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Multi-scale analysis of spatiotemporal evolution and driving factors of eco-environmental quality in a Ningxia irrigation district,China 认领 引用
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作者 LI Zequan CHAI Mingtang +4 位作者 ZHU Lei HE Junjie DING Yimin XU Fengkun XU Xiyuan 《Journal of Geographical Sciences》 SCIE CSCD 2026年第2期471-493,共23页
The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a mo... The Qingtongxia Irrigation District in Ningxia is an important hydrological and ecological region.To assess its ecological environment quality from 2001 to 2021 across multiple scales and identify driving factors,a modified remote sensing ecological index(MRSEI)was developed by incorporating evapotranspiration.Spatial and temporal patterns were analyzed using the coefficient of variation,spatial autocorrelation,and semi-variogram methods,while influencing factors were explored via the optimal parameter geographical detector model.The MRSEI’s first principal component loadings and rankings aligned with those of RSEI(average contribution:81.31%),effectively reflecting spatiotemporal variations.At sub-irrigation district and landscape scales,ecological quality was slightly lower than at the district level but remained stable.Moderate and good ecological grades accounted for 36.28%and 33.38%of the area,respectively,at the district scale,and the moderate grade reached 70.48%on smaller scales.Spatial heterogeneity intensified with decreasing scale,and human activity lost explanatory power below a 5 km range.Human factors mainly drove ecological differentiation at the district scale,while natural factors dominated at finer scales.The MRSEI offers a novel tool for ecological assessment in arid/semi-arid areas and supports scale-adapted ecological protection strategies. 展开更多
关键词 ecological environment quality multi-scales remote sensing ecological index spatial heterogeneity semi-variance function
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AMVT-NMN:Adaptive Multi-Scale Vision Transformer with Neuromorphic Memory Networks for Enhanced Lung Cancer Detection 认领 引用
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作者 Wariyo Godana Arero Yaqin Zhao +6 位作者 Mudasir Ahmad Wani Pir Noman Ahmad Kashish Ara Shakil Sadique Ahmad Sidrak Habtemariam Teredda Merhawit Berhane Teklu Longwen Wu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期1236-1262,共27页
Lung cancer accounts for the highest number of cancer deaths globally,underscoring the urgent need for early and precise detection to enhance patient outcomes.While deep learning has made remarkable strides in analyzi... Lung cancer accounts for the highest number of cancer deaths globally,underscoring the urgent need for early and precise detection to enhance patient outcomes.While deep learning has made remarkable strides in analyzing medical images,current approaches face a fundamental challenge.They cannot adequately capture detailed local patterns and broader contextual relationships within lung Computed tomography(CT)scans.To address this limitation,we introduce AMVT-NMN(adaptive multi-scale vision transformer with neuromorphic memory networks),which combines three complementary mechanisms.The dynamic adaptive kernel networks component intelligently adjusts receptive field sizes based on input characteristics,enabling flexible feature capture across multiple scales.The neuromorphic contextual memory attention module draws inspiration from how human memory systems process information,maintaining a dynamic record of diagnostically relevant patterns to inform current predictions.The hierarchical cross-scale fusion mechanism with learnable weights synthesizes information from different resolution levels through adaptive weighting.Testing on the Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases(IQOTHNCCD)dataset demonstrates strong performance:97.9%accuracy,96.5%sensitivity,98.7%specificity,and 99.2%Area under the Curve-Receiver Operating Characteristic(AUC-ROC).These results surpass existing methods such as CNN-GD,which achieved 97.2%accuracy.Notably,the high specificity translates to fewer false alarms,potentially reducing unnecessary biopsies and follow-up imaging outcomes that matter considerably in clinical practice.Result of AMVT-NMN generalization to the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI),Lung nodule analysis(LUNA16),and Non-Small Cell Lung Cancer(NSCLC)-Radiomics datasets showed AUCs of 96.5%,92.8%,and 97.2%,respectively.Ablation experiments confirm that each architectural element of AMVT-NMN contributes meaningfully to overall performance.Five-fold cross-validation yielded consistent results(97.71±0.57%),indicating reliable performance across different patient subsets.The memory-augmented design shows particular promise for handling diagnostically ambiguous cases.It is focused on pattern recognition and computational intelligence,which is useful for coping with uncertain information in intelligent diagnosis systems,meeting the growing trend for trusted artificial intelligence(AI)in decision-making. 展开更多
