As the penetration ratio of wind power in active distribution networks continues to increase,the system exhibits some characteristics such as randomness and *** and accurate short-term wind power prediction is essenti...
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As the penetration ratio of wind power in active distribution networks continues to increase,the system exhibits some characteristics such as randomness and *** and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization *** on the spatio-temporal features of Numerical Weather Prediction(NWP)data,it proposes the WVMD_DSN(Whale Optimization Algorithm,Variational Mode Decomposition,Dual Stream Network)*** model first applies Pearson correlation coefficient(PCC)to choose some NWP features with strong correlation to wind power to form the feature ***,it decomposes the feature set using Variational Mode Decomposition(VMD)to eliminate the nonstationarity and obtains Intrinsic Mode Functions(IMFs).Here Whale Optimization Algorithm(WOA)is applied to optimise the key parameters of VMD,namely the number of mode components K and penalty factor ***,incorporating attention mechanism(AM),Squeeze-Excitation Network(SENet),and Bidirectional Gated Recurrent Unit(BiGRU),it constructs the dual-stream network(DSN)for short-term wind power *** experiments demonstrate that the WVMD_DSN model outperforms existing baseline algorithms and exhibits good generalization *** relevant code is available at https://***/ruanyuyuan/***(accessed on 20 August 2024).
Clustering is a common method used in machine learning to group sample points in a dataset. Gaussian Mixture Clustering (GMC) is a clustering method based on maximum likelihood estimation and expectation maximisation ...
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Extreme learning machine (ELM), as a single hidden layer feedforward neural network (SLFN), has attracted extensive attention because of its fast learning speed and high accuracy. However, the random selection of inpu...
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The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based ***-based social networks have become very popular as they provide end users like us with several such ser...
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The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based ***-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our ***,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the *** to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these ***,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after *** have come up with a new trajectory privacy protection solution that contraposes the area of interest for ***,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving ***,our privacy protection mechanism focuses on the user's areas of interest rather than the entire ***,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge *** test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.
Graph neural networks have proven their effectiveness for user-item interaction graph collaborative filtering. However, most of the existing recommendation models highly depended on abundant and high-quality datasets ...
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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.
Dear Editor,This letter proposes a parameter-free multiple kernel clustering(MKC)method by using shifted Laplacian *** MKC can effectively cluster nonlinear data,but it faces two main challenges:1)As an unsupervised m...
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Dear Editor,This letter proposes a parameter-free multiple kernel clustering(MKC)method by using shifted Laplacian *** MKC can effectively cluster nonlinear data,but it faces two main challenges:1)As an unsupervised method,it is up against parameter problems which makes the parameters intractable to tune and is unfeasible in real-life applications;2)Only considers the clustering information,but ignores the interference of noise within Laplacian.
Owing to the computational density and complexity of vehicle applications, unique vehicle mobility and limited edge server resources, Vehicle Edge Computing (VEC) faces significant challenges. Unmanned Aerial Vehicles...
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With the continuous growth of the population, crowd counting plays a crucial role in intelligent monitoring systems for the Internet of Things (IoT) and smart city development. Accurate monitoring of crowd density not...
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Grid automation is widely used in finite element analysis, computational fluid dynamics and other fields. Its core lies in accurately dividing complex geometries into suitable grids for numerical calculations. To solv...
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