Image processing and restoration are important in computer vision, particularly for images that are damaged by noise, blur, and other issues. Traditional methods often have a hard time with problems like periodic nois...
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Image processing and restoration are important in computer vision, particularly for images that are damaged by noise, blur, and other issues. Traditional methods often have a hard time with problems like periodic noise and do not effectively combine local and global data during the restoration process. To address these problems, we suggest an enhanced image restoration model that merges Lewin architecture with swinIR, using advanced deep learning methods. This approach combines these techniques for a better restoration process improved by 4.2%. The model's effectiveness is checked using PsNR and ssIM measurements, showing that it can lower noise while keeping key image details intact. When compared to traditional methods, our model shows better results, creating a new standard in image restoration for difficult situations. Test resultsshow that this combined approach greatly enhances fixing performance across various image datasets, making it a strong solution for clearer images and noise reduction.
A physically feasible,reliable,and safe motion is essential for robot operation.A parameterization-based trajectory planning approach is proposed for an 8-DOF manipulator with multiple *** inverse kinematic solution i...
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A physically feasible,reliable,and safe motion is essential for robot operation.A parameterization-based trajectory planning approach is proposed for an 8-DOF manipulator with multiple *** inverse kinematic solution is obtained through an analytical method,and the trajectory is planned in joint *** such,the trajectory planning of the 8-DOF manipulator is transformed into a parameterization-based trajectory optimization problem within its physical,obstacle and task constraints,and the optimization variables are significantly *** teaching-learning-based optimization(TLBO)algorithm is employed to search for the redundant parameters to generate an optimal *** and physical experiment results demonstrate that this approach can effectively solve the trajectory planning problem of the ***,the planned trajectory has no theoretical end-effector deviation for the task *** approach can provide a reference for the motion planning of other redundant manipulators.
aaaa The adaptive learning community seeks to provide solutions to customize and enhance students’ learning experiences when accessing web-basedlearningsystems. The adaptation usually occurs from the use of learnin...
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Accurate operational methods used to measure, verify, and report changes in biomass at large spatial scales are required to support conservation initiatives. In thisstudy, we demonstrate that machine learning can be ...
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Accurate operational methods used to measure, verify, and report changes in biomass at large spatial scales are required to support conservation initiatives. In thisstudy, we demonstrate that machine learning can be used to model aboveground biomass (AGB) in both tropical and temperate forest ecosystems when provided with a sufficiently large training dataset. Using wavelet-transformed airborne hyperspectral imagery, we trained a shallow neural network (sNN) to model AGB. An existing global AGB map developed as part of the European space Agency's DUE GlobBiomassprojectserved as the training data for all study sites. At the temperate site, we also trained the model on airborne-LiDAR-derived AGB. In comparison, for all study sites, we also trained a separate deep convolutional neural network (3D-CNN) with the hyperspectral imagery. Our resultsshow that extracting both spatial and spectral features with the 3D-CNN produced the lowest RMsE across all study sites. For example, at the tropical forest site the Tortuguero conservation area, with the 3D-CNN, an RMsE of 21.12 Mg/ha (R-2 of 0.94) was reached in comparison to the sNN model, which had an RMsE of 43.47 Mg/ha (R-2 0.72), accounting for a similar to 50% reduction in prediction uncertainty. The 3D-CNN models developed for the other tropical and temperate sites produced similar results, with a range in RMsE of 13.5 Mg/ha-31.18 Mg/ha. In the future, assufficiently large field-based datasets become available (e.g., the national forest inventory), a 3D-CNN approach could help to reduce the uncertainty between hyperspectral reflectance and forest biomass estimates across tropical and temperate bioclimatic domains.
The growing prevalence of uncertainty in global events posessignificant challenges to economic cycle forecasting, emphasizing the need for more robust predictive models. Thisstudy addresses this gap by developing a ...
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The growing prevalence of uncertainty in global events posessignificant challenges to economic cycle forecasting, emphasizing the need for more robust predictive models. Thisstudy addresses this gap by developing a novel forecasting framework that integrates multiple uncertainty indices to improve accuracy, stability, and interpretability, particularly during uncertainty shocks. To achieve this, several methodological innovations were implemented. First, newssentiment-based uncertainty indices were incorporated as candidate variables to capture uncertainty dynamics. second, Bayesian least absolute shrinkage and selection operator (Bayesian LAssO) was employed for efficient variable selection, mitigating the curse of dimensionality in small samples. Third, the multi-objective Lichtenberg algorithm (MOLA) was applied to optimize the prediction window size, ensuring model robustness. Additionally, a MOLA-based extreme gradient boosting (MOLA-XGBoost) model was developed to fine-tune hyperparameters across dimensions of prediction accuracy, stability, and directional consistency. Finally, sHapley Additive exPlanations (sHAP) theory was used to enhance model interpretability. Thisstudy forecasts China's economic cycle using multiple indicators, demonstrating that the proposed approach consistently delivers accurate and robust predictions even under uncertainty shocks. The findings highlight the crucial role of uncertainty indices in improving economic forecasts, offering new insights and methodologies for predictive modeling in volatile environments.
Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This rese...
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Large language models have garnered significant attention and are widely utilized across different fields due to their impressive performance. However, centralized training of these models can pose privacy risks like ...
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computer-generated holography (CGH) has made significant advancements and is considered a leading approach for near-eye 3D displays. Recent learning-based CGH methods address the time-quality trade-off of traditional ...
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作者:
Hu, RuijieFaculty of Science and Technology
Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNU-HKBU United International College Zhuhai China
With the rapid development of financial markets, stock price prediction, as a highly complex and challenging topic, has attracted more and more attention from academia and industry. Thisstudy aims to use multiple mac...
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In recent years, deep learning has revolutionized fieldssuch ascomputer vision, speech recognition, and natural language processing, primarily through techniques applied to data in Euclidean spaces. However, many re...
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In recent years, deep learning has revolutionized fieldssuch ascomputer vision, speech recognition, and natural language processing, primarily through techniques applied to data in Euclidean spaces. However, many real world applications involve data from non-Euclidean domains, where graphs naturally represent entities and their complex interdependencies. Traditional machine learning methods have often struggled to processsuch data in an effective manner. Graph Neural Networks represent a crucial advance in the use of deep learning to interpret and extract knowledge from graph-based data. They have opened up new possibilities for taskssuch as node categorization, link inference, and comprehensive graph analysis. This paper provides a detailed analysis of Graph Neural Network (GNN) methodologies, emphasizing their architectural diversity and wide ranging applications. GNN models are systematically categorized into fundamental frameworkssuch as message passing paradigms, spectral and spatial methods, and advanced extensionssuch as hypergraph neural networks and multigraph approaches. This paper also explores domainssuch associal network analysis, molecular biology, traffic forecasting, and recommendation systems. In addition, it emphasizessome critical open challenges, including scalability, dynamic graph modeling, and robustness against noisy or incomplete data. The paper concludes with a proposal for future research directions to improve the scalability, interpretability, and adaptability of GNNs in this fast-evolving field.
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