With the advent of the Web 3.0 era, the amount and types of data in the network have sharply increased, and the application scenarios of recommendation algorithms are continuously expanding. Location recommendation ha...
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Learning the accurate dynamics of robotic systems directly from the trajectory data is currently a prominent research *** physics-enforced networks,exemplified by Hamiltonian neural networks and Lagrangian neural netw...
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Learning the accurate dynamics of robotic systems directly from the trajectory data is currently a prominent research *** physics-enforced networks,exemplified by Hamiltonian neural networks and Lagrangian neural networks,demonstrate proficiency in modeling ideal physical systems,but face limitations when applied to systems with uncertain non-conservative dynamics due to the inherent constraints of the conservation laws *** this paper,we present a novel augmented deep Lagrangian network,which seamlessly integrates a deep Lagrangian network with a standard deep *** fusion aims to effectively model uncertainties that surpass the limitations of conventional Lagrangian *** proposed network is applied to learn inverse dynamics model of two multi-degree manipulators including a 6-dof UR-5 robot and a 7-dof SARCOS manipulator under *** experimental results clearly demonstrate that our approach exhibits superior modeling precision and enhanced physical credibility.
Current state-of-the-art QoS prediction methods face two main limitations. Firstly, most existing QoS prediction approaches are centralized, gathering all user-service invocation QoS records for training and optimizat...
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Surface defects can affect the quality of steel *** methods based on computer vision are currently applied to surface defect detection of steel ***,their real-time performance and object detection of small defect are ...
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Surface defects can affect the quality of steel *** methods based on computer vision are currently applied to surface defect detection of steel ***,their real-time performance and object detection of small defect are still *** improved object detection network based on You Only Look One-level Feature(YOLOF)is proposed to show excellent performance in surface defect detection of steel plate,called ***,the anchor-free detector is used to reduce the network ***,deformable convolution network and local spatial attention module are introduced into the feature extraction network to increase the contextual information in the feature ***,the soft non-maximum suppression is used to improve detection accuracy ***,data augmentation is performed for small defect objects during training to improve detection *** show the average precision and average precision for small objects are 42.7%and 33.5%at a detection speed of 62 frames per second on a single GPU,*** shows that DLF-YOLOF has excellent performance to meet the needs of industrial real-time detection.
The phenomenal rise in network traffic across various sectors, driven by advancements in network communication, has led to an explosion of connected devices. While internet-based service providers have enhanced smart ...
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Image classification is crucial for various applications,including digital construction,smart manu-facturing,and medical *** on the inadequate model generalization and data privacy concerns in few-shot image classific...
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Image classification is crucial for various applications,including digital construction,smart manu-facturing,and medical *** on the inadequate model generalization and data privacy concerns in few-shot image classification,in this paper,we propose a federated learning approach that incorporates privacy-preserving ***,we utilize contrastive learning to train on local few-shot image data and apply various data augmentation methods to expand the sample size,thereby enhancing the model’s generalization capabilities in few-shot ***,we introduce local differential privacy techniques and weight pruning methods to safeguard model parameters,perturbing the transmitted parameters to ensure user data ***,numerical simulations are conducted to demonstrate the effectiveness of our proposed *** results indicate that our approach significantly enhances model generalization and test accuracy compared to several popular federated learning algorithms while maintaining data privacy,highlighting its effectiveness and practicality in addressing the challenges of model generalization and data privacy in few-shot image scenarios.
Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality ***,in prac...
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Most of the search-based software remodularization(SBSR)approaches designed to address the software remodularization problem(SRP)areutilizing only structural information-based coupling and cohesion quality ***,in practice apart from these quality criteria,there require other aspects of coupling and cohesion quality criteria such as lexical and changed-history in designing the modules of the software ***,consideration of limited aspects of software information in the SBSR may generate a sub-optimal modularization ***,such modularization can be good from the quality metrics perspective but may not be acceptable to the *** produce a remodularization solution acceptable from both quality metrics and developers’perspectives,this paper exploited more dimensions of software information to define the quality criteria as modularization ***,these objectives are simultaneously optimized using a tailored manyobjective artificial bee colony(MaABC)to produce a remodularization *** assess the effectiveness of the proposed approach,we applied it over five software *** obtained remodularization solutions are evaluated with the software quality metrics and developers view of *** demonstrate that the proposed software remodularization is an effective approach for generating good quality modularization solutions.
The detection of hypersonic targets usually confronts range migration(RM)issue before coherent integration(CI).The traditional methods aiming at correcting RM to obtain CI mainly considers the narrow-band radar ***,wi...
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The detection of hypersonic targets usually confronts range migration(RM)issue before coherent integration(CI).The traditional methods aiming at correcting RM to obtain CI mainly considers the narrow-band radar ***,with the increasing requirement of far-range detection,the time bandwidth product,which is corresponding to radar’s mean power,should be promoted in actual ***,the echo signal generates the scale effect(SE)at large time bandwidth product situation,influencing the intra and inter pulse integration *** eliminate SE and correct RM,this paper proposes an effective algorithm,i.e.,scaled location rotation transform(ScLRT).The ScLRT can remove SE to obtain the matching pulse compression(PC)as well as correct RM to complete CI via the location rotation transform,being implemented by seeking the actual rotation *** to the traditional coherent detection algorithms,Sc LRT can address the SE problem to achieve better detection/estimation *** last,this paper gives several simulations to assess the viability of ScLRT.
Non-intrusive load monitoring(NILM)technology aims to infer the operation information of electrical appliances from the total household load signals,which is of great significance for energy conservation and ***,exist...
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Non-intrusive load monitoring(NILM)technology aims to infer the operation information of electrical appliances from the total household load signals,which is of great significance for energy conservation and ***,existing methods are difficult to effectively capture the complex nonlinear features of the power consumption flow,which affects the energy disaggregation *** this end,this paper designs a method based on temporal convolutional network(TCN),efficient channel attention(ECA),and long short-term memory(LSTM).The method first creatively proposes a two-stage improved TCN(TSTCN),which overcomes its problems of extracting discontinuous information and poor correlation of long-distance information while enhancing the ability to extract high-level load *** a novel improved ECA attention mechanism(IECA)is embedded,which is also combined with the skip connection technique to pay channel-weighted attention to important feature maps and promote information ***,the LSTM with strong temporal memory capability is introduced to learn the dependencies in the load power sequence and realize load *** on two real-world datasets,REDD and UK-DALE,show that the proposed model significantly outperforms other comparative NILM algorithms and achieves satisfactory tracking with the actual appliance operating *** results show that the mean absolute error(MAE)of all appliances decreases by 18.67%on average,and the F1 score improves by 38.70%.
The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing s...
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The multitude of airborne point clouds limits the point cloud processing *** are grouped based on similar points,which can effectively alleviate the demand for computing resources and improve processing ***,existing superpoint segmentation methods focus only on local geometric structures,resulting in inconsistent spectral features of points within a *** feature inconsistencies degrade the performance of subsequent ***,this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral information for multispectral point cloud superpoint segmentation(GSI-SS).Specifically,a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented *** the formation of the primary superpoints,an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is *** are conducted on two real multispectral point cloud datasets,and the proposed method achieved higher recall,precision,F score,and lower global consistency and feature classification *** experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.
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