In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and...In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and G(z)=∑^(N)_(i)=1 A_(−i)z^(i),A_(i)ae culants.展开更多
In Global Navigation Satellite System(GNSS)meteo rology,the atmospheric weighted mean temperatu re(T_(m))is a critical intermediate parameter for converting zenith wet delay(ZWD)to precipitable water vapor(PWV),essent...In Global Navigation Satellite System(GNSS)meteo rology,the atmospheric weighted mean temperatu re(T_(m))is a critical intermediate parameter for converting zenith wet delay(ZWD)to precipitable water vapor(PWV),essential for accurate atmospheric water content estimation.However,global models often overlook regional climatic variability,leading to reduced accuracy in localized applications.This study introduces an improved T_(m)model developed using radiosonde observations across Iran and GNSS radio occultation(RO)profiles from CHAMP,GRACE,MetOp-A/B/C,COSMIC,TerraSAR-X,and TanDEM-X missions collected between 2007 and 2022.A novel integral formulation was proposed to estimate T_(m)more accurately by incorporating vertical water vapor distribution and temperature linearity.Based on this formulation,three regional T_(m)models were constructed using annual,semiannual,and diurnal periodicities,along with surface temperature(T_(s)),each varying in structure and complexity.Validation against independent radiosonde observations from 2022 showed that Models Two and Three outperformed the Bevis model,reducing RMSE by 30.7%.When evaluated against GNSS RO profiles,Model One—excluding T_(s)due to its inaccessibility in RO data—yielded the highest accuracy,with a 42.6%improvement in RMSE over the Bevis model.To evaluate the practical effectiveness of the proposed T_(m)model,PWV was derived from GNSS data at the tehn and tabz stations during the second half of 2022and compared with PWV values obtained from co-located radiosonde observations in Tehran and Tabriz.Using T_(m)from Model One improved PWV estimation compared to the Bevis model,reducing RMSE and MAE by up to 54%and 53.8%in Tabriz and 50.6%and 52.9%in Tehran,respectively.These results demonstrate that regionalized T_(m)modeling,particularly approaches that avoid dependence on T_(s),can significantly enhance GNSS-based PWV estimation in areas with limited surface data.展开更多
Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies...Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies.Additionally,the non-linear nature of cost functions can cause algorithms to become trapped in local optima.Furthermore,there is often a lack of adequate consideration for real-world constraints,for example,due to the necessity for obstacle avoidance or because of the restrictions of flight safety.To address the aforementioned issues,this paper proposes a dynamic weighted spherical particle swarm optimization(DW-SPSO)algorithm.The algorithm adopts a dual Sigmoid-based adaptive weight adjustment mechanism for balancing global exploration and local exploitation,as well as a lens-based opposition learning one to improve search flexibility and solution diversity.Simulation experiments on real digital elevation models demonstrate that DW-SPSO significantly outperforms recent state-of-the-art particle swarm optimization(PSO)variants in terms of path safety,smoothness,and convergence speed.The performance superiority is statistically validated by the Wilcoxon signed-rank test.The results confirm the algorithm’s effectiveness in generating high-quality UAV paths under diverse threat conditions,offering a robust solution for autonomous navigation systems.展开更多
The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such...The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.展开更多
The Transformer has achieved great success in the field of medical image segmentation,but its quadratic computational complexity limits its application in dense medical image prediction.Recently,the receptance weighte...The Transformer has achieved great success in the field of medical image segmentation,but its quadratic computational complexity limits its application in dense medical image prediction.Recently,the receptance weighted key value(RWKV)architecture has garnered widespread attention due to its linear computational complexity and its capability of parallel computation during training.Despite the RWKV model's proficiency in addressing long-range modeling tasks with linear computational complexity,most current RWKV-based approaches employ static scanning patterns.These patterns may inadvertently incorporate biased prior knowledge into the model's predictions.To address this challenge,we propose a multi-head scan strategy combined with padding methods to effectively simulate spatial continuity in 2D images.Within the Feature Aggregation Attention(FAA)module,asymmetric convolutions are designed to aggregate 1D sequence features along a single dimension,thereby expanding effective receptive fields while preserving structural sparsity.Additionally,panoramic token shift(P-Shift)effectively models local dependency relationships by moving tokens from a wide receptive field.Extensive experiments conducted on the ISIC17/18 and ACDC datasets demonstrate that our method exhibits superior performance in dense medical image prediction tasks.展开更多
