We propose a novel metasurface based on a combined pattern of outer C-shaped ring and inner rectangular ring.By Fourier convolution operation to generating different predesigned sequences of metasurfaces,we realize va...We propose a novel metasurface based on a combined pattern of outer C-shaped ring and inner rectangular ring.By Fourier convolution operation to generating different predesigned sequences of metasurfaces,we realize various functionalities to flexible manipulate terahertz waves including vortex terahertz beam splitting,anomalous vortex terahertz wave deflection,vortex terahertz wave splitting and deflection simultaneously.The incident terahertz wave can be flexibly controlled in a single metasurface.The designed metasurface has an extensive application prospect in the field of future terahertz communication and sensing.展开更多
By using a certain hybrid-type convolution operator,we first introduce a new subclass of normalized analytic functions in the open unit disk.For members of this analytic function class,we then derive several propertie...By using a certain hybrid-type convolution operator,we first introduce a new subclass of normalized analytic functions in the open unit disk.For members of this analytic function class,we then derive several properties and characteristics including(for example)the modified Hadamard products,Holder's inequalities and convolution properties as well as some closure properties under a general family of integral transforms.展开更多
Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton s...Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused.Moreover,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes shared.Existing Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations efficiently.To solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different views.To make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the graph.Based on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation network.Hourglass-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features efficiently.Experimental results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimation accuracy.展开更多
Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional a...Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational efficiency. Key architectural components such as convolution operations, shallow and deep blocks, skip connections, and hybrid encoders are examined for their roles in enhancing spatial representation and semantic consistency. We further discuss the importance of hierarchical and instance-aware segmentation and annotation in interpreting complex biological scenes and multiplexed medical images. By bridging methodological developments with diverse application domains, this paper outlines current trends and future directions for semantic segmentation, emphasizing its critical role in facilitating annotation, diagnosis, and discovery in biomedical research.展开更多
Visual tracking is a classical computer vision problem with many applications.Efficient convolution operators(ECO)is one of the most outstanding visual tracking algorithms in recent years,it has shown great performanc...Visual tracking is a classical computer vision problem with many applications.Efficient convolution operators(ECO)is one of the most outstanding visual tracking algorithms in recent years,it has shown great performance using discriminative correlation filter(DCF)together with HOG,color maps and VGGNet features.Inspired by new deep learning models,this paper propose a hybrid efficient convolution operators integrating fully convolution network(FCN)and residual network(ResNet)for visual tracking,where FCN and ResNet are introduced in our proposed method to segment the objects from backgrounds and extract hierarchical feature maps of objects,respectively.Compared with the traditional VGGNet,our approach has higher accuracy for dealing with the issues of segmentation and image size.The experiments show that our approach would obtain better performance than ECO in terms of precision plot and success rate plot on OTB-2013 and UAV123 datasets.展开更多
In this paper some Voronovskaya approximation formulae for a class of Mellin convolution operators of the type （Twf）（x,y）=∫R^2＋Kw（tx^-1,vy^-1）f（t,v）dtdv/tv are given. Moreover, various examples are discussed.
In this paper,we use the method of representation of Lie group to study a class of nonhomoge- neous convolution operator on the nilpotent Lie group H^M×R^k,and give a criteerion of their hypoellipticity.
