The implementation of intelligent driving technology is inseparable from reliable wireless communication and efficient cache scheduling in Vehicle Ad-hoc Networks (VANETs). The purpose of cache scheduling in VANETs is...
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This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mod...
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This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode *** algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low *** offshore wind power generation system model is presented to verify the algorithm *** offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/*** with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational ***,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation *** results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system.
Self-supervised learning is attracting significant attention from researchers in the point cloud processing field. However, due to the natural sparsity and irregularity of point clouds, effectively extracting discrimi...
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Self-supervised learning is attracting significant attention from researchers in the point cloud processing field. However, due to the natural sparsity and irregularity of point clouds, effectively extracting discriminative and transferable features for efficient training on downstream tasks remains an unsolved challenge. Consequently, we propose PointSmile, a reconstruction-free self-supervised learning paradigm by maximizing curriculum mutual information(CMI) across the replicas of point cloud objects. From the perspective of how-and-what-to-learn, PointSmile is designed to imitate human curriculum learning, i.e.,starting with easier topics in a curriculum and gradually progressing to learning more complex topics in the curriculum. To solve “how-to-learn”, we introduce curriculum data augmentation(CDA) of point *** encourages PointSmile to follow a learning path that starts from learning easy data samples and progresses to learning hard data samples, such that the latent space can be dynamically affected to create better embeddings. To solve “what-to-learn”, we propose maximizing both feature-and class-wise CMI to better extract discriminative features of point clouds. Unlike most existing methods, PointSmile does not require a pretext task or cross-modal data to yield rich latent representations; additionally, it can be easily transferred to various backbones. We demonstrate the effectiveness and robustness of PointSmile in downstream tasks such as object classification and segmentation. The study results show that PointSmile outperforms existing self-supervised methods and compares favorably with popular fully supervised methods on various standard architectures. The code is available at https://***/theaalee/PointSmile.
With the development of deep learning and computer vision, face detection has achieved rapid progress owing. Face detection has several application domains, including identity authentication, security protection, medi...
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Evaluating the urban of low-carbon development (LCDU) is crucial for promoting sustainable urban development. Previous evaluation works are weak in exploring the influence of spatial patterns of urban agglomerations o...
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This paper focuses on the finite-time control(FTC) of the composite formation consensus(CFC)problems for multi-robot systems(MRSs). The CFC problems are firstly proposed for MRSs under the complex network topology of ...
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This paper focuses on the finite-time control(FTC) of the composite formation consensus(CFC)problems for multi-robot systems(MRSs). The CFC problems are firstly proposed for MRSs under the complex network topology of cooperative or cooperative-competitive networks. Regarding the problems of FTC and CFC on multiple Lagrange systems(MLSs), coupled sliding variables are introduced to deal with the robustness and consistent convergence. Then, the adaptive finite-time protocols are given based on the displacement approaches. With the premised FTC, tender-tracking methods are further developed for the problems of tracking information disparity. Stability analyses of those MLSs mentioned above are clarified with Lyapunov candidates considering the coupled sliding vectors, which provide new verification for tender-tracking systems. Under the given coupled-sliding-variable-based finite-time protocols, MLSs distributively adjust the local formation error to achieve global CFC and perform uniform convergence in time-varying tracking. Finally, simulation experiments are conducted while providing practical solutions for the theoretical results.
This paper proposes a Markov decision process based service migration algorithm to satisfy quality of service(QoS) requirements when the terminals leave the original server. Services were divided into real-time servic...
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This paper proposes a Markov decision process based service migration algorithm to satisfy quality of service(QoS) requirements when the terminals leave the original server. Services were divided into real-time services and non-real-time services, each type of them has different requirements on transmission bandwidth and latency,which were considered in the revenue function. Different values were assigned to the weight coefficients of QoS parameters for different service types in the revenue and cost functions so as to distinguish the differences between the two service types. The overall revenue was used for migration decisions, rather than fixed threshold or instant *** Markov decision process was used to maximize the overall revenue of the system. Simulation results show that the proposed algorithm obtained more revenue compared with the existing works.
To reduce the acquisition cost of aerodynamic heating data for high-speed aircraft by leveraging diverse data sources, this paper proposes a multi-source data fusion method based on sparse sampling and multi-fidelity ...
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The paper presents a recommender algorithm for visual analysis based on Data field Schema and Aggregation, and developed an automated data analysis solution recommendation system (AutoEDA) in conjunction with the Expl...
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In this paper, we propose hardware acceleration to improve a performance of scripting programming languages for embedded developments. Scripting programming languages enable more efficient software developments and sc...
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