In eld seismic data acquisition,seismic traces are often aected by substantial data gaps and strong noise interference due to environmental and instrumental factors,thus degrading the resolution and signalto-noise rat...In eld seismic data acquisition,seismic traces are often aected by substantial data gaps and strong noise interference due to environmental and instrumental factors,thus degrading the resolution and signalto-noise ratio(SNR)of the seismic profiles.Effective seismic data reconstruction and noise suppression techniques are therefore essential to recover missing signals and improve data quality.In this study,a fast projection onto convex sets(FPOCS)algorithm is proposed by incorporating an inertial parameter that involves a linear combination of the two preceding iterations based on the traditional projection onto convex sets(POCS)algorithm.Then,a weighting factor is introduced to achieve simultaneous data reconstruction and noise suppression using the weighted fast projection onto convex sets(WFPOCS)algorithm.To further suppress residual random noise in the updated solution,an optimization strategy is adopted by swapping the order of the iterative hard thresholding operator and the projection operator.The nal algorithm,termed the improved weighted fast projection onto convex sets(IWFPOCS),achieves high-efciency reconstruction and effective noise suppression.Compared with WFPOCS,the proposed method maintains fast reconstruction speed while demonstrating superior denoising performance on irregularly missing and noisy datasets.Field data experiments conrm that the proposed method signicantly improves the SNR and resolution of seismic data,oering strong practical potential for subsequent processing and interpretation.展开更多
Inhomogeneous Calderon-Zygmund operator T maps each atom into an approximate molecule of weighted local Hardy space if and only if some approximate cancellation condition holds for T.An equivalent norm for weighted Le...Inhomogeneous Calderon-Zygmund operator T maps each atom into an approximate molecule of weighted local Hardy space if and only if some approximate cancellation condition holds for T.An equivalent norm for weighted Lebesgue space which has vanishing moments up to order s plays an important role,where s∈N.展开更多
In this paper,the Paley-Wiener theorem is extended to the analytic function spaces with general weights.We first generalize the theorem to weighted Hardy spaces Hp(0<p<∞)on tube domains by constructing a sequen...In this paper,the Paley-Wiener theorem is extended to the analytic function spaces with general weights.We first generalize the theorem to weighted Hardy spaces Hp(0<p<∞)on tube domains by constructing a sequence of L^(1)functions converging to the given function and verifying their representation in the form of Fourier transform to establish the desired result of the given function.Applying this main result,we further generalize the Paley-Wiener theorem for band-limited functions to the analytic function spaces L^(p)(0<p<∞)with general weights.展开更多
The boundness and compactness of products of multiplication,composition and differentiation on weighted Bergman spaces in the unit ball are studied.We define the differentiation operator on the space of holomorphic fu...The boundness and compactness of products of multiplication,composition and differentiation on weighted Bergman spaces in the unit ball are studied.We define the differentiation operator on the space of holomorphic functions in the unit ball by radial derivative.Then we extend the Sharma's results.展开更多
The aim of the present paper is to study 2-complex symmetric bounded weighted composition operators on the Fock space of C^(N) with the conjugations J and J_(t,A,b) defined by ■ respectively,where k(z_(1),...,z_N)=(...The aim of the present paper is to study 2-complex symmetric bounded weighted composition operators on the Fock space of C^(N) with the conjugations J and J_(t,A,b) defined by ■ respectively,where k(z_(1),...,z_N)=(■,...,■),t∈C,b∈C^(N) and A is a linear operator on C^(N).An example of 2-complex symmetric bounded weighted composition operator with the conjugation J_(t,A,b) is given.展开更多
The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the...The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the equivalent conditions of complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space.The results complement the corresponding results in probability space to those for sequences of independent,identically distributed random variables under sublinear expectation space.展开更多
The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show...The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data,as they rely on a single-dimensional membership value.To overcome these limitations,we propose an auto-weighted multi-view neutrosophic fuzzy clustering(AW-MVNFC)algorithm.Our method leverages the neutrosophic framework,an extension of fuzzy sets,to explicitly model imprecision and ambiguity through three membership degrees.The core novelty of AWMVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view.Through a unified objective function,AW-MVNFC jointly optimizes the neutrosophic membership assignments,cluster centers,and the distributions of view and feature weights.Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods,demonstrating its effectiveness in handling the complexities of multi-view data.展开更多
Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptibl...Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.展开更多
The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV imag...The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV images,which is inspired by the Retinex theory and guided by a light weighted map.Firstly,we propose a new network for reflectance component processing to suppress the noise in images.Secondly,we construct an illumination enhancement module that uses a light weighted map to guide the enhancement process.Finally,the processed reflectance and illumination components are recombined to obtain the enhancement results.Experimental results show that our method can suppress the noise in images while enhancing image brightness,and prevent over enhancement in bright regions.Code and data are available at https://gitee.com/baixiaotong2/uav-images.git.展开更多
