The communication maintenance problem of robot swarms is important to multi-robot control in applications like rescue and area exploration. In this paper, we propose a robot-relay-based framework to keep the robot swa...
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The selection of insulating fluids is critical for the reliable and efficient operation of power and distribution transformers. This paper provides a comprehensive review of various optimization techniques employed to...
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Mobile Wireless Sensor Network (MWSN) allows rapid and transitory connections among mobile sensor nodes lacking the help of structure. Due to the rapid growth of MWSN, traffic necessitates a severe and unbalanced load...
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Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning a...
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Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning and thus hinder large-scale deployment. In this paper, we propose BEV-Locator: an end-to-end visual semantic localization neural network using multi-view camera images. Specifically, a visual BEV(bird-eye-view) encoder extracts and flattens the multi-view images into BEV space. While the semantic map features are structurally embedded as map query sequences. Then a cross-model transformer associates the BEV features and semantic map queries. The localization information of ego-car is recursively queried out by cross-attention modules. Finally, the ego pose can be inferred by decoding the transformer outputs. This end-to-end model speaks to its broad applicability across different driving environments, including high-speed scenarios. We evaluate the proposed method in large-scale nuScenes and Qcraft datasets. The experimental results show that the BEV-Locator is capable of estimating the vehicle poses under versatile scenarios, which effectively associates the cross-model information from multi-view images and global semantic maps. The experiments report satisfactory accuracy with mean absolute errors of 0.052 m, 0.135 m and 0.251° in lateral, longitudinal translation and heading angle degree.
With the increasing complexity of distributed systems, achieving an optimal distribution of tasks across resources is paramount for enhancing system performance. Therefore, in this study, a novel multi-objective load ...
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In this paper, semi-supervised subspace clustering based on tensor low-rank representation is proposed to do the clustering task. In our paper, we propose to use the weighted tensor Schatten-p norm to approximate tens...
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An innovative deep learning structure, PulmoNetX, integrates the capabilities of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to enhance pneumonia detection in chest X-ray imagery. During prepro...
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A novel semantics for MARL called neural concurrent game structure (NCGS) is introduced, which extends CGS with neural network and roles where the agents are implemented via feed-forward ReLU neural networks. To forma...
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Aiming at the problem of secure sharing of edge-side privacy data under the industrial Internet architecture, a trusted secret sharing method based on secure multi-party computation is proposed. This method converts t...
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In this paper, multiple sequence alignment (MSA) is modeled as a two-objective minimization problem, the score of the alignment serves as an objective, the penalty of the gaps serves as the other objective. Three scor...
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