Stability of infinite matrices has important applications to spline approximation, wavelets, Gabor time-frequency analysis, etc. In this paper, perturbation analysis for convolution dominated infinite matrices was stu...Stability of infinite matrices has important applications to spline approximation, wavelets, Gabor time-frequency analysis, etc. In this paper, perturbation analysis for convolution dominated infinite matrices was studied by introducing an idea of lp-stability at infinity. For infinite matrices in the Gohberg-Baskakov-Sjostrand class, a practical criterion for the lp-stability at infinity of convolution dominated infinite matrices on Zd under perturbation of compact operators was given.展开更多
In this paper, some approximation formulae for a class of convolution type double singular integral operators depending on three parameters of the type(T_λf)(x, y) = ∫_a^b ∫_a^b f(t, s)K_λ(t-x,s-y)dsdt, x,y ∈(a,...In this paper, some approximation formulae for a class of convolution type double singular integral operators depending on three parameters of the type(T_λf)(x, y) = ∫_a^b ∫_a^b f(t, s)K_λ(t-x,s-y)dsdt, x,y ∈(a,b), λ ∈ Λ [0,∞),(0.1)are given. Here f belongs to the function space L_1( <a,b >~2), where <a,b> is an arbitrary interval in R. In this paper three theorems are proved, one for existence of the operator(T_λf)(x, y) and the others for its Fatou-type pointwise convergence to f(x_0, y_0), as(x,y,λ) tends to(x_0, y_0, λ_0). In contrast to previous works, the kernel functions K_λ(u,v)don't have to be 2π-periodic, positive, even and radial. Our results improve and extend some of the previous results of [1, 6, 8, 10, 11, 13] in three dimensional frame and especially the very recent paper [15].展开更多
Here concerned and further investigated is a certain operator method for the computation of convolutions of polynomials.We provide a general formulation of the method with a refinement for certain old results,and also...Here concerned and further investigated is a certain operator method for the computation of convolutions of polynomials.We provide a general formulation of the method with a refinement for certain old results,and also give some new applications to convolved sums involving several noted special polynomials.The advantage of the method using operators is illustrated with concrete examples.Finally,also presented is a brief investigation on convolution polynomials having two types of summations.展开更多
With the rapid progress in data-driven approaches,artificial intelligence,and big data analytics technologies,utilizing electroencephalogram(EEG)signals for emotion analysis in the field of the Internet of Medical Thi...With the rapid progress in data-driven approaches,artificial intelligence,and big data analytics technologies,utilizing electroencephalogram(EEG)signals for emotion analysis in the field of the Internet of Medical Things can effectively assist in the diagnosis of specific diseases.While existing emotion analysis methods focus on the utilization of effective deep models for data-driven and big data analytics technology,they often struggle to extract long-range dependencies and accurately model local relationships within multi-channel EEG signals.In addition,the subjective scores of the subjects may not match the predefined emotional labels.To overcome these limitations,this paper proposes a new data-driven dynamic graph-embedded Transformer network(DGETN)that has emerged in different tasks of graph data mining for emotion analysis of EEG signals in the scene of IoMT.Firstly,we extract the frequency features differential entropy(DE)and use the linear dynamic system(LDS)method to alleviate the redundancy and noise information.Secondly,to effectively explore the long-range information and local modeling ability,a novel feature extraction module is designed by embedding the dynamic graph convolution operations in the Transformer encoder for mining the discriminant features of data.Moreover,the graph convolution operations can effectively exploit the spatial information between different channels.At last,we introduce the minimum category confusion(MCC)loss to alleviate the fuzziness of classification.We take two commonly used EEG sentiment analysis datasets as a study.The DGETN has achieved state-of-the-art accuracies of 99.38%on the SEED dataset,and accuracies of 99.24%and 98.85%for valence and arousal prediction on the DEAP dataset,respectively.展开更多