The weighted Drazin invertibility of rectangular matrixs over an arbitrary ring are studied.Some equivalent conditions and Characterizations are given for existence of the weighted Drazin inverse of a rectangular matr...The weighted Drazin invertibility of rectangular matrixs over an arbitrary ring are studied.Some equivalent conditions and Characterizations are given for existence of the weighted Drazin inverse of a rectangular matrix over an arbitrary ring.Moreover,the weighted Drazin inverse of a rectangular matrices product PAQ can be characterized and computed.This generalizes results obtained for the Drazin inverse of such product of square matrices.The results also apply to morphisms in(additive)categories.展开更多
In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergen...In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergence of NSD random variables.We establish and improve a general result on the complete f-moment convergence for Sung’s type randomly weighted sums of NSD random variables under some general assumptions.As an application,we show the complete consistency for the randomly weighted estimator in a nonparametric regression model based on NSD errors.展开更多
We study certain weighted Bergman and weighted Besov spaces of holomorphic functions in the polydisk and in the unit ball.We seek conditions on the weight functions to guarantee that the dilations of a given function ...We study certain weighted Bergman and weighted Besov spaces of holomorphic functions in the polydisk and in the unit ball.We seek conditions on the weight functions to guarantee that the dilations of a given function converge to the same function in norm;in particular,we seek conditions on the weights to ensure that the analytic polynomials are dense in the space.展开更多
Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology.The combination of domain knowledge and fuzzy clustering algorithms has some problems,such as ini...Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology.The combination of domain knowledge and fuzzy clustering algorithms has some problems,such as initialization sensitivity and information granule weight optimization.Therefore,we propose a weighted kernel fuzzy clustering algorithm based on a relative density view(RDVWKFC).Compared with the traditional density-based methods,RDVWKFC can capture the intrinsic structure of the data more accurately,thus improving the initial quality of the clustering.By introducing a Relative Density based Knowledge Extraction Method(RDKM)and adaptive weight optimization mechanism,we effectively solve the limitations of view initialization and information granule weight optimization.RDKM can accurately identify high-density regions and optimize the initialization process.The adaptive weight mechanism can reduce noise and outliers’interference in the initial cluster centre selection by dynamically allocating weights.Experimental results on 14 benchmark datasets show that the proposed algorithm is superior to the existing algorithms in terms of clustering accuracy,stability,and convergence speed.It shows adaptability and robustness,especially when dealing with different data distributions and noise interference.Moreover,RDVWKFC can also show significant advantages when dealing with data with complex structures and high-dimensional features.These advancements provide versatile tools for real-world applications such as bioinformatics,image segmentation,and anomaly detection.展开更多
Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribu...Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribute-based conditional proxy re-encryption(AB-CPRE)schemes proposed so far do not take into account the importance of user attributes.A weighted attribute-based conditional proxy re-encryption(WAB-CPRE)scheme is thus designed to provide more precise decryption rights delegation.By introducing the concept of weight attributes,the quantity of system attributes managed by the server is reduced greatly.At the same time,a weighted tree structure is constructed to simplify the expression of access structure effectively.With conditional proxy re-encryption,large amounts of data and complex computations are outsourced to cloud servers,so the data owner(DO)can revoke the user’s decryption rights directly with minimal costs.The scheme proposed achieves security against chosen plaintext attacks(CPA).Experimental simulation results demonstrated that the decryption time is within 6–9 ms,and it has a significant reduction in communication and computation cost on the user side with better functionality compared to other related schemes,which enables users to access cloud data on devices with limited resources.展开更多
In the article,we provide a sharp lower bound for the weighted Lehmer mean of the complete p-elliptic integrals of the first and second kinds,which is the extension of the previous results for complete p-elliptic inte...In the article,we provide a sharp lower bound for the weighted Lehmer mean of the complete p-elliptic integrals of the first and second kinds,which is the extension of the previous results for complete p-elliptic integrals.展开更多
文摘In this paper,we present a necessary and sufficient condition for hyponormal block Toeplitz operators T on the vector-valued weighted Bergman space with symbolsΦ(z)=G^(*)(z)+F(z),where F(z)=∑^(N)_(i)=1 A_(i)z^(i)and G(z)=∑^(N)_(i)=1 A_(−i)z^(i),A_(i)ae culants.
