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Air-Ground Collaborative Mobile Edge Computing:Architecture,Challenges,and Opportunities

China Communications 2024年第5期21卷 1-16页

作者： Qin Zhen He Shoushuai Wang Hai Qu Yuben Dai Haipeng Xiong Fei Wei Zhenhua Li Hailong College of Communications Engineering Army Engineering University of PLANanjing 210000China Department of Information and Communication Noncommissioned Officer Academy of PAPHangzhou 310000China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information TechnologyNanjing 211106China College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China CMC Political and Law Commission Beijing100120China Xi’an Research Institute of High Technology Xi’an 710025China

By pushing computation,cache,and network control to the edge,mobile edge computing(MEC)is expected to play a leading role in fifth generation(5G)and future sixth generation(6G).Nevertheless,facing ubiquitous fast-growing computational demands,it is impossible for a single MEC paradigm to effectively support high-quality intelligent services at end user equipments(UEs).To address this issue,we propose an air-ground collaborative MEC(AGCMEC)architecture in this *** proposed AGCMEC integrates all potentially available MEC servers within air and ground in the envisioned 6G,by a variety of collaborative ways to provide computation services at their best for ***,we introduce the AGC-MEC architecture and elaborate three typical use ***,we discuss four main challenges in the AGC-MEC as well as their potential ***,we conduct a case study of collaborative service placement for AGC-MEC to validate the effectiveness of the proposed collaborative service placement ***,we highlight several potential research directions of the AGC-MEC.

关键词： air-ground architecture collaborative mobile edge computing

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Real-time 3D Object Detection based on Dynamic Sparse Voxel Transformer in Mining Area 22

Real-time 3D Object Detection based on Dynamic Sparse Voxel ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Sun, Shengdi Wang, Zhangyu Liu, Runsen Yang, Songyue Wang, Liyun Liu, Wentao School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

To improve the safety and efficiency of mining operation, the development of unmanned transportation technologies for mining trucks is extraordinarily rapid. The application of LiDAR in autonomous mining trucks has also become increasingly popular. However, due to the significant difference in the size of objects and the complex noise, the problem of point cloud object detection in mining scenarios remains a major challenge. Existing methods, such as those based on point cloud clustering algorithms, have proved to be difficult to meet practical application requirements. In this paper, a real-time point cloud object detection based on Dynamic Sparse Voxel Transformer (DSVT) is introduced. A series of experiments were conducted to examine the efficacy of the proposed methodology. The experimental results demonstrated that the proposed method attained a mean average precision of 75%. Additionally, the detection latency of the proposed method was observed to be 62 ms. Collectively, the detection speed and accuracy both align with the practical application requirements. © 2024 IEEE.

关键词： Trucks

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Toward Comprehensive Semantic Prompt for Region Contrastive Learning Underwater Image Enhancement

Toward Comprehensive Semantic Prompt for Region Contrastive ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Xiao Fu, Yongsheng Wang, Wei Liu, Wei School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350368741

Underwater image enhancement (UIE) focuses on mitigating image quality degradation due to light absorption and scattering. However, most existing methods enhance images via a global and uniform manner, neglecting the inherent semantic information in different regions, which may cause the network to easily deviate from the region's original color. Moreover, these methods typically rely on clear images to guide network convergence, a process constrained by the limited availability of real-world datasets, making it extremely challenging to train enhancement models for various degradations. To address these challenges, this paper introduces a semantic guidance and region contrastive constraints network (SRCNet). Initially, we propose a semantic-aware RWKV (Receptance Weighted Key Value) block and a semantic prompt regularization module. These components leverage intra-target semantic correlations to preserve image details and colors within a global perceptual framework, while employing focal loss to emphasize the restoration of severely degraded regions. Subsequently, we introduce a region contrastive learning method that effectively utilizes negative samples to precisely capture features sensitive to degradation factors, thereby fostering robust feature distributions. Finally, experimental results demonstrate that our method outperforms existing state-of-the-art (SOTA) approaches. © 2025 IEEE.