As the key part of Prognostics and Health Management（PHM）, Remaining Useful Life（RUL） estimation has been extensively investigated in recent years. Current RUL estimation studies considering the intervention of im...As the key part of Prognostics and Health Management（PHM）, Remaining Useful Life（RUL） estimation has been extensively investigated in recent years. Current RUL estimation studies considering the intervention of imperfect maintenance activities usually assumed that maintenance activities have a single influence on the degradation level or degradation rate, but not on both.Aimed at this problem, this paper proposes a new degradation modeling and RUL estimation method taking the influence of imperfect maintenance activities on both the degradation level and the degradation rate into account. Toward this end, a stochastic degradation model considering imperfect maintenance activities is firstly constructed based on the diffusion process. Then, the Probability Density Function（PDF） of the RUL is derived by the convolution operator under the concept of First Hitting Time（FHT）. To implement the proposed RUL estimation method,the Maximum Likelihood Estimation（MLE） is utilized to estimate the degradation related parameters based on the Condition Monitoring（CM） data, while the Bayesian method is utilized to estimate the maintenance related parameters based on the maintenance data. Finally, a numerical example and a practical case study are provided to demonstrate the superiority of the proposed method. The experimental results show that the proposed method could greatly improve the RUL estimation accuracy for the degrading equipment subjected to imperfect maintenance activities.展开更多
The filter operator used in normal multichannel matching filter is physically realizable.This filter operator only delays seismic data in the filtering process.A noncausal multichannel matching filter based on a least...The filter operator used in normal multichannel matching filter is physically realizable.This filter operator only delays seismic data in the filtering process.A noncausal multichannel matching filter based on a least squares criterion is proposed to resolve the problem in which predicted multiple model data is Iater than real data.The diferences between causal and noncausal multichannel matching filters are compared using a synthetic shot gather,which demonstrates the validity of the noncausal matching filter.In addition,a variable length sliding window which changes with ofset and layer velocity is proposed to solve the count of events increasing with increasing ofset in a fixed Iength sliding window.This variable length sliding window is also introduced into the modified and expanded multichannel matching filter.This method is applied to the Pluto1.5 synthetic data set.The benefits of the non.causal filter operator and variable length sliding window are demonstrated bv the good multiple attenuation result.展开更多
A deep-learning-based method,called ConvLSTMP3,is developed to predict the sea surface heights(SSHs).ConvLSTMP3 is data-driven by treating the SSH prediction problem as the one of extracting the spatial-temporal featu...A deep-learning-based method,called ConvLSTMP3,is developed to predict the sea surface heights(SSHs).ConvLSTMP3 is data-driven by treating the SSH prediction problem as the one of extracting the spatial-temporal features of SSHs,in which the spatial features are“learned”by convolutional operations while the temporal features are tracked by long short term memory(LSTM).Trained by a reanalysis dataset of the South China Sea(SCS),ConvLSTMP3 is applied to the SSH prediction in a region of the SCS east off Vietnam coast featured with eddied and offshore currents in summer.Experimental results show that ConvLSTMP3 achieves a good prediction skill with a mean RMSE of 0.057 m and accuracy of 93.4%averaged over a 15-d prediction period.In particular,ConvLSTMP3 shows a better performance in predicting the temporal evolution of mesoscale eddies in the region than a full-dynamics ocean model.Given the much less computation in the prediction required by ConvLSTMP3,our study suggests that the deep learning technique is very useful and effective in the SSH prediction,and could be an alternative way in the operational prediction for ocean environments in the future.展开更多
The existence, uniqueness of bounded and continuous solutions of a class of integrodifferential equations and some estimates of solutions are established. Applying these results to integrodifferential systems with a s...The existence, uniqueness of bounded and continuous solutions of a class of integrodifferential equations and some estimates of solutions are established. Applying these results to integrodifferential systems with a small parameter ε>0 , we obtain, in particular, some estimates of solutions uniform in ε.展开更多
In this paper, we use the methods of differential subordination and the properties of convolution to investigate the class Wp（H（ai, bj）; φ） of multivalent analytic functions, which is defined by the Dziok-Srivast...In this paper, we use the methods of differential subordination and the properties of convolution to investigate the class Wp（H（ai, bj）; φ） of multivalent analytic functions, which is defined by the Dziok-Srivastava operator H（a1,..., aq; b1,..., bs）. Some inclusion properties for this class are obtained.展开更多