文摘In Global Navigation Satellite System(GNSS)meteo rology,the atmospheric weighted mean temperatu re(T_(m))is a critical intermediate parameter for converting zenith wet delay(ZWD)to precipitable water vapor(PWV),essential for accurate atmospheric water content estimation.However,global models often overlook regional climatic variability,leading to reduced accuracy in localized applications.This study introduces an improved T_(m)model developed using radiosonde observations across Iran and GNSS radio occultation(RO)profiles from CHAMP,GRACE,MetOp-A/B/C,COSMIC,TerraSAR-X,and TanDEM-X missions collected between 2007 and 2022.A novel integral formulation was proposed to estimate T_(m)more accurately by incorporating vertical water vapor distribution and temperature linearity.Based on this formulation,three regional T_(m)models were constructed using annual,semiannual,and diurnal periodicities,along with surface temperature(T_(s)),each varying in structure and complexity.Validation against independent radiosonde observations from 2022 showed that Models Two and Three outperformed the Bevis model,reducing RMSE by 30.7%.When evaluated against GNSS RO profiles,Model One—excluding T_(s)due to its inaccessibility in RO data—yielded the highest accuracy,with a 42.6%improvement in RMSE over the Bevis model.To evaluate the practical effectiveness of the proposed T_(m)model,PWV was derived from GNSS data at the tehn and tabz stations during the second half of 2022and compared with PWV values obtained from co-located radiosonde observations in Tehran and Tabriz.Using T_(m)from Model One improved PWV estimation compared to the Bevis model,reducing RMSE and MAE by up to 54%and 53.8%in Tabriz and 50.6%and 52.9%in Tehran,respectively.These results demonstrate that regionalized T_(m)modeling,particularly approaches that avoid dependence on T_(s),can significantly enhance GNSS-based PWV estimation in areas with limited surface data.
基金supported by the National Natural Science Foundation of China(Grant No.62106092)the Natural Science Foundation of Fujian Province(Grant Nos.2024J01822,2025J01981)the Natural Science Foundation of Zhangzhou City(Grant No.ZZ2024J28).
文摘Path planning for Unmanned Aerial Vehicles(UAVs)in complex environments presents several challenges.Traditional algorithms often struggle with the complexity of high-dimensional search spaces,leading to inefficiencies.Additionally,the non-linear nature of cost functions can cause algorithms to become trapped in local optima.Furthermore,there is often a lack of adequate consideration for real-world constraints,for example,due to the necessity for obstacle avoidance or because of the restrictions of flight safety.To address the aforementioned issues,this paper proposes a dynamic weighted spherical particle swarm optimization(DW-SPSO)algorithm.The algorithm adopts a dual Sigmoid-based adaptive weight adjustment mechanism for balancing global exploration and local exploitation,as well as a lens-based opposition learning one to improve search flexibility and solution diversity.Simulation experiments on real digital elevation models demonstrate that DW-SPSO significantly outperforms recent state-of-the-art particle swarm optimization(PSO)variants in terms of path safety,smoothness,and convergence speed.The performance superiority is statistically validated by the Wilcoxon signed-rank test.The results confirm the algorithm’s effectiveness in generating high-quality UAV paths under diverse threat conditions,offering a robust solution for autonomous navigation systems.