关键词： contrastive learning rwkv semantic prompt Underwater image enhancement

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Physics-based battery SOC estimation methods:Recent advances and future perspectives

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Journal of Energy Chemistry 2024年第2期89卷 27-40,I0003页

作者： Longxing Wu Zhiqiang Lyu Zebo Huang Chao Zhang Changyin Wei College of Mechanical Engineering Anhui Science and Technology UniversityChuzhou 233100AnhuiChina Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Hubei University of Arts and ScienceXiangyang 441053HubeiChina School of Internet Anhui UniversityHefei 230039AnhuiChina School of Mechanical and Electrical Engineering Guilin University of Electronic TechnologyGuilin 541004GuangxiChina

The reliable prediction of state of charge(SOC)is one of the vital functions of advanced battery management system(BMS),which has great significance towards safe operation of electric *** far,the empirical model-based and data-driven-based SOC estimation methods of lithium-ion batteries have been comprehensively discussed and reviewed in various ***,few reviews involving SOC estimation focused on electrochemical mechanism,which gives physical explanations to SOC and becomes most attractive candidate for advanced *** this reason,this paper comprehensively surveys on physics-based SOC algorithms applied in advanced ***,the research progresses of physical SOC estimation methods for lithium-ion batteries are thoroughly discussed and corresponding evaluation criteria are carefully ***,future perspectives of the current researches on physics-based battery SOC estimation are *** insights stated in this paper are expected to catalyze the development and application of the physics-based advanced BMS algorithms.

关键词： Lithium-ion batteries State of charge Electrochemical model Battery management system

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MAGPNet: Multi-Domain Attention-Guided Pyramid Network for Infrared Small Object Detection

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Journal of Shanghai Jiaotong University (science) 2024年 1-18页

作者： Ding, Leqi Wang, Biyun Yao, Lixiu Cai, Yunze Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China National Key Laboratory of Air-based Information Perception and Fusion Henan Luoyang471009 China Shanghai Institute of Aerospace Control Technology Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation Shanghai201109 China

To overcome the obstacles of poor feature extraction and little prior information on the appearance of infrared dim small targets, we propose a multi-domain attention-guided pyramid network (MAGPNet). Specifically, we design three modules to ensure that salient features of small targets can be acquired and retained in the multi-scale feature maps. To improve the adaptability of the network for targets of different sizes, we design a kernel aggregation attention block with a receptive field attention branch and weight the feature maps under different perceptual fields with attention mechanism. Based on the research on human vision system, we further propose an adaptive local contrast measure module to enhance the local features of infrared small targets. With this parameterized component, we can implement the information aggregation of multi-scale contrast saliency maps. Finally, to fully utilize the information within spatial and channel domains in feature maps of different scales, we propose the mixed spatial-channel attention-guided fusion module to achieve high-quality fusion effects while ensuring that the small target features can be preserved at deep layers. Experiments on public datasets demonstrate that our MAGPNet can achieve a better performance over other state-of-the-art methods in terms of the intersection of union, Precision, Recall, and F-measure. In addition, we conduct detailed ablation studies to verify the effectiveness of each component in our network. © 2024, Shanghai Jiao Tong University.

关键词： Software engineering

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Hyperspectral Image Anomaly Detection Based on Auto Encoder 4

Hyperspectral Image Anomaly Detection Based on Auto Encoder

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4th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2022

作者： Li, Guanzhe Wu, Lingda Hao, Hongxing Science and Technology on Complex Electronic System Simulation Laboratory Space engineering university Beijing China

ISBN: (数字)9781510662179

ISBN: (纸本)9781510662162

The reason for the existence of small targets and sub-pixel targets in hyperspectral images is that the low spatial resolution leads to the phenomenon of mixed pixels, which makes some targets exist in the form of small targets and sub-pixel targets in the image. Moreover, the traditional anomaly detection algorithm introduces the regularization parameter into hyperspectral anomaly detection, but the regularization parameter needs to be manually set and adjusted. However, since the anomaly detection is carried out without prior information, in real situations, it is difficult to obtain the prior information of the target, so the regularization parameter may be difficult to determine. In addition, considering that traditional algorithms are not sensitive to small target pixel targets, a hyperspectral anomaly detection network based on self encoder is designed for hyperspectral images with small targets and sub-pixel targets. © 2023 SPIE.

关键词： Anomaly detection

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DDCTrack： Dynamic Token Sampling for Efficient UAV Transformer Tracking 27th

DDCTrack： Dynamic Token Sampling for Efficient UAV Transfor...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Du, Guocai Zhou, Peiyong Yadikar, Nurbiya Aysa, Alimjan Ubul, Kurban College of Information Science and Engineering Xinjiang University Urumqi830046 China The Key Laboratory of Multilingual Information Technology Xinjiang University Urumqi830046 China