New higher dimensional distributions are introduced in the framework of Clifford analysis.They complete the picture already established in previous work, offering unity and structuralclarity. Amongst them are the buil...New higher dimensional distributions are introduced in the framework of Clifford analysis.They complete the picture already established in previous work, offering unity and structuralclarity. Amongst them are the building blocks of the principal value distribution, involvingspherical harmonics, considered by Horvath and Stein.展开更多
Let G=H<sub>k</sub><sup>n</sup> be the（2n+1）-dimensional Heisenberg group over local field K.In this paper we prove some theorems about convolution operators on H<sup>P</sup>（G）...Let G=H<sub>k</sub><sup>n</sup> be the（2n+1）-dimensional Heisenberg group over local field K.In this paper we prove some theorems about convolution operators on H<sup>P</sup>（G）and vector-valued Hardy spaces.As an example,the distribution ∫<sub>k<sup>*</sup></sub>dt/|t| for some ∈S（G）,/∫=0 is a ramified 0-type kernel.These results can be applied to characterize H<sup>P</sup>（G）spaces by square functions.展开更多
The paper is a contribution to the problem of approximating random set with values in a separable Banach space. This class of set-valued function is widely used in many areas.We investigate the properties of p-bounded...The paper is a contribution to the problem of approximating random set with values in a separable Banach space. This class of set-valued function is widely used in many areas.We investigate the properties of p-bounded integrable random set. Based on this we endow it with △p metric which can be viewed as a integral type hausdorff metric and present some approximation theorem of a class of convolution operators with respect to △p metric. Moreover we also can establish analogous theorem for other integral type operator in △p space.展开更多
Let 0【p≤1q【0, and w<sub>1</sub>, w<sub>2</sub> ∈A<sub>1</sub> （Muckenhoupt-class）. In this paper the authors prove that the strongly singular convolution operators are bounded...Let 0【p≤1q【0, and w<sub>1</sub>, w<sub>2</sub> ∈A<sub>1</sub> （Muckenhoupt-class）. In this paper the authors prove that the strongly singular convolution operators are bounded from the homogeneous weighted Herz-type Hardy spaces HK<sub>q</sub><sup>α,p</sup>（w<sub>1</sub>; w<sub>2</sub>） to the homogeneous weighted Herz spaces K<sub>q</sub><sup>α,p</sup>（w<sub>1</sub>;w<sub>2</sub>）, provided α=n（1--1/q）. Moreover, the boundedness of these operators on the non-homogeneous weighted Herz-type Hardy spaces HK<sub>q</sub><sup>α,p</sup>（w<sub>1</sub>, w<sub>2</sub>） is also investigated.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61871355 and 61831012)the Talent Project of Zhejiang Provincial Department of Science and Technology(Grant No.2018R52043)the Research Funds for Universities of Zhejiang Province,China(Grant Nos.2020YW20 and 2021YW86)。
文摘We propose a novel metasurface based on a combined pattern of outer C-shaped ring and inner rectangular ring.By Fourier convolution operation to generating different predesigned sequences of metasurfaces,we realize various functionalities to flexible manipulate terahertz waves including vortex terahertz beam splitting,anomalous vortex terahertz wave deflection,vortex terahertz wave splitting and deflection simultaneously.The incident terahertz wave can be flexibly controlled in a single metasurface.The designed metasurface has an extensive application prospect in the field of future terahertz communication and sensing.
文摘By using a certain hybrid-type convolution operator,we first introduce a new subclass of normalized analytic functions in the open unit disk.For members of this analytic function class,we then derive several properties and characteristics including(for example)the modified Hadamard products,Holder's inequalities and convolution properties as well as some closure properties under a general family of integral transforms.
基金supported in part by the National Natural Science Foundation of China under Grants 61973065,U20A20197,61973063.
文摘Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused.Moreover,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes shared.Existing Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations efficiently.To solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different views.To make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the graph.Based on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation network.Hourglass-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features efficiently.Experimental results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimation accuracy.
基金Open Access funding provided by the National Institutes of Health(NIH)The funding for this project was provided by NCATS Intramural Fund.