基金Under the auspices of the National Natural Science Foundation of China(No.42271224,41901193)Ministry of Edu cation Humanities and Social Sciences Research Planning Fund Project of China(No.24YJAZH190)+1 种基金Anhui Province Excellent Youth Research Project in Universities(No.2022AH030019)Anhui Social Sciences Innovation Development Research Project(No.2024CXQ503)。
文摘The accessibility of urban public transit directly influences residents’quality of life,travel behavior,and social equity.Its correlation with housing prices has garnered significant attention across disciplines such as geography,economics,and urban planning.Although much existing research focuses on the impact of individual transportation facilities on housing prices,there is a notable gap in comprehensive analyses that assess the influence of overall urban transit accessibility on housing market dynamics.This study selected the main urban area of Hefei,China,as a case to investigate the spatial distribution of housing prices and evaluate public transit accessibility in 2022.Employing techniques such as the optimized parameter geographical detector and local spatial regression models,the study aimed to elucidate the effects and underlying mechanisms of urban transit accessibility on housing prices.The findings revealed that:1)housing prices in Hefei exhibited a clustered spatial pattern,with high prices concentrated in the city center and lower prices in peripheral areas,forming three distinct high-price hotspots with a‘belt-like’distribution;2)public transit accessibility showed a‘coreperiphery’structure,with accessibility declining in a‘circumferential’pattern around the city center.Based on the‘housing price-accessibility’dimension,four categories were identified:high price-high accessibility(37.25%),high price-low accessibility(19.07%),low price-high accessibility(21.95%),and low price-low accessibility(21.73%);3)the impact of transit accessibility on housing prices was spatially heterogeneous,with bus travel showing the strongest explanatory power(0.692),followed by automobile,subway,and bicycle travel.The interaction of these transportation modes generated a synergistic effect on housing price differentiation,with most influencing factors contributing more than 25%.These findings offer valuable insights for optimizing the spatial distribution of public transit infrastructure and improving both urban housing quality and residents’living standards.
基金Supported by Zhejiang Provincial Natural Science Foundation of China(LY22F020025)the National Natural Science Foundation of China(62072126)。
文摘The Transformer has achieved great success in the field of medical image segmentation,but its quadratic computational complexity limits its application in dense medical image prediction.Recently,the receptance weighted key value(RWKV)architecture has garnered widespread attention due to its linear computational complexity and its capability of parallel computation during training.Despite the RWKV model's proficiency in addressing long-range modeling tasks with linear computational complexity,most current RWKV-based approaches employ static scanning patterns.These patterns may inadvertently incorporate biased prior knowledge into the model's predictions.To address this challenge,we propose a multi-head scan strategy combined with padding methods to effectively simulate spatial continuity in 2D images.Within the Feature Aggregation Attention(FAA)module,asymmetric convolutions are designed to aggregate 1D sequence features along a single dimension,thereby expanding effective receptive fields while preserving structural sparsity.Additionally,panoramic token shift(P-Shift)effectively models local dependency relationships by moving tokens from a wide receptive field.Extensive experiments conducted on the ISIC17/18 and ACDC datasets demonstrate that our method exhibits superior performance in dense medical image prediction tasks.
基金supported in part by the Foundation of National Key Laboratory of Uranium Resources Exploration-Mining and Nuclear Remote Sensing under Grant 2024QZ-TD-13in part by the National Natural Science Foundation of China under Grant 42564006+1 种基金in part by the Natural Science Foundation of Jiangxi Province under Grant 20242BAB26051in part by the Open Fund of SINOPEC Key Laboratory of Geophysics,and in part by support the plan of Ganpo Juncai under Grant 20243BCE51012.
文摘In eld seismic data acquisition,seismic traces are often aected by substantial data gaps and strong noise interference due to environmental and instrumental factors,thus degrading the resolution and signalto-noise ratio(SNR)of the seismic profiles.Effective seismic data reconstruction and noise suppression techniques are therefore essential to recover missing signals and improve data quality.In this study,a fast projection onto convex sets(FPOCS)algorithm is proposed by incorporating an inertial parameter that involves a linear combination of the two preceding iterations based on the traditional projection onto convex sets(POCS)algorithm.Then,a weighting factor is introduced to achieve simultaneous data reconstruction and noise suppression using the weighted fast projection onto convex sets(WFPOCS)algorithm.To further suppress residual random noise in the updated solution,an optimization strategy is adopted by swapping the order of the iterative hard thresholding operator and the projection operator.The nal algorithm,termed the improved weighted fast projection onto convex sets(IWFPOCS),achieves high-efciency reconstruction and effective noise suppression.Compared with WFPOCS,the proposed method maintains fast reconstruction speed while demonstrating superior denoising performance on irregularly missing and noisy datasets.Field data experiments conrm that the proposed method signicantly improves the SNR and resolution of seismic data,oering strong practical potential for subsequent processing and interpretation.
