An improved version of the sparse A^(*)algorithm is proposed to address the common issue of excessive expansion of nodes and failure to consider current ship status and parameters in traditional path planning algorith...An improved version of the sparse A^(*)algorithm is proposed to address the common issue of excessive expansion of nodes and failure to consider current ship status and parameters in traditional path planning algorithms.This algorithm considers factors such as initial position and orientation of the ship,safety range,and ship draft to determine the optimal obstacle-avoiding route from the current to the destination point for ship planning.A coordinate transformation algorithm is also applied to convert commonly used latitude and longitude coordinates of ship travel paths to easily utilized and analyzed Cartesian coordinates.The algorithm incorporates a hierarchical chart processing algorithm to handle multilayered chart data.Furthermore,the algorithm considers the impact of ship length on grid size and density when implementing chart gridification,adjusting the grid size and density accordingly based on ship length.Simulation results show that compared to traditional path planning algorithms,the sparse A^(*)algorithm reduces the average number of path points by 25%,decreases the average maximum storage node number by 17%,and raises the average path turning angle by approximately 10°,effectively improving the safety of ship planning paths.展开更多
Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.I...Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.展开更多
The proportionate recursive least squares(PRLS)algorithm has shown faster convergence and better performance than both proportionate updating(PU)mechanism based least mean squares(LMS)algorithms and RLS algorithms wit...The proportionate recursive least squares(PRLS)algorithm has shown faster convergence and better performance than both proportionate updating(PU)mechanism based least mean squares(LMS)algorithms and RLS algorithms with a sparse regularization term.In this paper,we propose a variable forgetting factor(VFF)PRLS algorithm with a sparse penalty,e.g.,l_(1)-norm,for sparse identification.To reduce the computation complexity of the proposed algorithm,a fast implementation method based on dichotomous coordinate descent(DCD)algorithm is also derived.Simulation results indicate superior performance of the proposed algorithm.展开更多
An improved adaptive genetic algorithm is presented in this paper. It primarily includes two modified methods： one is novel adaptive probabilities of crossover and mutation, the other is truncated selection approach....An improved adaptive genetic algorithm is presented in this paper. It primarily includes two modified methods： one is novel adaptive probabilities of crossover and mutation, the other is truncated selection approach. This algorithm has been validated to be superior to the simple genetic algorithm （SGA） by a complicated binary testing function. Then the proposed algorithm is applied to optimizing the planar retrodirective array to reduce the cost of the hardware. The fitness function is discussed in the optimization example. After optimization, the sparse planar retrodirective antenna array keeps excellent retrodirectivity, while the array architecture has been simplified by 34%. The optimized antenna array can replace uniform full array effectively. Results show that this work will gain more engineering benefits in practice.展开更多
For quantum sparse graph codes with stabilizer formalism, the unavoidable girth-four cycles in their Tanner graphs greatly degrade the iterative decoding performance with standard belief-propagation （BP） algorithm. ...For quantum sparse graph codes with stabilizer formalism, the unavoidable girth-four cycles in their Tanner graphs greatly degrade the iterative decoding performance with standard belief-propagation （BP） algorithm. In this paper, we present a jointly-check iterative algorithm suitable for decoding quantum sparse graph codes efficiently. Numerical simulations show that this modified method outperforms standard BP algorithm with an obvious performance improvement.展开更多
In this paper,we reconstruct strongly-decaying block sparse signals by the block generalized orthogonal matching pursuit(BgOMP)algorithm in the l2-bounded noise case.Under some restraints on the minimum magnitude of t...In this paper,we reconstruct strongly-decaying block sparse signals by the block generalized orthogonal matching pursuit(BgOMP)algorithm in the l2-bounded noise case.Under some restraints on the minimum magnitude of the nonzero elements of the strongly-decaying block sparse signal,if the sensing matrix satisfies the the block restricted isometry property(block-RIP),then arbitrary strongly-decaying block sparse signals can be accurately and steadily reconstructed by the BgOMP algorithm in iterations.Furthermore,we conjecture that this condition is sharp.展开更多
Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and ...Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and nonlinear resampling is proposed in this paper. First,the sparse representation is used to compute particle weights by considering the fact that the weights are sparse when the object moves abruptly,so the potential object region can be predicted more precisely. Then,a nonlinear resampling process is proposed by utilizing the nonlinear sorting strategy,which can solve the problem of particle diversity impoverishment caused by traditional resampling methods. Experimental results based on videos containing objects with various abrupt motions have demonstrated the effectiveness of the proposed algorithm.展开更多
In this paper,a fast orthogonal matching pursuit(OMP)algorithm based on optimized iterative process is proposed for sparse time-varying underwater acoustic(UWA)channel estimation.The channel estimation consists of cal...In this paper,a fast orthogonal matching pursuit(OMP)algorithm based on optimized iterative process is proposed for sparse time-varying underwater acoustic(UWA)channel estimation.The channel estimation consists of calculating amplitude,delay and Doppler scaling factor of each path using the received multi-path signal.This algorithm,called as OIP-FOMP,can reduce the computationally complexity of the traditional OMP algorithm and maintain accuracy in the presence of severe inter-carrier interference that exists in the time-varying UWA channels.In this algorithm,repeated inner product operations used in the OMP algorithm are removed by calculating the candidate path signature Hermitian inner product matrix in advance.Efficient QR decomposition is used to estimate the path amplitude,and the problem of reconstruction failure caused by inaccurate delay selection is avoided by optimizing the Hermitian inner product matrix.Theoretical analysis and simulation results show that the computational complexity of the OIP-FOMP algorithm is reduced by about 1/4 compared with the OMP algorithm,without any loss of accuracy.展开更多
Designing a sparse array with reduced transmit/receive modules(TRMs)is vital for some applications where the antenna system’s size,weight,allowed operating space,and cost are limited.Sparse arrays exhibit distinct ar...Designing a sparse array with reduced transmit/receive modules(TRMs)is vital for some applications where the antenna system’s size,weight,allowed operating space,and cost are limited.Sparse arrays exhibit distinct architectures,roughly classified into three categories:Thinned arrays,nonuniformly spaced arrays,and clustered arrays.While numerous advanced synthesis methods have been presented for the three types of sparse arrays in recent years,a comprehensive review of the latest development in sparse array synthesis is lacking.This work aims to fill this gap by thoroughly summarizing these techniques.The study includes synthesis examples to facilitate a comparative analysis of different techniques in terms of both accuracy and efficiency.Thus,this review is intended to assist researchers and engineers in related fields,offering a clear understanding of the development and distinctions among sparse array synthesis techniques.展开更多
Wayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness(OOR) damage wheels...Wayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness(OOR) damage wheels, such as wheel flats and polygonal wheels. This automatic damage identification algorithm is based on the vertical acceleration evaluated on the rails using a virtual wayside monitoring system and involves the application of a two-step procedure. The first step aims to define a confidence boundary by using(healthy) measurements evaluated on the rail constituting a baseline. The second step of the procedure involves classifying damage of predefined scenarios with different levels of severities. The proposed procedure is based on a machine learning methodology and includes the following stages:(1) data collection,(2) damage-sensitive feature extraction from the acquired responses using a neural network model, i.e., the sparse autoencoder(SAE),(3) data fusion based on the Mahalanobis distance, and(4) unsupervised feature classification by implementing outlier and cluster analysis. This procedure considers baseline responses at different speeds and rail irregularities to train the SAE model. Then, the trained SAE is capable to reconstruct test responses(not trained) allowing to compute the accumulative difference between original and reconstructed signals. The results prove the efficiency of the proposed approach in identifying the two most common types of OOR in railway wheels.展开更多
This study introduces a pre-orthogonal adaptive Fourier decomposition(POAFD)to obtain approximations and numerical solutions to the fractional Laplacian initial value problem and the extension problem of Caffarelli an...This study introduces a pre-orthogonal adaptive Fourier decomposition(POAFD)to obtain approximations and numerical solutions to the fractional Laplacian initial value problem and the extension problem of Caffarelli and Silvestre(generalized Poisson equation).As a first step,the method expands the initial data function into a sparse series of the fundamental solutions with fast convergence,and,as a second step,makes use of the semigroup or the reproducing kernel property of each of the expanding entries.Experiments show the effectiveness and efficiency of the proposed series solutions.展开更多