文摘Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational efficiency. Key architectural components such as convolution operations, shallow and deep blocks, skip connections, and hybrid encoders are examined for their roles in enhancing spatial representation and semantic consistency. We further discuss the importance of hierarchical and instance-aware segmentation and annotation in interpreting complex biological scenes and multiplexed medical images. By bridging methodological developments with diverse application domains, this paper outlines current trends and future directions for semantic segmentation, emphasizing its critical role in facilitating annotation, diagnosis, and discovery in biomedical research.
文摘Visual tracking is a classical computer vision problem with many applications.Efficient convolution operators(ECO)is one of the most outstanding visual tracking algorithms in recent years,it has shown great performance using discriminative correlation filter(DCF)together with HOG,color maps and VGGNet features.Inspired by new deep learning models,this paper propose a hybrid efficient convolution operators integrating fully convolution network(FCN)and residual network(ResNet)for visual tracking,where FCN and ResNet are introduced in our proposed method to segment the objects from backgrounds and extract hierarchical feature maps of objects,respectively.Compared with the traditional VGGNet,our approach has higher accuracy for dealing with the issues of segmentation and image size.The experiments show that our approach would obtain better performance than ECO in terms of precision plot and success rate plot on OTB-2013 and UAV123 datasets.
文摘In this paper some Voronovskaya approximation formulae for a class of Mellin convolution operators of the type （Twf）（x,y）=∫R^2＋Kw（tx^-1,vy^-1）f（t,v）dtdv/tv are given. Moreover, various examples are discussed.
文摘In this paper,we use the method of representation of Lie group to study a class of nonhomoge- neous convolution operator on the nilpotent Lie group H^M×R^k,and give a criteerion of their hypoellipticity.
基金National Natural Science Foundation of China(No.10971023)Fundamental Research Funds for the Central Universities of China
文摘Stability of infinite matrices has important applications to spline approximation, wavelets, Gabor time-frequency analysis, etc. In this paper, perturbation analysis for convolution dominated infinite matrices was studied by introducing an idea of lp-stability at infinity. For infinite matrices in the Gohberg-Baskakov-Sjostrand class, a practical criterion for the lp-stability at infinity of convolution dominated infinite matrices on Zd under perturbation of compact operators was given.
文摘In this paper, some approximation formulae for a class of convolution type double singular integral operators depending on three parameters of the type(T_λf)(x, y) = ∫_a^b ∫_a^b f(t, s)K_λ(t-x,s-y)dsdt, x,y ∈(a,b), λ ∈ Λ [0,∞),(0.1)are given. Here f belongs to the function space L_1( <a,b >~2), where <a,b> is an arbitrary interval in R. In this paper three theorems are proved, one for existence of the operator(T_λf)(x, y) and the others for its Fatou-type pointwise convergence to f(x_0, y_0), as(x,y,λ) tends to(x_0, y_0, λ_0). In contrast to previous works, the kernel functions K_λ(u,v)don't have to be 2π-periodic, positive, even and radial. Our results improve and extend some of the previous results of [1, 6, 8, 10, 11, 13] in three dimensional frame and especially the very recent paper [15].
文摘Here concerned and further investigated is a certain operator method for the computation of convolutions of polynomials.We provide a general formulation of the method with a refinement for certain old results,and also give some new applications to convolved sums involving several noted special polynomials.The advantage of the method using operators is illustrated with concrete examples.Finally,also presented is a brief investigation on convolution polynomials having two types of summations.