关键词 Adaptive multi-scale vision transformer dynamic adaptive kernel networks hierarchical cross-scale fusion lung cancer detection neuromorphic memory networks
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MSD-YOLO: A Multi-Scale and Detail-Enhancement Network for Traffic Sign Detection 认领 引用
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作者 Mingfang Li Damin Zhang +3 位作者 Qing He Chenglong Zhou Mingrong Li Xiaobo Zhou 《Computers, Materials & Continua》 SCIE EI 2026年第6期2248-2271,共24页
Traffic sign detection is a critical task in autonomous driving environmental perception.However,models often suffer from degraded detection performance in complex real-world scenarios due to variable target scales,bl... Traffic sign detection is a critical task in autonomous driving environmental perception.However,models often suffer from degraded detection performance in complex real-world scenarios due to variable target scales,blurred fine-grained features,and complex background interference.This paper proposes an improved YOLOv8n detection model,MSD-YOLO,to address these challenges.First,a Multi-scale Detail Enhancement Module(MDEM)is designed,which achieves targeted enhancement of edge features through high-frequency residual modulation and multi-scale cooperative attention.Second,an enhanced feature pyramid network termed SG-FPN is constructed.It introduces soft nearest neighbor interpolation(SNI)for semantic-spatial aligned feature fusion and employs enhanced lightweight convolution(GSConvE)to improve feature representation.Additionally,the Wise-ShapeIoU optimization loss function is adopted,integrating shape-aware geometric constraints and a dynamic sample weighting strategy to enhance the localization accuracy for traffic signs of different scales and shapes.Experiments on the TT100K dataset show that our method effectively improves detection performance,with mAP@0.5 and mAP@0.5:0.95 increasing by 3.1%and 2.7%,respectively,compared to the baseline YOLOv8n.Moreover,cross-dataset evaluations on CCTSDB and GTSDB show that the model exhibits good generalization capability and robustness.The experimental results indicate that the proposed method can enhance detection accuracy while maintaining efficient real-time inference,offering an effective solution for traffic sign detection in complex scenarios. 展开更多
关键词 Traffic sign detection YOLOv8n multi-scale feature fusion loss function
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Multi-Scale Vision Transformer with Dynamic Multi-Loss Function for Medical Image Retrieval and Classification 认领 引用
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作者 Omar Alqahtani Mohamed Ghouse +2 位作者 Asfia Sabahath Omer Bin Hussain Arshiya Begum 《Computers, Materials & Continua》 SCIE EI 2025年第5期2221-2244,共24页
This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi... This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features,while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance.Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets,yielding notable improvements.Specifically,on the ISIC-2018 dataset,our method achieves an F1-Score improvement of+4.84% compared to the standard ViT,with a precision increase of+5.46% for melanoma(MEL).On the ChestX-ray14 dataset,the method delivers an F1-Score improvement of 5.3%over the conventional ViT,with precision gains of+5.0% for pneumonia(PNEU)and+5.4%for fibrosis(FIB).Experimental results demonstrate that our approach outperforms traditional CNN-based models and existing ViT variants,particularly in retrieving relevant medical cases and enhancing diagnostic accuracy.These findings highlight the potential of the proposedmethod for large-scalemedical image analysis,offering improved tools for clinical decision-making through superior classification and case comparison. 展开更多
关键词 Medical image retrieval vision transformer multi-scale encoding multi-loss function ISIC-2018 ChestX-ray14
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Using mixed kernel support vector machine to improve the predictive accuracy of genome selection 认领 引用 被引量：2
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作者 Jinbu Wang Wencheng Zong +6 位作者 Liangyu Shi Mianyan Li Jia Li Deming Ren Fuping Zhao Lixian Wang Ligang Wang 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第2期775-787,共13页
