced performance of the improved TM method over the traditional methods. This research proposes a bearing fault diagnosis method which has the advantages of high efficiency, good waveform symmetry and enhanced in-band noise removal capability.展开更多
This communique is opted to study the approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices.We choose the geodesic distance betweenAHXXA an...This communique is opted to study the approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices.We choose the geodesic distance betweenAHXXA and P as the cost function,and put forward the Extended Hamiltonian algorithm(EHA)and Natural gradient algorithm(NGA)for the solution.Finally,several numerical experiments give you an idea about the effectiveness of the proposed algorithms.We also show the comparison between these two algorithms EHA and NGA.Obtained results are provided and analyzed graphically.We also conclude that the extended Hamiltonian algorithm has better convergence speed than the natural gradient algorithm,whereas the trajectory of the solution matrix is optimal in case of Natural gradient algorithm(NGA)as compared to Extended Hamiltonian Algorithm(EHA).The aim of this paper is to show that the Extended Hamiltonian algorithm(EHA)has superior convergence properties as compared to Natural gradient algorithm(NGA).Upto the best of author’s knowledge,no approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices is found so far in the literature.展开更多
A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic response...A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.展开更多
A new framework based on the curved Riemannian manifold is proposed to calculate the numerical solution of the Lyapunov matrix equation by using a natural gradient descent algorithm and taking the geodesic distance as...A new framework based on the curved Riemannian manifold is proposed to calculate the numerical solution of the Lyapunov matrix equation by using a natural gradient descent algorithm and taking the geodesic distance as the objective function. Moreover, a gradient descent algorithm based on the classical Euclidean distance is provided to compare with this natural gradient descent algorithm. Furthermore, the behaviors of two proposed algorithms and the conventional modified conjugate gradient algorithm are compared and demonstrated by two simulation examples. By comparison, it is shown that the convergence speed of the natural gradient descent algorithm is faster than both of the gradient descent algorithm and the conventional modified conjugate gradient algorithm in solving the Lyapunov equation.展开更多
The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the position of astronomical bodies and the trajectory of cannon balls.In moder...The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the position of astronomical bodies and the trajectory of cannon balls.In modern times,this idea has been extended,for example,to ultrafast nonlinear optics serving as a surrogate analog computer to probe the behavior of complex phenomena such as rogue waves.Here we discuss a new paradigm where physical phenomena coded as an algorithm perform computational imaging tasks.Specifically,diffraction followed by coherent detection becomes an image enhancement tool.Vision Enhancement via Virtual diffraction and coherent Detection(VEViD)reimagines a digital image as a spatially varying metaphoric“lightfield”and then subjects the field to the physical processes akin to diffraction and coherent detection.The term“Virtual”captures the deviation from the physical world.The light field is pixelated and the propagation imparts a phase with dependence on frequency which is different from the monotonically-increasing behavior of physical diffraction.Temporal frequencies exist in three bands corresponding to the RGB color channels of a digital image.The phase of the output,not the intensity,represents the output image.VEViD is a high-performance low-light-level and color enhancement tool that emerges from this paradigm.The algorithm is extremely fast,interpretable,and reduces to a compact and intuitively-appealing mathematical expression.We demonstrate image enhancement of 4k video at over 200 frames per second and show the utility of this physical algorithm in improving the accuracy of object detection in low-light conditions by neural networks.The application of VEViD to color enhancement is also demonstrated.展开更多
Text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rich and precise information embod- ied in text is very useful in a wide range of...Text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rich and precise information embod- ied in text is very useful in a wide range of vision-based ap- plications, therefore text detection and recognition in natu- ral scenes have become important and active research topics in computer vision and document analysis. Especially in re- cent years, the community has seen a surge of research efforts and substantial progresses in these fields, though a variety of challenges （e.g. noise, blur, distortion, occlusion and varia- tion） still remain. The purposes of this survey are three-fold： 1） introduce up-to-date works, 2） identify state-of-the-art al- gorithms, and 3） predict potential research directions in the future. Moreover, this paper provides comprehensive links to publicly available resources, including benchmark datasets, source codes, and online demos. In summary, this literature review can serve as a good reference for researchers in the areas of scene text detection and recognition.展开更多