文摘With the rapid progress in data-driven approaches,artificial intelligence,and big data analytics technologies,utilizing electroencephalogram(EEG)signals for emotion analysis in the field of the Internet of Medical Things can effectively assist in the diagnosis of specific diseases.While existing emotion analysis methods focus on the utilization of effective deep models for data-driven and big data analytics technology,they often struggle to extract long-range dependencies and accurately model local relationships within multi-channel EEG signals.In addition,the subjective scores of the subjects may not match the predefined emotional labels.To overcome these limitations,this paper proposes a new data-driven dynamic graph-embedded Transformer network(DGETN)that has emerged in different tasks of graph data mining for emotion analysis of EEG signals in the scene of IoMT.Firstly,we extract the frequency features differential entropy(DE)and use the linear dynamic system(LDS)method to alleviate the redundancy and noise information.Secondly,to effectively explore the long-range information and local modeling ability,a novel feature extraction module is designed by embedding the dynamic graph convolution operations in the Transformer encoder for mining the discriminant features of data.Moreover,the graph convolution operations can effectively exploit the spatial information between different channels.At last,we introduce the minimum category confusion(MCC)loss to alleviate the fuzziness of classification.We take two commonly used EEG sentiment analysis datasets as a study.The DGETN has achieved state-of-the-art accuracies of 99.38%on the SEED dataset,and accuracies of 99.24%and 98.85%for valence and arousal prediction on the DEAP dataset,respectively.
基金co-supported by the National Science Foundation of China(NSFC)(Nos.61573365,61603398,61374126,61473094,and 61773386)the Young Talent Fund of University Association for Science and Technology in Shaanxi,Chinathe Young Elite Scientists Sponsorship Program(YESS)by China Association for Science and Technology(CAST)
文摘As the key part of Prognostics and Health Management（PHM）, Remaining Useful Life（RUL） estimation has been extensively investigated in recent years. Current RUL estimation studies considering the intervention of imperfect maintenance activities usually assumed that maintenance activities have a single influence on the degradation level or degradation rate, but not on both.Aimed at this problem, this paper proposes a new degradation modeling and RUL estimation method taking the influence of imperfect maintenance activities on both the degradation level and the degradation rate into account. Toward this end, a stochastic degradation model considering imperfect maintenance activities is firstly constructed based on the diffusion process. Then, the Probability Density Function（PDF） of the RUL is derived by the convolution operator under the concept of First Hitting Time（FHT）. To implement the proposed RUL estimation method,the Maximum Likelihood Estimation（MLE） is utilized to estimate the degradation related parameters based on the Condition Monitoring（CM） data, while the Bayesian method is utilized to estimate the maintenance related parameters based on the maintenance data. Finally, a numerical example and a practical case study are provided to demonstrate the superiority of the proposed method. The experimental results show that the proposed method could greatly improve the RUL estimation accuracy for the degrading equipment subjected to imperfect maintenance activities.
基金supported by the National 863 Program (Grant No. 2006AA09A102-09)the National 973 Program (GrantNo. 2007CB209606)
文摘The filter operator used in normal multichannel matching filter is physically realizable.This filter operator only delays seismic data in the filtering process.A noncausal multichannel matching filter based on a least squares criterion is proposed to resolve the problem in which predicted multiple model data is Iater than real data.The diferences between causal and noncausal multichannel matching filters are compared using a synthetic shot gather,which demonstrates the validity of the noncausal matching filter.In addition,a variable length sliding window which changes with ofset and layer velocity is proposed to solve the count of events increasing with increasing ofset in a fixed Iength sliding window.This variable length sliding window is also introduced into the modified and expanded multichannel matching filter.This method is applied to the Pluto1.5 synthetic data set.The benefits of the non.causal filter operator and variable length sliding window are demonstrated bv the good multiple attenuation result.
基金The National Key Research and Development Program under contract Nos 2018YFC1406204 and 2018YFC1406201the Guangdong Special Support Program under contract No.2019BT2H594+5 种基金the Taishan Scholar Foundation under contract No.tsqn201812029the National Natural Science Foundation of China under contract Nos U1811464,61572522,61572523,61672033,61672248,61873280,41676016 and 41776028the Natural Science Foundation of Shandong Province under contract Nos ZR2019MF012 and 2019GGX101067the Fundamental Research Funds of Central Universities under contract Nos 18CX02152A and 19CX05003A-6the fund of the Shandong Province Innovation Researching Group under contract No.2019KJN014the Key Special Project for Introduced Talents Team of the Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou)under contract No.GML2019ZD0303.