The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects acc... The advantages of genome selection(GS) in animal and plant breeding are self-evident.Traditional parametric models have disadvantage in better fit the increasingly large sequencing data and capture complex effects accurately.Machine learning models have demonstrated remarkable potential in addressing these challenges.In this study,we introduced the concept of mixed kernel functions to explore the performance of support vector machine regression(SVR) in GS.Six single kernel functions(SVR_L,SVR_C,SVR_G,SVR_P,SVR_S,SVR_L) and four mixed kernel functions(SVR_GS,SVR_GP,SVR_LS,SVR_LP) were used to predict genome breeding values.The prediction accuracy,mean squared error(MSE) and mean absolute error(MAE) were used as evaluation indicators to compare with two traditional parametric models(GBLUP,BayesB) and two popular machine learning models(RF,KcRR).The results indicate that in most cases,the performance of the mixed kernel function model significantly outperforms that of GBLUP,BayesB and single kernel function.For instance,for T1 in the pig dataset,the predictive accuracy of SVR_GS is improved by 10% compared to GBLUP,and by approximately 4.4 and 18.6% compared to SVR_G and SVR_S respectively.For E1 in the wheat dataset,SVR_GS achieves 13.3% higher prediction accuracy than GBLUP.Among single kernel functions,the Laplacian and Gaussian kernel functions yield similar results,with the Gaussian kernel function performing better.The mixed kernel function notably reduces the MSE and MAE when compared to all single kernel functions.Furthermore,regarding runtime,SVR_GS and SVR_GP mixed kernel functions run approximately three times faster than GBLUP in the pig dataset,with only a slight increase in runtime compared to the single kernel function model.In summary,the mixed kernel function model of SVR demonstrates speed and accuracy competitiveness,and the model such as SVR_GS has important application potential for GS. 展开更多
关键词 genome selection machine learning support vector machine kernel function mixed kernel function
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Structural characterization and immunological function of a neutral polysaccharide from Areca catechu L.kernels 认领 引用
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作者 Jing Zhang Xuejie Feng +4 位作者 Jiahui Dai Shiping Wang Xiaoning Kang Wenting Dai Jianbang Ji 《Tropical Plants》 CSCD 2025年第1期223-235,共13页
Areca nut kernels are the byproduct of areca nut processing.Some of the major bioactive components of the areca nut are polysaccharides.Polysaccharides are known to exert numerous biological activities.Thus,polysaccha... Areca nut kernels are the byproduct of areca nut processing.Some of the major bioactive components of the areca nut are polysaccharides.Polysaccharides are known to exert numerous biological activities.Thus,polysaccharides isolated from areca nut kernels may be clinically valuable.To determine the medicinal value of areca nut kernel polysaccharides(AKP),ultrasonic-assisted extraction,ethanol precipitation,and dialysis were used for separation and purification.SEM,XRD,FTIR,and NMR were used for structural characterization and RAW264.7 macrophages were used for in vitro immune activity assessment.The AKP were isolated was a neutral polysaccharide,and the molecular weight(Mw)was 6.95×105Da,primarily composed of mannose(58.7%)and galactose(33.6%);the main chain of AKP is→4)-β-D-Manp-(1→,and the branched chain was mainly ofα-D-Galp-(1→attached to the O-6 position of→4,6)-β-D-Manp-(1→.Cell experiments show that AKP(20μg/mL)exerts its immune function via the TLR4/NF-κB signaling pathway regulation,decreasing TLR4,P65,IκB-α,and MyD88,and reducing the release of TNF-α,NO,IL-6,and IL-1β.In addition,AKP immune activity might be ascribed to its high Mw and rich monosaccharide composition.This study offers a solid scientific foundation for the development of AKP for wide clinical application. 展开更多
关键词 structural char Immunological function Neutral polysaccharide Structural characterization areca nut kernels separation purificationsemxrdftirand areca nut kernel polysaccharides akp ultrasonic assisted Areca catechu kernels
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Multi-scale analysis of the spatial structure of China’s major function zoning 认领 引用 被引量：15
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作者 WANG Yafei FAN Jie 《Journal of Geographical Sciences》 SCIE CSCD 2020年第2期197-211,共15页