Behind the prevalence of multimedia technology,digital copyright disputes are becoming increasingly serious.The digital watermarking prevention technique against the copyright infringement needs to be improved urgentl...Behind the prevalence of multimedia technology,digital copyright disputes are becoming increasingly serious.The digital watermarking prevention technique against the copyright infringement needs to be improved urgently.Among the proposed technologies,zero-watermarking has been favored recently.In order to improve the robustness of the zero-watermarking,a novel robust audio zerowatermarking method based on sparse representation is proposed.The proposed scheme is mainly based on the K-singular value decomposition(K-SVD)algorithm to construct an optimal over complete dictionary from the background audio signal.After that,the orthogonal matching pursuit(OMP)algorithm is used to calculate the sparse coefficient of the segmented test audio and generate the corresponding sparse coefficient matrix.Then,the mean value of absolute sparse coefficients in the sparse matrix of segmented speech is calculated and selected,and then comparing the mean absolute coefficient of segmented speech with the average value of the selected coefficients to realize the embedding of zero-watermarking.Experimental results show that the proposed audio zerowatermarking algorithm based on sparse representation performs effectively in resisting various common attacks.Compared with the baseline works,the proposed method has better robustness.展开更多
Neutron computed tomography(NCT)is widely used as a noninvasive measurement technique in nuclear engineering,thermal hydraulics,and cultural heritage.The neutron source intensity of NCT is usually low and the scan tim...Neutron computed tomography(NCT)is widely used as a noninvasive measurement technique in nuclear engineering,thermal hydraulics,and cultural heritage.The neutron source intensity of NCT is usually low and the scan time is long,resulting in a projection image containing severe noise.To reduce the scanning time and increase the image reconstruction quality,an effective reconstruction algorithm must be selected.In CT image reconstruction,the reconstruction algorithms can be divided into three categories:analytical algorithms,iterative algorithms,and deep learning.Because the analytical algorithm requires complete projection data,it is not suitable for reconstruction in harsh environments,such as strong radia-tion,high temperature,and high pressure.Deep learning requires large amounts of data and complex models,which cannot be easily deployed,as well as has a high computational complexity and poor interpretability.Therefore,this paper proposes the OS-SART-PDTV iterative algorithm,which uses the ordered subset simultaneous algebraic reconstruction technique(OS-SART)algorithm to reconstruct the image and the first-order primal–dual algorithm to solve the total variation(PDTV),for sparse-view NCT three-dimensional reconstruction.The novel algorithm was compared with other algorithms(FBP,OS-SART-TV,OS-SART-AwTV,and OS-SART-FGPTV)by simulating the experimental data and actual neutron projection experiments.The reconstruction results demonstrate that the proposed algorithm outperforms the FBP,OS-SART-TV,OS-SART-AwTV,and OS-SART-FGPTV algorithms in terms of preserving edge structure,denoising,and suppressing artifacts.展开更多
We consider the task of binary classification in the high-dimensional setting where the number of features of the given data is larger than the number of observations.To accomplish this task,we propose an adherently p...We consider the task of binary classification in the high-dimensional setting where the number of features of the given data is larger than the number of observations.To accomplish this task,we propose an adherently penalized optimal scoring(APOS)model for simultaneously performing discriminant analysis and feature selection.In this paper,an efficient algorithm based on the block coordinate descent(BCD)method and the SSNAL algorithm is developed to solve the APOS approximately.The convergence results of our method are also established.Numerical experiments conducted on simulated and real datasets demonstrate that the proposed model is more efficient than several sparse discriminant analysis methods.展开更多