文摘A deep-learning-based method,called ConvLSTMP3,is developed to predict the sea surface heights(SSHs).ConvLSTMP3 is data-driven by treating the SSH prediction problem as the one of extracting the spatial-temporal features of SSHs,in which the spatial features are“learned”by convolutional operations while the temporal features are tracked by long short term memory(LSTM).Trained by a reanalysis dataset of the South China Sea(SCS),ConvLSTMP3 is applied to the SSH prediction in a region of the SCS east off Vietnam coast featured with eddied and offshore currents in summer.Experimental results show that ConvLSTMP3 achieves a good prediction skill with a mean RMSE of 0.057 m and accuracy of 93.4%averaged over a 15-d prediction period.In particular,ConvLSTMP3 shows a better performance in predicting the temporal evolution of mesoscale eddies in the region than a full-dynamics ocean model.Given the much less computation in the prediction required by ConvLSTMP3,our study suggests that the deep learning technique is very useful and effective in the SSH prediction,and could be an alternative way in the operational prediction for ocean environments in the future.
文摘The existence, uniqueness of bounded and continuous solutions of a class of integrodifferential equations and some estimates of solutions are established. Applying these results to integrodifferential systems with a small parameter ε>0 , we obtain, in particular, some estimates of solutions uniform in ε.
基金Supported by the National Natural Science Foundation of China(Grant No.11271045)the Funds of Doctoral Programme of China(Grant No.20100003110004)+1 种基金the Natural Science Foundation of Inner Mongolia Province(Grant Nos.2010MS01172014MS0101)
文摘In this paper, we use the methods of differential subordination and the properties of convolution to investigate the class Wp（H（ai, bj）; φ） of multivalent analytic functions, which is defined by the Dziok-Srivastava operator H（a1,..., aq; b1,..., bs）. Some inclusion properties for this class are obtained.
文摘New higher dimensional distributions are introduced in the framework of Clifford analysis.They complete the picture already established in previous work, offering unity and structuralclarity. Amongst them are the building blocks of the principal value distribution, involvingspherical harmonics, considered by Horvath and Stein.
文摘Let G=H<sub>k</sub><sup>n</sup> be the（2n+1）-dimensional Heisenberg group over local field K.In this paper we prove some theorems about convolution operators on H<sup>P</sup>（G）and vector-valued Hardy spaces.As an example,the distribution ∫<sub>k<sup>*</sup></sub>dt/|t| for some ∈S（G）,/∫=0 is a ramified 0-type kernel.These results can be applied to characterize H<sup>P</sup>（G）spaces by square functions.
基金the the Morningside Center of Mathematics of the Chinese Academy of Sciencesthe Program of "One Hundred Distinguished Chinese Scientists" of the Chinese Academy of Sciences.
文摘The paper is a contribution to the problem of approximating random set with values in a separable Banach space. This class of set-valued function is widely used in many areas.We investigate the properties of p-bounded integrable random set. Based on this we endow it with △p metric which can be viewed as a integral type hausdorff metric and present some approximation theorem of a class of convolution operators with respect to △p metric. Moreover we also can establish analogous theorem for other integral type operator in △p space.
基金the National Natural Science Foundation of China
文摘Let 0【p≤1q【0, and w<sub>1</sub>, w<sub>2</sub> ∈A<sub>1</sub> （Muckenhoupt-class）. In this paper the authors prove that the strongly singular convolution operators are bounded from the homogeneous weighted Herz-type Hardy spaces HK<sub>q</sub><sup>α,p</sup>（w<sub>1</sub>; w<sub>2</sub>） to the homogeneous weighted Herz spaces K<sub>q</sub><sup>α,p</sup>（w<sub>1</sub>;w<sub>2</sub>）, provided α=n（1--1/q）. Moreover, the boundedness of these operators on the non-homogeneous weighted Herz-type Hardy spaces HK<sub>q</sub><sup>α,p</sup>（w<sub>1</sub>, w<sub>2</sub>） is also investigated.