The spatial structures of China’s Major Function Zoning are important constraining indicators in all types of spatial planning and key parameters for accurately downscaling major functions.Taking the proportion of ur... The spatial structures of China’s Major Function Zoning are important constraining indicators in all types of spatial planning and key parameters for accurately downscaling major functions.Taking the proportion of urbanization zones,agricultural development zones and ecological security zones as the basic parameter,this paper explores the spatial structures of major function zoning at different scales using spatial statistics,spatial modeling and landscape metrics methods.The results show:First,major function zones have spatial gradient structures,which are prominently represented by latitudinal and longitudinal gradients,a coastal distance gradient,and an eastern-central-western gradient.Second,the pole-axis system structure and core-periphery structure exist at provincial scales.The general principle of the pole-axis structure is that as one moves along the distance axis,the proportion of urbanization zones decreases and the proportion of ecological security zones increases.This also means that the proportion of different function zones has a ring-shaped spatial differentiation principle with distance from the core.Third,there is a spatial mosaic structure at the city and county scale.This spatial mosaic structure has features of both spatial heterogeneity,such as agglomeration and dispersion,as well as of mutual,adjacent topological correlation and spatial proximity.The results of this study contribute to scientific knowledge on major function zones and the principles of spatial organization,and it acts as an important reference for China’s integrated geographical zoning. 展开更多
关键词 China major function zoning multi-scale spatial gradient pole-axis core-periphery spatial mosaic
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Numerical simulation of the fluid and flexible rods interaction using a semi-resolved coupling model promoted by anisotropic Gaussian kernel function 认领 引用
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作者 Caiping Jin Jingxin Zhang Yonglin Sun 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2025年第1期5-8,共4页
The numerical simulation of the fluid flow and the flexible rod(s)interaction is more complicated and has lower efficiency due to the high computational cost.In this paper,a semi-resolved model coupling the computatio... The numerical simulation of the fluid flow and the flexible rod(s)interaction is more complicated and has lower efficiency due to the high computational cost.In this paper,a semi-resolved model coupling the computational fluid dynamics and the flexible rod dynamics is proposed using a two-way domain expansion method.The gov-erning equations of the flexible rod dynamics are discretized and solved by the finite element method,and the fluid flow is simulated by the finite volume method.The interaction between fluids and solid rods is modeled by introducing body force terms into the momentum equations.Referred to the traditional semi-resolved numerical model,an anisotropic Gaussian kernel function method is proposed to specify the interactive forces between flu-ids and solid bodies for non-circle rod cross-sections.A benchmark of the flow passing around a single flexible plate with a rectangular cross-section is used to validate the algorithm.Focused on the engineering applications,a test case of a finite patch of cylinders is implemented to validate the accuracy and efficiency of the coupled model. 展开更多
关键词 Semi-resolved coupling model Two-way domain expansion method Anisotropic Gaussian kernel function Flexible rod(s)
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CT-MFENet:Context Transformer and Multi-Scale Feature Extraction Network via Global-Local Features Fusion for Retinal Vessels Segmentation 认领 引用
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作者 SHAO Dangguo YANG Yuanbiao +1 位作者 MA Lei YI Sanli 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第4期668-682,共15页
Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete v... Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net. 展开更多
关键词 retinal vessel segmentation context transformer(CT) multi-scale dense residual hybrid loss function global-local fusion
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SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control 认领 引用 被引量：17
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作者 钟伟民 何国龙 +1 位作者 皮道映 孙优贤 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2005年第3期373-379,共7页
A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identifica... A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection. 展开更多
关键词 nonlinear model predictive control support vector machine nonlinear system identification kernel function
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Development and application of a multi-physics and multi-scale coupling program for lead-cooled fast reactor 认领 引用 被引量：11
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作者 Xiao Luo Chi Wang +4 位作者 Ze-Ren Zou Lian-Kai Cao Shuai Wang Zhao Chen Hong-Li Chen 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2022年第2期40-52,共13页