Pilot pattern has a significant effect on the performance of channel estimation based on compressed sensing.However,because of the influence of the number of subcarriers and pilots,the complexity of the enumeration me...Pilot pattern has a significant effect on the performance of channel estimation based on compressed sensing.However,because of the influence of the number of subcarriers and pilots,the complexity of the enumeration method is computationally impractical.The meta-heuristic algorithm of the salp swarm algorithm(SSA)is employed to address this issue.Like most meta-heuristic algorithms,the SSA algorithm is prone to problems such as local optimal values and slow convergence.In this paper,we proposed the CWSSA to enhance the optimization efficiency and robustness by chaotic opposition-based learning strategy,adaptive weight factor,and increasing local search.Experiments show that the test results of the CWSSA on most benchmark functions are better than those of other meta-heuristic algorithms.Besides,the CWSSA algorithm is applied to pilot pattern optimization,and its results are better than other methods in terms of BER and MSE.展开更多
In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and t...In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and the greatest common divisor.We further provided several suggestions for teaching.展开更多
Denoising of chaotic signal is a challenge work due to its wide-band and noise-like characteristics.The algorithm should make the denoised signal have a high signal to noise ratio and retain the chaotic characteristic...Denoising of chaotic signal is a challenge work due to its wide-band and noise-like characteristics.The algorithm should make the denoised signal have a high signal to noise ratio and retain the chaotic characteristics.We propose a denoising method of chaotic signals based on sparse decomposition and K-singular value decomposition(K-SVD)optimization.The observed signal is divided into segments and decomposed sparsely.The over-complete atomic library is constructed according to the differential equation of chaotic signals.The orthogonal matching pursuit algorithm is used to search the optimal matching atom.The atoms and coefficients are further processed to obtain the globally optimal atoms and coefficients by K-SVD.The simulation results show that the denoised signals have a higher signal to noise ratio and better preserve the chaotic characteristics.展开更多
The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,wh...The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,which is not conducive to the control of USV and also affects navigation safety.In this paper,these problems were addressed through the following improvements.First,the path search angle and security were comprehensively considered,and a security expansion strategy of nodes based on the 5×5 neighborhood was proposed.The A^(*)algorithm search neighborhood was expanded from 3×3 to 5×5,and safe nodes were screened out for extension via the node security expansion strategy.This algorithm can also optimize path search angles while improving path security.Second,the distance from the current node to the target node was introduced into the heuristic function.The efficiency of the A^(*)algorithm was improved,and the path was smoothed using the Floyd algorithm.For the dynamic adjustment of the weight to improve the efficiency of DWA,the distance from the USV to the target point was introduced into the evaluation function of the dynamic-window approach(DWA)algorithm.Finally,combined with the local target point selection strategy,the optimized DWA algorithm was performed for local path planning.The experimental results show the smooth and safe path planned by the fusion algorithm,which can successfully avoid dynamic obstacles and is effective and feasible in path planning for USVs.展开更多
Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability...Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.展开更多
基金Supported by the Tianjin University of Technology Graduate R esearch Innovation Project(YJ2281).
文摘An improved version of the sparse A^(*)algorithm is proposed to address the common issue of excessive expansion of nodes and failure to consider current ship status and parameters in traditional path planning algorithms.This algorithm considers factors such as initial position and orientation of the ship,safety range,and ship draft to determine the optimal obstacle-avoiding route from the current to the destination point for ship planning.A coordinate transformation algorithm is also applied to convert commonly used latitude and longitude coordinates of ship travel paths to easily utilized and analyzed Cartesian coordinates.The algorithm incorporates a hierarchical chart processing algorithm to handle multilayered chart data.Furthermore,the algorithm considers the impact of ship length on grid size and density when implementing chart gridification,adjusting the grid size and density accordingly based on ship length.Simulation results show that compared to traditional path planning algorithms,the sparse A^(*)algorithm reduces the average number of path points by 25%,decreases the average maximum storage node number by 17%,and raises the average path turning angle by approximately 10°,effectively improving the safety of ship planning paths.