The bionics-based swarm intelligence optimization algorithm is a typical natural heuristic algorithm whose goal is to find the global optimal solution of the optimization problem.It simulates the group behavior of var...The bionics-based swarm intelligence optimization algorithm is a typical natural heuristic algorithm whose goal is to find the global optimal solution of the optimization problem.It simulates the group behavior of various animals and uses the information exchange and cooperation between individuals to achieve optimal goals through simple and effective interaction with experienced and intelligent individuals.This paper first introduces the principles of various swarm intelligent optimization algorithms.Then,the typical application of these swarm intelligence optimization algorithms in various fields is listed.After that,the advantages and defects of all swarm intelligence optimization algorithms are summarized.Next,the improvement strategies of various swarm intelligence optimization algorithms are explained.Finally,the future development of various swarm intelligence optimization algorithms is prospected.展开更多
文摘There are three difficult problems in the application of genetic algorithms, namely, the parameter control, the premature convergence and the deception problem.Based on genetic algorithm with varying population size, a self adaptive genetic algorithm called natural genetic algorithm (NGA) is proposed. This algorithm introduces the population size threshold and the immigrant concepts, and adopts dynamically changing parameters. The design and structure of NGA are discussed in this paper.The performance of NGA is also analyzed.
基金Supported by National Natural Science Foundation of China (Grant Nos. 51805342,51875376, 52007128)Jiangsu Provincial Natural Science Foundation of China (Grant No. BK20180842)+2 种基金China Postdoctoral Science Foundation (Grant Nos. 2021M692354, 2018M640514)Suzhou Prospective Research Program of China (Grant No. SYG201932)Jiangsu Provincial Natural Science Fund for Colleges and Universities of China (Grant No. 18KJB470022)。
文摘Variational mode decomposition(VMD) has been proved to be useful for extraction of fault-induced transients of rolling bearings. Multi-bandwidth mode manifold(Triple M, TM) is one variation of the VMD, which units multiple fault-related modes with different bandwidths by a nonlinear manifold learning algorithm named local tangent space alignment(LTSA). The merit of the TM method is that the bearing fault-induced transients extracted contain low level of in-band noise without optimization of the VMD parameters. However, the determination of the neighborhood size of the LTSA is time-consuming, and the extracted fault-induced transients may have the problem of asymmetry in the up-and-down direction. This paper aims to improve the efficiency and waveform symmetry of the TM method.Specifically, the multi-bandwidth modes consisting of the fault-related modes with different bandwidths are first obtained by repeating the recycling VMD(RVMD) method with different bandwidth balance parameters. Then, the LTSA algorithm is performed on the multi-bandwidth modes to extract their inherent manifold structure, in which the natural nearest neighbor(Triple N, TN) algorithm is adopted to efficiently and reasonably select the neighbors of each data point in the multi-bandwidth modes. Finally, a weight-based feature compensation strategy is designed to synthesize the low-dimensional manifold features to alleviate the asymmetry problem, resulting in a symmetric TM feature that can represent the real fault transient components. The major contribution of the improved TM method for bearing fault diagnosis is that the pure fault-induced transients are extracted efficiently and are symmetrical as the real. One simulation analysis and two experimental applications in bearing fault diagnosis validate the enhanced performance of the improved TM method over the traditional methods. This research proposes a bearing fault diagnosis method which has the advantages of high efficiency, good waveform symmetry and enhanced in-band noise removal capability.
文摘This communique is opted to study the approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices.We choose the geodesic distance betweenAHXXA and P as the cost function,and put forward the Extended Hamiltonian algorithm(EHA)and Natural gradient algorithm(NGA)for the solution.Finally,several numerical experiments give you an idea about the effectiveness of the proposed algorithms.We also show the comparison between these two algorithms EHA and NGA.Obtained results are provided and analyzed graphically.We also conclude that the extended Hamiltonian algorithm has better convergence speed than the natural gradient algorithm,whereas the trajectory of the solution matrix is optimal in case of Natural gradient algorithm(NGA)as compared to Extended Hamiltonian Algorithm(EHA).The aim of this paper is to show that the Extended Hamiltonian algorithm(EHA)has superior convergence properties as compared to Natural gradient algorithm(NGA).Upto the best of author’s knowledge,no approximate solution of the Algebraic Lyapunov equation on the manifold of positive-definite Hermitian matrices is found so far in the literature.