In this study,a multi-physics and multi-scale coupling program,Fluent/KMC-sub/NDK,was developed based on the user-defined functions(UDF)of Fluent,in which the KMC-sub-code is a sub-channel thermal-hydraulic code and t... In this study,a multi-physics and multi-scale coupling program,Fluent/KMC-sub/NDK,was developed based on the user-defined functions(UDF)of Fluent,in which the KMC-sub-code is a sub-channel thermal-hydraulic code and the NDK code is a neutron diffusion code.The coupling program framework adopts the"master-slave"mode,in which Fluent is the master program while NDK and KMC-sub are coupled internally and compiled into the dynamic link library(DLL)as slave codes.The domain decomposition method was adopted,in which the reactor core was simulated by NDK and KMC-sub,while the rest of the primary loop was simulated using Fluent.A simulation of the reactor shutdown process of M2LFR-1000 was carried out using the coupling program,and the code-to-code verification was performed with ATHLET,demonstrating a good agreement,with absolute deviation was smaller than 0.2%.The results show an obvious thermal stratification phenomenon during the shutdown process,which occurs 10 s after shutdown,and the change in thermal stratification phenomena is also captured by the coupling program.At the same time,the change in the neutron flux density distribution of the reactor was also obtained. 展开更多
关键词 Multi-physics and multi-scale coupling method User-defined functions Dynamic link library Thermal stratification Lead-cooled fast reactor
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Landslide prediction based on improved principal component analysis and mixed kernel function least squares support vector regression model 认领 引用 被引量：8
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作者 LI Li-min CHENG Shao-kang WEN Zong-zhou 《Journal of Mountain Science》 SCIE CSCD 2021年第8期2130-2142,共13页
Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive measures.Many factors can influence the occurrence of landslides,which is easy to have a... Landslide probability prediction plays an important role in understanding landslide information in advance and taking preventive measures.Many factors can influence the occurrence of landslides,which is easy to have a curse of dimensionality and thus lead to reduce prediction accuracy.Then the generalization ability of the model will also decline sharply when there are only small samples.To reduce the dimension of calculation and balance the model’s generalization and learning ability,this study proposed a landslide prediction method based on improved principal component analysis(PCA)and mixed kernel function least squares support vector regression(LSSVR)model.First,the traditional PCA was introduced with the idea of linear discrimination,and the dimensions of initial influencing factors were reduced from 8 to 3.The improved PCA can not only weight variables but also extract the original feature.Furthermore,combined with global and local kernel function,the mixed kernel function LSSVR model was framed to improve the generalization ability.Whale optimization algorithm(WOA)was used to optimize the parameters.Moreover,Root Mean Square Error(RMSE),the sum of squared errors(SSE),Mean Absolute Error(MAE),Mean Absolute Precentage Error(MAPE),and reliability were employed to verify the performance of the model.Compared with radial basis function(RBF)LSSVR model,Elman neural network model,and fuzzy decision model,the proposed method has a smaller deviation.Finally,the landslide warning level obtained from the landslide probability can also provide references for relevant decision-making departments in emergency response. 展开更多
关键词 Landslide probability LSSVR Mixed kernel function Improved PCA Warning level
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Multilinear commutators of BMO functions and multilinear singular integrals with non-smooth kernels 认领 引用 被引量：8
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作者 LIAN Jia-li LI Ji WU Huo-xiong 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2011年第1期109-120,共12页
This paper is concerned with certain multilinear commutators of BMO functions and multilinear singular integral operators with non-smooth kernels. By the sharp maximal functions estimates, the weighted norm inequaliti... This paper is concerned with certain multilinear commutators of BMO functions and multilinear singular integral operators with non-smooth kernels. By the sharp maximal functions estimates, the weighted norm inequalities for this kind of commutators are established. 展开更多
关键词 Multilineav operator commutator non-smooth kernel sharp maximal function weighted norminequality.