文摘Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.
基金supported by National Key Research and Development Program of China(2020YFB0505803)National Key Research and Development Program of China(2016YFB0501700)。
文摘The proportionate recursive least squares(PRLS)algorithm has shown faster convergence and better performance than both proportionate updating(PU)mechanism based least mean squares(LMS)algorithms and RLS algorithms with a sparse regularization term.In this paper,we propose a variable forgetting factor(VFF)PRLS algorithm with a sparse penalty,e.g.,l_(1)-norm,for sparse identification.To reduce the computation complexity of the proposed algorithm,a fast implementation method based on dichotomous coordinate descent(DCD)algorithm is also derived.Simulation results indicate superior performance of the proposed algorithm.
文摘An improved adaptive genetic algorithm is presented in this paper. It primarily includes two modified methods： one is novel adaptive probabilities of crossover and mutation, the other is truncated selection approach. This algorithm has been validated to be superior to the simple genetic algorithm （SGA） by a complicated binary testing function. Then the proposed algorithm is applied to optimizing the planar retrodirective array to reduce the cost of the hardware. The fitness function is discussed in the optimization example. After optimization, the sparse planar retrodirective antenna array keeps excellent retrodirectivity, while the array architecture has been simplified by 34%. The optimized antenna array can replace uniform full array effectively. Results show that this work will gain more engineering benefits in practice.
基金Project supported by the National Natural Science Foundation of China(Grant No.60972046)Grant from the National Defense Pre-Research Foundation of China
文摘For quantum sparse graph codes with stabilizer formalism, the unavoidable girth-four cycles in their Tanner graphs greatly degrade the iterative decoding performance with standard belief-propagation （BP） algorithm. In this paper, we present a jointly-check iterative algorithm suitable for decoding quantum sparse graph codes efficiently. Numerical simulations show that this modified method outperforms standard BP algorithm with an obvious performance improvement.
基金supported by Natural Science Foundation of China(62071262)the K.C.Wong Magna Fund at Ningbo University.
文摘In this paper,we reconstruct strongly-decaying block sparse signals by the block generalized orthogonal matching pursuit(BgOMP)algorithm in the l2-bounded noise case.Under some restraints on the minimum magnitude of the nonzero elements of the strongly-decaying block sparse signal,if the sensing matrix satisfies the the block restricted isometry property(block-RIP),then arbitrary strongly-decaying block sparse signals can be accurately and steadily reconstructed by the BgOMP algorithm in iterations.Furthermore,we conjecture that this condition is sharp.
基金Supported by the National Natural Science Foundation of China(61701029)
文摘Object tracking with abrupt motion is an important research topic and has attracted wide attention.To obtain accurate tracking results,an improved particle filter tracking algorithm based on sparse representation and nonlinear resampling is proposed in this paper. First,the sparse representation is used to compute particle weights by considering the fact that the weights are sparse when the object moves abruptly,so the potential object region can be predicted more precisely. Then,a nonlinear resampling process is proposed by utilizing the nonlinear sorting strategy,which can solve the problem of particle diversity impoverishment caused by traditional resampling methods. Experimental results based on videos containing objects with various abrupt motions have demonstrated the effectiveness of the proposed algorithm.
基金supported in part by the National Natural Science Foundation of China(NSFC)(No.U1806201,61671261)Project of Shandong Province Higher Educational Science and Technology Program(No.J17KA058,J17KB154).
