This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method...
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This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is *** depth model ensures image feature accuracy and image trajectory smoothness in rotating target *** relative velocities of the UAV and the dynamic target are estimated using the proposed velocity *** to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is *** integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance *** stability of the velocity observer and IBVS controller is analyzed using the Lyapunov *** simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.
Permanent magnet synchronous motors (PMSMs) offer the benefits of high torque density and a superior power factor. Nevertheless, the challenge lies in the inherent difficulty of adjusting the permanent magnet flux. To...
Permanent magnet synchronous motors (PMSMs) offer the benefits of high torque density and a superior power factor. Nevertheless, the challenge lies in the inherent difficulty of adjusting the permanent magnet flux. To address this issue, this paper analyzes the influence of electromagnetic parameters on the constant-power speed range (CPSR). First, the analysis focuses on the influence of d-axis inductance and saliency ratio, aiming to establish a design method for achieving a wide CPSP in PMSMs. Second, based on the electromagnetic parameter requirements, a reverse-salient PMSM is proposed and its working principle is explained. Finally, the electromagnetic characteristics, including torque and the CPSP, are determined through the application of the finite element method (FEM). The obtained results confirm that the reverse-salient PMSM achieves a CPSP of 7:1, thus validating the accuracy of the theoretical analysis.
This paper presents an innovative framework for achieving energy-efficient and sustainable communication in Vehicular Ad Hoc Networks (VANETs) through a novel integration of Grey Wolf Optimization (GWO) and an Adaptiv...
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ISBN:
(数字)9798331508876
ISBN:
(纸本)9798331508883
This paper presents an innovative framework for achieving energy-efficient and sustainable communication in Vehicular Ad Hoc Networks (VANETs) through a novel integration of Grey Wolf Optimization (GWO) and an Adaptive Weighted Clustering Algorithm (AWCA). The proposed framework addresses key challenges in VANETs, including high energy consumption, dynamic topology changes, and scalability, by leveraging metaheuristic-driven clustering techniques. GWO is employed to optimize cluster head selection and parameter weighting in AWCA, ensuring adaptive clustering based on real-time metrics such as residual energy, traffic density, and communication overhead. This integration facilitates green communication principles by minimizing energy consumption through efficient data aggregation, adaptive cluster maintenance, and energy-aware routing strategies. Extensive simulations are conducted across various mobility scenarios, demonstrating that the proposed framework (GWO-AWCA) significantly outperforms most popular approaches (LEACH and DEEC) in terms of energy savings, cluster stability, and PDR.
A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branc...
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ISBN:
(数字)9798331521950
ISBN:
(纸本)9798331521967
A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method.
Model interpretation methods are essential for understanding the predictions of machine learning models. How-ever, when applied to dynamic multivariate time series data, existing approaches encounter significant chall...
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ISBN:
(数字)9798331533113
ISBN:
(纸本)9798331533120
Model interpretation methods are essential for understanding the predictions of machine learning models. How-ever, when applied to dynamic multivariate time series data, existing approaches encounter significant challenges, primarily due to the temporal correlations within the same feature at different time points. Ignoring temporal dependencies of time series data during interpretation usually leads to inadequate explanation results. To address these challenges, we propose a novel algorithm called Dynamic Sliding Window Sampling (DSWS) for explaining time series models. This algorithm dynam-ically generates importance scores for the same feature across various time steps by iteratively removing feature sets from specific time periods. Leveraging a sliding time window with an adaptable length, DSWS effectively captures the temporal dependencies of dynamic features and delineates their influence boundaries. Experiments on both synthetic and real world data sets validate DSWS’ applicability for time series data with dynamic temporal dependencies. Furthermore, it outperforms state-of-the-art methods in computational efficiency.
In large-scale environmental monitoring, massive sensor nodes regularly collect data and transmit them over long distances, which burdens the network and leads to the increased energy consumption. To deal with this pr...
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The science of robotics is becoming more and more relevant for industrial production. Visualization of the production system involves the exact modeling of its components as accurately as possible. For a robotic techn...
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Many state-of-the-art low-light image enhancement techniques now suffer from issues like color distortion, detail blurring, and the halo effect, hindering their ability to produce visual effects. This paper presents a...
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Score-based generative models have emerged as the state-of-the-art in generative modeling. In this paper, we introduce a novel sampling scheme that can be combined with pre-trained score-based diffusion models to spee...
Score-based generative models have emerged as the state-of-the-art in generative modeling. In this paper, we introduce a novel sampling scheme that can be combined with pre-trained score-based diffusion models to speed up sampling by a factor of two to five in terms of the number of function evaluations (NFEs) with a superior Fréchet Inception distance (FID), compared to Annealed Langevin dynamics in noise-conditional score network (NCSN) and improved noise-conditional score network (NCSN++). The proposed sampling algorithm is inspired by momentum-based accelerated gradient descent used in convex optimization techniques. We validate the sampling efficiency of the proposed algorithm in terms of FID on CIFAR-10 and CelebA datasets.
Time series clustering plays a vital role in various fields, including power grids and finance. In recent years, contrastive learning has been widely adopted to improve the performance of clustering algorithms. Howeve...
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ISBN:
(数字)9798331533113
ISBN:
(纸本)9798331533120
Time series clustering plays a vital role in various fields, including power grids and finance. In recent years, contrastive learning has been widely adopted to improve the performance of clustering algorithms. However, these methods were initially designed for image data. When applied to time series, which exhibit unique characteristics such as mean reversion, the labels of generated samples often do not match the labels of the original samples. This misalignment occurs because the nature of time series data is not sufficiently preserved during augmentation. Therefore, ensuring that the inherent properties of time series data are maintained during augmentation has become a critical issue. In this paper, we firstly introduce a novel data augmentation method called HTGAN (Hurst-Time series Generative Adversarial Network), which is integrated into a contrastive learning framework. Moreover, we propose a new clustering framework, TSCCL (Contrastive Learning Framework for Time Series Clustering), specifically designed for financial time series data clustering. Experimental results demonstrate that TSCCL outperforms all baseline methods, highlighting the effectiveness of HTGAN in financial time series data augmentation.
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