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A multi-scale convolutional auto-encoder and its application in fault diagnosis of rolling bearings 认领 引用 被引量：12
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作者 Ding Yunhao Jia Minping 《Journal of Southeast University(English Edition)》 EI CAS 2019年第4期417-423,共7页
Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on ... Aiming at the difficulty of fault identification caused by manual extraction of fault features of rotating machinery,a one-dimensional multi-scale convolutional auto-encoder fault diagnosis model is proposed,based on the standard convolutional auto-encoder.In this model,the parallel convolutional and deconvolutional kernels of different scales are used to extract the features from the input signal and reconstruct the input signal;then the feature map extracted by multi-scale convolutional kernels is used as the input of the classifier;and finally the parameters of the whole model are fine-tuned using labeled data.Experiments on one set of simulation fault data and two sets of rolling bearing fault data are conducted to validate the proposed method.The results show that the model can achieve 99.75%,99.3%and 100%diagnostic accuracy,respectively.In addition,the diagnostic accuracy and reconstruction error of the one-dimensional multi-scale convolutional auto-encoder are compared with traditional machine learning,convolutional neural networks and a traditional convolutional auto-encoder.The final results show that the proposed model has a better recognition effect for rolling bearing fault data. 展开更多
关键词 fault diagnosis deep learning convolutional auto-encoder multi-scale convolutional kernel feature extraction
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Commutators of Weighted Lipschitz Functions and Multilinear Singular Integrals with Non-Smooth Kernels 认领 引用 被引量：3
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作者 LIAN Jia-li MA Bo-lin WU Huo-xiong 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2011年第3期353-367,共15页
This paper is devoted to studying the commutators of the multilinear singular integral operators with the non-smooth kernels and the weighted Lipschitz functions. Some mapping properties for two types of commutators o... This paper is devoted to studying the commutators of the multilinear singular integral operators with the non-smooth kernels and the weighted Lipschitz functions. Some mapping properties for two types of commutators on the weighted Lebesgue spaces, which extend and generalize some previous results, are obtained. 展开更多
关键词 Commutator multilinear singular integral non-smooth kernel weighted Lipschitz function weights.
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An algorithm for moving target detection in IR image based on grayscale distribution and kernel function 认领 引用 被引量：7
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作者 王鲁平 张路平 +1 位作者 赵明 李飚 《Journal of Central South University》 SCIE EI CAS 2014年第11期4270-4278,共9页
A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to de... A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection(MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel(GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms. 展开更多
关键词 moving target detection gray-weighted kernel function dynamic background
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A Full-Newton Step Feasible IPM for Semidefinite Optimization Based on a Kernel Function with Linear Growth Term 认领 引用 被引量：2
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作者 GENG Jie ZHANG Mingwang PANG Jinjuan 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2020年第6期501-509,共9页
In this paper,we propose and analyze a full-Newton step feasible interior-point algorithm for semidefinite optimization based on a kernel function with linear growth term.The kernel function is used both for determini... In this paper,we propose and analyze a full-Newton step feasible interior-point algorithm for semidefinite optimization based on a kernel function with linear growth term.The kernel function is used both for determining the search directions and for measuring the distance between the given iterate and theμ-center for the algorithm.By developing a new norm-based proximity measure and some technical results,we derive the iteration bound that coincides with the currently best known iteration bound for the algorithm with small-update method.In our knowledge,this result is the first instance of full-Newton step feasible interior-point method for SDO which involving the kernel function. 展开更多