文摘In this paper,a fast orthogonal matching pursuit(OMP)algorithm based on optimized iterative process is proposed for sparse time-varying underwater acoustic(UWA)channel estimation.The channel estimation consists of calculating amplitude,delay and Doppler scaling factor of each path using the received multi-path signal.This algorithm,called as OIP-FOMP,can reduce the computationally complexity of the traditional OMP algorithm and maintain accuracy in the presence of severe inter-carrier interference that exists in the time-varying UWA channels.In this algorithm,repeated inner product operations used in the OMP algorithm are removed by calculating the candidate path signature Hermitian inner product matrix in advance.Efficient QR decomposition is used to estimate the path amplitude,and the problem of reconstruction failure caused by inaccurate delay selection is avoided by optimizing the Hermitian inner product matrix.Theoretical analysis and simulation results show that the computational complexity of the OIP-FOMP algorithm is reduced by about 1/4 compared with the OMP algorithm,without any loss of accuracy.
基金supported by the National Natural Science Foundation of China under Grant No.U2341208.
文摘Designing a sparse array with reduced transmit/receive modules(TRMs)is vital for some applications where the antenna system’s size,weight,allowed operating space,and cost are limited.Sparse arrays exhibit distinct architectures,roughly classified into three categories:Thinned arrays,nonuniformly spaced arrays,and clustered arrays.While numerous advanced synthesis methods have been presented for the three types of sparse arrays in recent years,a comprehensive review of the latest development in sparse array synthesis is lacking.This work aims to fill this gap by thoroughly summarizing these techniques.The study includes synthesis examples to facilitate a comparative analysis of different techniques in terms of both accuracy and efficiency.Thus,this review is intended to assist researchers and engineers in related fields,offering a clear understanding of the development and distinctions among sparse array synthesis techniques.
基金a result of project WAY4SafeRail—Wayside monitoring system FOR SAFE RAIL transportation, with reference NORTE-01-0247-FEDER-069595co-funded by the European Regional Development Fund (ERDF), through the North Portugal Regional Operational Programme (NORTE2020), under the PORTUGAL 2020 Partnership Agreement+3 种基金financially supported by Base Funding-UIDB/04708/2020Programmatic Funding-UIDP/04708/2020 of the CONSTRUCT—Instituto de Estruturas e Constru??esfunded by national funds through the FCT/ MCTES (PIDDAC)Grant No. 2021.04272. CEECIND from the Stimulus of Scientific Employment, Individual Support (CEECIND) - 4th Edition provided by “FCT – Funda??o para a Ciência, DOI : https:// doi. org/ 10. 54499/ 2021. 04272. CEECI ND/ CP1679/ CT0003”。
文摘Wayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness(OOR) damage wheels, such as wheel flats and polygonal wheels. This automatic damage identification algorithm is based on the vertical acceleration evaluated on the rails using a virtual wayside monitoring system and involves the application of a two-step procedure. The first step aims to define a confidence boundary by using(healthy) measurements evaluated on the rail constituting a baseline. The second step of the procedure involves classifying damage of predefined scenarios with different levels of severities. The proposed procedure is based on a machine learning methodology and includes the following stages:(1) data collection,(2) damage-sensitive feature extraction from the acquired responses using a neural network model, i.e., the sparse autoencoder(SAE),(3) data fusion based on the Mahalanobis distance, and(4) unsupervised feature classification by implementing outlier and cluster analysis. This procedure considers baseline responses at different speeds and rail irregularities to train the SAE model. Then, the trained SAE is capable to reconstruct test responses(not trained) allowing to compute the accumulative difference between original and reconstructed signals. The results prove the efficiency of the proposed approach in identifying the two most common types of OOR in railway wheels.
基金supported by the Science and Technology Development Fund of Macao SAR(FDCT0128/2022/A,0020/2023/RIB1,0111/2023/AFJ,005/2022/ALC)the Shandong Natural Science Foundation of China(ZR2020MA004)+2 种基金the National Natural Science Foundation of China(12071272)the MYRG 2018-00168-FSTZhejiang Provincial Natural Science Foundation of China(LQ23A010014).