文摘Inhomogeneous Calderon-Zygmund operator T maps each atom into an approximate molecule of weighted local Hardy space if and only if some approximate cancellation condition holds for T.An equivalent norm for weighted Lebesgue space which has vanishing moments up to order s plays an important role,where s∈N.
基金Supported by the National Natural Science Foundation of China(12301101)the Guangdong Basic and Applied Basic Research Foundation(2022A1515110019 and 2020A1515110585)。
文摘In this paper,the Paley-Wiener theorem is extended to the analytic function spaces with general weights.We first generalize the theorem to weighted Hardy spaces Hp(0<p<∞)on tube domains by constructing a sequence of L^(1)functions converging to the given function and verifying their representation in the form of Fourier transform to establish the desired result of the given function.Applying this main result,we further generalize the Paley-Wiener theorem for band-limited functions to the analytic function spaces L^(p)(0<p<∞)with general weights.
基金Supported by Natural Science Foundation of Guangdong Province in China(2018KTSCX161)。
文摘The boundness and compactness of products of multiplication,composition and differentiation on weighted Bergman spaces in the unit ball are studied.We define the differentiation operator on the space of holomorphic functions in the unit ball by radial derivative.Then we extend the Sharma's results.
基金Supported by Sichuan Science and Technology Program (No.2022ZYD0010)。
文摘The aim of the present paper is to study 2-complex symmetric bounded weighted composition operators on the Fock space of C^(N) with the conjugations J and J_(t,A,b) defined by ■ respectively,where k(z_(1),...,z_N)=(■,...,■),t∈C,b∈C^(N) and A is a linear operator on C^(N).An example of 2-complex symmetric bounded weighted composition operator with the conjugation J_(t,A,b) is given.
基金supported by Doctoral Scientific Research Starting Foundation of Jingdezhen Ceramic University(Grant No.102/01003002031)Re-accompanying Funding Project of Academic Achievements of Jingdezhen Ceramic University(Grant Nos.215/20506277,215/20506341)。
文摘The complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space is studied.By moment inequality and truncation methods,we establish the equivalent conditions of complete convergence for weighted sums of sequences of independent,identically distributed random variables under sublinear expectation space.The results complement the corresponding results in probability space to those for sequences of independent,identically distributed random variables under sublinear expectation space.
文摘The increasing prevalence of multi-view data has made multi-view clustering a crucial technique for discovering latent structures from heterogeneous representations.However,traditional fuzzy clustering algorithms show limitations with the inherent uncertainty and imprecision of such data,as they rely on a single-dimensional membership value.To overcome these limitations,we propose an auto-weighted multi-view neutrosophic fuzzy clustering(AW-MVNFC)algorithm.Our method leverages the neutrosophic framework,an extension of fuzzy sets,to explicitly model imprecision and ambiguity through three membership degrees.The core novelty of AWMVNFC lies in a hierarchical weighting strategy that adaptively learns the contributions of both individual data views and the importance of each feature within a view.Through a unified objective function,AW-MVNFC jointly optimizes the neutrosophic membership assignments,cluster centers,and the distributions of view and feature weights.Comprehensive experiments conducted on synthetic and real-world datasets demonstrate that our algorithm achieves more accurate and stable clustering than existing methods,demonstrating its effectiveness in handling the complexities of multi-view data.
文摘Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.
基金supported by the National Natural Science Foundation of China(Nos.62201454 and 62306235)the Xi’an Science and Technology Program of Xi’an Science and Technology Bureau(No.23SFSF0004)。
文摘The unmanned aerial vehicle(UAV)images captured under low-light conditions are often suffering from noise and uneven illumination.To address these issues,we propose a low-light image enhancement algorithm for UAV images,which is inspired by the Retinex theory and guided by a light weighted map.Firstly,we propose a new network for reflectance component processing to suppress the noise in images.Secondly,we construct an illumination enhancement module that uses a light weighted map to guide the enhancement process.Finally,the processed reflectance and illumination components are recombined to obtain the enhancement results.Experimental results show that our method can suppress the noise in images while enhancing image brightness,and prevent over enhancement in bright regions.Code and data are available at https://gitee.com/baixiaotong2/uav-images.git.