ISBN: (纸本)9783031783531

Although state-of-the-art transformer models have shown promising results in unmanned aerial vehicle (UAV) tracking, they come with high computational demands. Existing tracking methods aim to reduce computational complexity by controlling the number of tokens. However, this method is not effective for all tracking methods. Therefore, we propose a novel dynamic token sampling for an efficient UAV transformer tracking framework. Unlike previous transformer-based tracking methods, our method avoids the need for complex head networks like classification and regression. It solely employs our newly designed encoder, comprising three key components: Dynamic Position Embedding, Dynamic Token Sampler, and Convolutional Feed-Forward Network. This module enhances visual representation by scoring and dynamically sampling tokens, allowing for a flexible token count that adapts to target changes within each frame. We utilize a simple image-sequence contrastive loss as the loss function. Our approach not only simplifies the tracking framework, but also achieves state-of-the-art performance on multiple challenging datasets at real-time run speeds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Aircraft detection

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In situ preparation of Ag@AgCl/Bio-veins composites and their photocatalytic activity and recyclability

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Frontiers of Chemical science and engineering 2023年第7期17卷 906-917页

作者： Min Zou Chao Tan Zhengqiu Yuan Ming Wu Jian Jian Lei Zhang Yan Zhang Zhou Ma Hu Zhou School of Resource&Environment and Safety Engineering Hunan University of Science and TechnologyXiangtan 411201China Key Laboratory of Theoretical Organic Chemistry and Functional Molecules Ministry of EducationFunctional Film Materials Engineering Research Center of Hunan ProvinceHunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and ConversionSchool of Chemistry and Chemical EngineeringHunan University of Science and TechnologyXiangtan 411201China School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China

The industrial application of nano-photocatalysts in wastewater treatment has been severely restricted for a long time due to their difficult separation,poor reusability,and low *** this work,a facile strategy was proposed to enhance the photocatalytic activity and recovery performance of Ag@AgCl *** veins(Bio-veins)with a unique 3D porous construction were used as carriers for the in-situ growth of Ag@AgCl *** electron microscopy results showed that the Ag@AgCl nanoparticles were uniformly loaded on the surface and interior of the Bio-veins,and the size of the Ag@AgCl nanoparticles immobilized on the Bio-veins(50–300 nm)was significantly smaller than Ag@AgCl obtained by the co-precipitation method(1–3μm).The Bio-veins played a vital role in the photocatalysis reaction *** degradation efficiency of the Ag@AgCl/Bio-veins(CI4)was up to 3.50 times as high as pure Ag@***,the composites also exhibited excellent recyclability and stability under both visible and solar *** work provided a suitable strategy for nano-photocatalysts for practical application and may also offer new possibilities for the high-value utilization of biomass materials.

关键词： Ag@AgCl biological veins photocatalytic activity recyclability

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Event-Triggered Mechanism-Based MPC for Path-Tracking Control of Four-Wheel Steering Vehicles 22

Event-Triggered Mechanism-Based MPC for Path-Tracking Contro...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhang, Xiangyu Xu, Guoyan Li, Han Chen, Peng Xia, Qi Cai, Han School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

In this study, we tackle the path-tracking problem of a nonlinear four-wheel steering vehicle dynamics model subject to model mismatches and propose a model predictive control (MPC) algorithm based on an event-triggered mechanism (ET-MPC). The goal is to maintain closed-loop control performance while reducing the computational and communication burdens of traditional MPC. We introduce an ET-MPC framework utilizing a model-free reinforcement learning agent with proximal policy optimization (PPO). This agent interacts with the MPC system, progressively learning to determine the optimal event-triggered mechanism. To enhance exploration and training efficiency, we incorporate the Long Short-Term Memory (LSTM) technique into PPO. Experimental results show that the proposed ET-MPC framework, combined with reinforcement learning for reward optimization, demonstrates superior overall performance in path-tracking control of four-wheel steering vehicles. © 2024 IEEE.

关键词： Four wheel steering

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Cable segmentation based on Mask2Former in open-pit mining area 22

Cable segmentation based on Mask2Former in open-pit mining a...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Wang, Liyun Zhou, Bin Yang, Songyue Liu, Wentao Li, Huazhi Sun, Shengdi School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China State Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehicles Ministry of Industry and Information Technology Beihang University Beijing China

ISBN: (纸本)9798331527471

The development of unmanned transportation technology has improved operational efficiency and safety in open-pit mining areas. However, there remain significant challenges to be addressed. One pressing issue is the need for mining trucks to pass through the area where the cable is laid on the ground. Accidentally crushing or damaging these cables would lead to significant risk to the mining area operation. However, due to the complexity of the mining environment and the characteristics of cables being thin and curved, the existing methods such as edge segmentation are difficult to meet the requirements for cable segmentation. This paper introduces a cable segmentation method based on Mask2Former and carries out comprehensive experiments to examine the effectiveness of the method. Experimental results show that the proposed method achieves IoU of 66.04% and PA of 80.08%, which can meet the requirements of practical applications. © 2024 IEEE.

关键词： Mine trucks

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