文摘This study introduces a pre-orthogonal adaptive Fourier decomposition(POAFD)to obtain approximations and numerical solutions to the fractional Laplacian initial value problem and the extension problem of Caffarelli and Silvestre(generalized Poisson equation).As a first step,the method expands the initial data function into a sparse series of the fundamental solutions with fast convergence,and,as a second step,makes use of the semigroup or the reproducing kernel property of each of the expanding entries.Experiments show the effectiveness and efficiency of the proposed series solutions.
基金the National Natural Science Foundation of China(No.62001100)the Fundamental Research Funds for the Central Universities(No.2232019D3-52)Shanghai Sailing Program.(No.19YF1402000).
文摘Behind the prevalence of multimedia technology,digital copyright disputes are becoming increasingly serious.The digital watermarking prevention technique against the copyright infringement needs to be improved urgently.Among the proposed technologies,zero-watermarking has been favored recently.In order to improve the robustness of the zero-watermarking,a novel robust audio zerowatermarking method based on sparse representation is proposed.The proposed scheme is mainly based on the K-singular value decomposition(K-SVD)algorithm to construct an optimal over complete dictionary from the background audio signal.After that,the orthogonal matching pursuit(OMP)algorithm is used to calculate the sparse coefficient of the segmented test audio and generate the corresponding sparse coefficient matrix.Then,the mean value of absolute sparse coefficients in the sparse matrix of segmented speech is calculated and selected,and then comparing the mean absolute coefficient of segmented speech with the average value of the selected coefficients to realize the embedding of zero-watermarking.Experimental results show that the proposed audio zerowatermarking algorithm based on sparse representation performs effectively in resisting various common attacks.Compared with the baseline works,the proposed method has better robustness.
基金supported by the National Key Research and Development Program of China(No.2022YFB1902700)the Joint Fund of Ministry of Education for Equipment Pre-research(No.8091B042203)+5 种基金the National Natural Science Foundation of China(No.11875129)the Fund of the State Key Laboratory of Intense Pulsed Radiation Simulation and Effect(No.SKLIPR1810)the Fund of Innovation Center of Radiation Application(No.KFZC2020020402)the Fund of the State Key Laboratory of Nuclear Physics and Technology,Peking University(No.NPT2023KFY06)the Joint Innovation Fund of China National Uranium Co.,Ltd.,State Key Laboratory of Nuclear Resources and Environment,East China University of Technology(No.2022NRE-LH-02)the Fundamental Research Funds for the Central Universities(No.2023JG001).
文摘Neutron computed tomography(NCT)is widely used as a noninvasive measurement technique in nuclear engineering,thermal hydraulics,and cultural heritage.The neutron source intensity of NCT is usually low and the scan time is long,resulting in a projection image containing severe noise.To reduce the scanning time and increase the image reconstruction quality,an effective reconstruction algorithm must be selected.In CT image reconstruction,the reconstruction algorithms can be divided into three categories:analytical algorithms,iterative algorithms,and deep learning.Because the analytical algorithm requires complete projection data,it is not suitable for reconstruction in harsh environments,such as strong radia-tion,high temperature,and high pressure.Deep learning requires large amounts of data and complex models,which cannot be easily deployed,as well as has a high computational complexity and poor interpretability.Therefore,this paper proposes the OS-SART-PDTV iterative algorithm,which uses the ordered subset simultaneous algebraic reconstruction technique(OS-SART)algorithm to reconstruct the image and the first-order primal–dual algorithm to solve the total variation(PDTV),for sparse-view NCT three-dimensional reconstruction.The novel algorithm was compared with other algorithms(FBP,OS-SART-TV,OS-SART-AwTV,and OS-SART-FGPTV)by simulating the experimental data and actual neutron projection experiments.The reconstruction results demonstrate that the proposed algorithm outperforms the FBP,OS-SART-TV,OS-SART-AwTV,and OS-SART-FGPTV algorithms in terms of preserving edge structure,denoising,and suppressing artifacts.