基金Supported by the National Natural Science Foundation of China（51079027）
文摘A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.
文摘A new framework based on the curved Riemannian manifold is proposed to calculate the numerical solution of the Lyapunov matrix equation by using a natural gradient descent algorithm and taking the geodesic distance as the objective function. Moreover, a gradient descent algorithm based on the classical Euclidean distance is provided to compare with this natural gradient descent algorithm. Furthermore, the behaviors of two proposed algorithms and the conventional modified conjugate gradient algorithm are compared and demonstrated by two simulation examples. By comparison, it is shown that the convergence speed of the natural gradient descent algorithm is faster than both of the gradient descent algorithm and the conventional modified conjugate gradient algorithm in solving the Lyapunov equation.
基金Parker Center for Cancer Immunotherapy(PICI),Grant No.20163828,and by the Office of Naval Research(ONR)Multi-disciplinary University Research Initiatives(MURI)program on Optical Computing Award Number N00014-14-1-0505.
文摘The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the position of astronomical bodies and the trajectory of cannon balls.In modern times,this idea has been extended,for example,to ultrafast nonlinear optics serving as a surrogate analog computer to probe the behavior of complex phenomena such as rogue waves.Here we discuss a new paradigm where physical phenomena coded as an algorithm perform computational imaging tasks.Specifically,diffraction followed by coherent detection becomes an image enhancement tool.Vision Enhancement via Virtual diffraction and coherent Detection(VEViD)reimagines a digital image as a spatially varying metaphoric“lightfield”and then subjects the field to the physical processes akin to diffraction and coherent detection.The term“Virtual”captures the deviation from the physical world.The light field is pixelated and the propagation imparts a phase with dependence on frequency which is different from the monotonically-increasing behavior of physical diffraction.Temporal frequencies exist in three bands corresponding to the RGB color channels of a digital image.The phase of the output,not the intensity,represents the output image.VEViD is a high-performance low-light-level and color enhancement tool that emerges from this paradigm.The algorithm is extremely fast,interpretable,and reduces to a compact and intuitively-appealing mathematical expression.We demonstrate image enhancement of 4k video at over 200 frames per second and show the utility of this physical algorithm in improving the accuracy of object detection in low-light conditions by neural networks.The application of VEViD to color enhancement is also demonstrated.
文摘Text, as one of the most influential inventions of humanity, has played an important role in human life, so far from ancient times. The rich and precise information embod- ied in text is very useful in a wide range of vision-based ap- plications, therefore text detection and recognition in natu- ral scenes have become important and active research topics in computer vision and document analysis. Especially in re- cent years, the community has seen a surge of research efforts and substantial progresses in these fields, though a variety of challenges （e.g. noise, blur, distortion, occlusion and varia- tion） still remain. The purposes of this survey are three-fold： 1） introduce up-to-date works, 2） identify state-of-the-art al- gorithms, and 3） predict potential research directions in the future. Moreover, this paper provides comprehensive links to publicly available resources, including benchmark datasets, source codes, and online demos. In summary, this literature review can serve as a good reference for researchers in the areas of scene text detection and recognition.
基金This work was supported by the National Natural Science Foundation of China(61601177)the Science and Technology Research Program of Hubei Provincial Department of Education(Q20171401)。
文摘The bionics-based swarm intelligence optimization algorithm is a typical natural heuristic algorithm whose goal is to find the global optimal solution of the optimization problem.It simulates the group behavior of various animals and uses the information exchange and cooperation between individuals to achieve optimal goals through simple and effective interaction with experienced and intelligent individuals.This paper first introduces the principles of various swarm intelligent optimization algorithms.Then,the typical application of these swarm intelligence optimization algorithms in various fields is listed.After that,the advantages and defects of all swarm intelligence optimization algorithms are summarized.Next,the improvement strategies of various swarm intelligence optimization algorithms are explained.Finally,the future development of various swarm intelligence optimization algorithms is prospected.