文摘The weighted Drazin invertibility of rectangular matrixs over an arbitrary ring are studied.Some equivalent conditions and Characterizations are given for existence of the weighted Drazin inverse of a rectangular matrix over an arbitrary ring.Moreover,the weighted Drazin inverse of a rectangular matrices product PAQ can be characterized and computed.This generalizes results obtained for the Drazin inverse of such product of square matrices.The results also apply to morphisms in(additive)categories.
基金supported by the National Social Science Fundation(Grant No.21BTJ040)the Project of Outstanding Young People in University of Anhui Province(Grant Nos.2023AH020037,SLXY2024A001).
文摘In this paper,by utilizing the Marcinkiewicz-Zygmund inequality and Rosenthal-type inequality of negatively superadditive dependent(NSD)random arrays and truncated method,we investigate the complete f-moment convergence of NSD random variables.We establish and improve a general result on the complete f-moment convergence for Sung’s type randomly weighted sums of NSD random variables under some general assumptions.As an application,we show the complete consistency for the randomly weighted estimator in a nonparametric regression model based on NSD errors.
文摘We study certain weighted Bergman and weighted Besov spaces of holomorphic functions in the polydisk and in the unit ball.We seek conditions on the weight functions to guarantee that the dilations of a given function converge to the same function in norm;in particular,we seek conditions on the weights to ensure that the analytic polynomials are dense in the space.
文摘Applying domain knowledge in fuzzy clustering algorithms continuously promotes the development of clustering technology.The combination of domain knowledge and fuzzy clustering algorithms has some problems,such as initialization sensitivity and information granule weight optimization.Therefore,we propose a weighted kernel fuzzy clustering algorithm based on a relative density view(RDVWKFC).Compared with the traditional density-based methods,RDVWKFC can capture the intrinsic structure of the data more accurately,thus improving the initial quality of the clustering.By introducing a Relative Density based Knowledge Extraction Method(RDKM)and adaptive weight optimization mechanism,we effectively solve the limitations of view initialization and information granule weight optimization.RDKM can accurately identify high-density regions and optimize the initialization process.The adaptive weight mechanism can reduce noise and outliers’interference in the initial cluster centre selection by dynamically allocating weights.Experimental results on 14 benchmark datasets show that the proposed algorithm is superior to the existing algorithms in terms of clustering accuracy,stability,and convergence speed.It shows adaptability and robustness,especially when dealing with different data distributions and noise interference.Moreover,RDVWKFC can also show significant advantages when dealing with data with complex structures and high-dimensional features.These advancements provide versatile tools for real-world applications such as bioinformatics,image segmentation,and anomaly detection.
基金Programs for Science and Technology Development of Henan Province,grant number 242102210152The Fundamental Research Funds for the Universities of Henan Province,grant number NSFRF240620+1 种基金Key Scientific Research Project of Henan Higher Education Institutions,grant number 24A520015Henan Key Laboratory of Network Cryptography Technology,grant number LNCT2022-A11.
文摘Conditional proxy re-encryption(CPRE)is an effective cryptographic primitive language that enhances the access control mechanism and makes the delegation of decryption permissions more granular,but most of the attribute-based conditional proxy re-encryption(AB-CPRE)schemes proposed so far do not take into account the importance of user attributes.A weighted attribute-based conditional proxy re-encryption(WAB-CPRE)scheme is thus designed to provide more precise decryption rights delegation.By introducing the concept of weight attributes,the quantity of system attributes managed by the server is reduced greatly.At the same time,a weighted tree structure is constructed to simplify the expression of access structure effectively.With conditional proxy re-encryption,large amounts of data and complex computations are outsourced to cloud servers,so the data owner(DO)can revoke the user’s decryption rights directly with minimal costs.The scheme proposed achieves security against chosen plaintext attacks(CPA).Experimental simulation results demonstrated that the decryption time is within 6–9 ms,and it has a significant reduction in communication and computation cost on the user side with better functionality compared to other related schemes,which enables users to access cloud data on devices with limited resources.
基金Supported by the National Natural Science Foundation of China(11971142)the Natural Science Foundation of Zhejiang Province(LY19A010012)the key Scientific Research Projects of Hunan Provincial Department of Education in 2021(21A0526)。
文摘In the article,we provide a sharp lower bound for the weighted Lehmer mean of the complete p-elliptic integrals of the first and second kinds,which is the extension of the previous results for complete p-elliptic integrals.