基金supported by the National Natural Science Foundation of China(No.12271097)the Key Program of National Science Foundation of Fujian Province of China(No.2023J02007)+1 种基金the Central Guidance on Local Science and Technology Development Fund of Fujian Province(No.2023L3003)the Fujian Alliance of Mathematics(No.2023SXLMMS01)。
文摘We consider the task of binary classification in the high-dimensional setting where the number of features of the given data is larger than the number of observations.To accomplish this task,we propose an adherently penalized optimal scoring(APOS)model for simultaneously performing discriminant analysis and feature selection.In this paper,an efficient algorithm based on the block coordinate descent(BCD)method and the SSNAL algorithm is developed to solve the APOS approximately.The convergence results of our method are also established.Numerical experiments conducted on simulated and real datasets demonstrate that the proposed model is more efficient than several sparse discriminant analysis methods.
文摘Pilot pattern has a significant effect on the performance of channel estimation based on compressed sensing.However,because of the influence of the number of subcarriers and pilots,the complexity of the enumeration method is computationally impractical.The meta-heuristic algorithm of the salp swarm algorithm(SSA)is employed to address this issue.Like most meta-heuristic algorithms,the SSA algorithm is prone to problems such as local optimal values and slow convergence.In this paper,we proposed the CWSSA to enhance the optimization efficiency and robustness by chaotic opposition-based learning strategy,adaptive weight factor,and increasing local search.Experiments show that the test results of the CWSSA on most benchmark functions are better than those of other meta-heuristic algorithms.Besides,the CWSSA algorithm is applied to pilot pattern optimization,and its results are better than other methods in terms of BER and MSE.
基金Supported by the Natural Science Foundation of Chongqing(General Program,NO.CSTB2022NSCQ-MSX0884)Discipline Teaching Special Project of Yangtze Normal University(csxkjx14)。
文摘In this paper,we prove that Euclid's algorithm,Bezout's equation and Divi-sion algorithm are equivalent to each other.Our result shows that Euclid has preliminarily established the theory of divisibility and the greatest common divisor.We further provided several suggestions for teaching.
基金National Natural Science Foundation of China(Grant No.61872083)the Natural Science Foundation of Guangdong Province,China(Grant Nos.2017A030310659 and 2019A1515011123).
文摘Denoising of chaotic signal is a challenge work due to its wide-band and noise-like characteristics.The algorithm should make the denoised signal have a high signal to noise ratio and retain the chaotic characteristics.We propose a denoising method of chaotic signals based on sparse decomposition and K-singular value decomposition(K-SVD)optimization.The observed signal is divided into segments and decomposed sparsely.The over-complete atomic library is constructed according to the differential equation of chaotic signals.The orthogonal matching pursuit algorithm is used to search the optimal matching atom.The atoms and coefficients are further processed to obtain the globally optimal atoms and coefficients by K-SVD.The simulation results show that the denoised signals have a higher signal to noise ratio and better preserve the chaotic characteristics.
基金Supported by the EDD of China(No.80912020104)the Science and Technology Commission of Shanghai Municipality(No.22ZR1427700 and No.23692106900).
文摘The traditional A^(*)algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,which is not conducive to the control of USV and also affects navigation safety.In this paper,these problems were addressed through the following improvements.First,the path search angle and security were comprehensively considered,and a security expansion strategy of nodes based on the 5×5 neighborhood was proposed.The A^(*)algorithm search neighborhood was expanded from 3×3 to 5×5,and safe nodes were screened out for extension via the node security expansion strategy.This algorithm can also optimize path search angles while improving path security.Second,the distance from the current node to the target node was introduced into the heuristic function.The efficiency of the A^(*)algorithm was improved,and the path was smoothed using the Floyd algorithm.For the dynamic adjustment of the weight to improve the efficiency of DWA,the distance from the USV to the target point was introduced into the evaluation function of the dynamic-window approach(DWA)algorithm.Finally,combined with the local target point selection strategy,the optimized DWA algorithm was performed for local path planning.The experimental results show the smooth and safe path planned by the fusion algorithm,which can successfully avoid dynamic obstacles and is effective and feasible in path planning for USVs.
文摘Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.