关键词 semidefinite optimization interior-point algorithm kernel function iteration complexity
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A KERNEL-TYPE ESTIMATOR OF A QUANTILE FUNCTION UNDER RANDOMLY TRUNCATED DATA 认领 引用 被引量：2
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作者 周勇 吴国富 李道纪 《Acta Mathematica Scientia》 SCIE 2006年第4期585-594,共10页
A kernel-type estimator of the quantile function Q（p） = inf{t：F（t） ≥ p}, 0 ≤ p ≤ 1, is proposed based on the kernel smoother when the data are subjected to random truncation. The Bahadur-type representations o... A kernel-type estimator of the quantile function Q（p） = inf{t：F（t） ≥ p}, 0 ≤ p ≤ 1, is proposed based on the kernel smoother when the data are subjected to random truncation. The Bahadur-type representations of the kernel smooth estimator are established, and from Bahadur representations the authors can show that this estimator is strongly consistent, asymptotically normal, and weakly convergent. 展开更多
关键词 Truncated data Product-limits quantile function kernel estimator Bahadur representation
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A Full-Newton Step Feasible Interior-Point Algorithm for the Special Weighted Linear Complementarity Problems Based on a Kernel Function 认领 引用 被引量：2
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作者 GENG Jie ZHANG Mingwang ZHU Dechun 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2024年第1期29-37,共9页
In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear ... In this paper,a new full-Newton step primal-dual interior-point algorithm for solving the special weighted linear complementarity problem is designed and analyzed.The algorithm employs a kernel function with a linear growth term to derive the search direction,and by introducing new technical results and selecting suitable parameters,we prove that the iteration bound of the algorithm is as good as best-known polynomial complexity of interior-point methods.Furthermore,numerical results illustrate the efficiency of the proposed method. 展开更多
关键词 interior-point algorithm weighted linear complementarity problem full-Newton step kernel function iteration complexity
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Multi-scale spatial relationships between soil total nitrogen and influencing factors in a basin landscape based on multivariate empirical mode decomposition 认领 引用 被引量：1
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作者 ZHU Hongfen CAO Yi +3 位作者 JING Yaodong LIU Geng BI Rutian YANG Wude 《Journal of Arid Land》 SCIE CSCD 2019年第3期385-399,共15页
The relationships between soil total nitrogen(STN)and influencing factors are scale-dependent.The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factor... The relationships between soil total nitrogen(STN)and influencing factors are scale-dependent.The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factors(elevation,slope and topographic wetness index),intrinsic soil factors(soil bulk density,sand content,silt content,and clay content)and combined environmental factors(including the first two principal components(PC1 and PC2)of the Vis-NIR soil spectra)along three sampling transects located at the upstream,midstream and downstream of Taiyuan Basin on the Chinese Loess Plateau.We separated the multivariate data series of STN and influencing factors at each transect into six intrinsic mode functions(IMFs)and one residue by multivariate empirical mode decomposition(MEMD).Meanwhile,we obtained the predicted equations of STN based on MEMD by stepwise multiple linear regression(SMLR).The results indicated that the dominant scales of explained variance in STN were at scale 995 m for transect 1,at scales 956 and 8852 m for transect 2,and at scales 972,5716 and 12,317 m for transect 3.Multi-scale correlation coefficients between STN and influencing factors were less significant in transect 3 than in transects 1 and 2.The goodness of fit root mean square error(RMSE),normalized root mean square error(NRMSE),and coefficient of determination(R2)indicated that the prediction of STN at the sampling scale by summing all of the predicted IMFs and residue was more accurate than that by SMLR directly.Therefore,the multi-scale method of MEMD has a good potential in characterizing the multi-scale spatial relationships between STN and influencing factors at the basin landscape scale. 展开更多
关键词 intrinsic mode function multivariate empirical mode decomposition multi-scale spatial relationship sampling transect soil total nitrogen Chinese Loess Plateau
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