检索结果-内蒙古大学图书馆

Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

引用

IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge Computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

GF-2 Image Blue Roof Building Extraction Method Based on Object-Oriented Classification Technology 1

引用

6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Zhang, Dezheng Yin, Xinyu Xie, Yonghong He, Zheyu Wang, Li School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (数字)9789811580833

ISBN: (纸本)9789811580826

Remote sensing image classification method is the focus of current academic field. Most studies use complex machine learning and deep learning methods to extract ground object, but lack simple and effective methods to extract ground object with distinctive features. In this paper, using rule-based object-oriented classification technology, through remote sensing image preprocessing, multi-scale segmentation, extraction rule formulation and other steps, using object-oriented classification method, taking Jinfeng District, Xixia District and Xingqing District of Yinchuan City as an example, The blue roof buildings in the range were extracted, and the results showed that the method can effectively extract the target features. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Roofs

来源：评论

学校读者我要写书评

暂无评论

Image Super-Resolution Reconstruction with Dense Residual Attention Module 1

引用

6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Wang, Li Chen, Ziyi Li, Hui Pan, Chengwei Zhang, Dezheng School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China

ISBN: (数字)9789811580833

ISBN: (纸本)9789811580826

Deep convolutional neural networks have recently achieved great success in the field of image super-resolution. However, most of the super-resolution methods based on deep neural network do not make full use of the multi-level features which extracted from low-resolution images, and do not pay attention to the high-frequency information which needs to be reconstructed in the image, so the performances are relatively poor. Aiming at these problems, we propose a dense residual attention module to improve the image reconstruction performance. The dense residual attention module proposed in this paper makes full use of low-level image feature, and the channel spatial attention mechanism makes the network pay more attention to the high-frequency information that the image needs to be repaired, and uses the sub-pixel convolution to complete the image. Experiments were carried out on five benchmark datasets Set5, Set14, BSD100, Urban100 and DIV2K100. When the magnification was 4, the PSNR and SSIM are 32.47 dB/0.8986, 29.72 dB/0.8004, 27.73 dB/0.7423, 26.63 dB/0.8030, 29.43 dB/0.9023. Compared with other methods, we obtain the expected results. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

引用

IEEE Conference on Decision and Control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimizing the fluidity in thin-section investment casting for vacuum induction molten AZ91D-1 %wt. CaO

引用

Journal of Magnesium and Alloys 2025年

作者： Carneiro, V.H. Lopes, V. Duarte, I. Puga, H. INESC TEC - Institute for Systems and Computer Engineering Technology and Science R. Dr. Roberto Frias Porto4200-465 Portugal Department of Engineering School of Science and Technology - Universidade de Trás-os-Montes e Alto Douro Quinta de Prados Vila Real5001-801 Portugal INL – International Iberian Nanotechnology Laboratory Avenida Mestre José Veiga Braga4715-330 Portugal CMEMS-UMinho Department of Mechanical Engineering University of Minho Campus de Azurém Guimarães4800-058 Portugal TEMA - Centre for Mechanical Technology and Automation Department of Mechanical Engineering University of Aveiro Campus Universitário de Santiago Aveiro3810-193 Portugal LASI - Intelligent Systems Associate Laboratory Campus Azurém Guimarães4800-058 Portugal

This paper discusses an experimental investigation into the fluidity of AZ91D-1 %wt. CaO magnesium melt via induction for thin-section investment casting. Plaster molds with thin spiral cavities (0.5 to 1.5 mm square sections) were designed and manufactured to assess the impact of casting conditions on filling length, as magnesium alloys cause severe melting and melt-mold exothermic reactions, making investment casting challenging. Combinations of traditional Mg-mold reaction mitigation techniques, such as applying a protective mold coating (Yttria) and vacuum, were examined to determine their role in the filling process. The results suggest that when induction is employed to melt reactive alloys, these methods are not always beneficial, as initially thought. Particularly at higher melt temperatures, the combination of Yttria-coated molds with low-pressure vacuum induction significantly reduce fluidity: vacuum induced melt levitation which promotes oxidation with the residual atmosphere;and Yttria-coating cracking due to thermal stress during the mold fabrication slows filling and promotes significant melt-mold reaction. This study shows that best results to investment cast thin-sections are obtained by avoiding both vacuum and protective coatings, providing a viable route for the precision manufacturing of stent biomedical devices. © 2025 Chongqing University

关键词： Investment casting

来源：评论

学校读者我要写书评

暂无评论

Robust Multiple-Structure Geometric Model Fitting by Preserving Neighborhood Consistency

SSRN

引用

SSRN 2023年

作者： Guo, Hanlin Xiao, Guobao Yan, Yan Lin, Shuyuan Wang, Hanzi Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen361005 China School of Electrical Engineering and Automation Xiamen University of Technology Xiamen361005 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China College of Information Science and Technology College of Cyber Security Jinan University Guangzhou510632 China

Geometric model fitting has been widely used in various computer vision applications. However, it remains a challenging task when handling the data that contains multiple model instances (i.e, structures) and a large number of false matches (i.e., outliers) between image pairs. In this paper, we propose a novel Neighborhood-Consistency-based Multiple-structure Fitting method (NCMF) to robustly estimate multiple model instances from the data with outliers, by preserving the neighborhood relationships of potential inliers. Specifically, we first propose a neighborhood consistency preservation algorithm, which explores both the motion pattern consistency and residual pattern consistency of input data, to alleviate the sensitivity to outliers. Consequently, we can produce the significant neighborhood set for each of input data and the reliable neighborhood relationships for data clustering. Then, we propose a robust neighborhoodconsistency-based clustering algorithm by an agglomerative clustering procedure, to effectively distinguish the inliers belonging to different model instances from outliers. In addition, we propose a neighborhood-relationship-based measurement function for data similarity to further make the clustering phase robust to outliers. Overall, NCMF is able to effectively estimate the number and the parameters of model instances from input data simultaneously. Extensive experimental results on several challenging datasets show that the proposed NCMF method can obtain significant superiority over several other state-of-theart model fitting methods on both fitting accuracy and computational efficiency. © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries

Exponential Weighted Moving Average-Based Variational Mode D...

引用

Data Driven Control and Learning Systems (DDCLS)

作者： Penghao Wu Hongfeng Tao Engang Tian Tangwen Yin Hongtian Chen Yiyang Chen School of Mechanical and Electrical Engineering Soochow University Suzhou P. R. China Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University Wuxi P. R. China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai P. R. China Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods.

关键词： Fault diagnosis Learning systems Employee welfare Sensitivity Power supplies Noise Electric vehicles

来源：评论

学校读者我要写书评

暂无评论

FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment

arXiv

引用

arXiv 2023年

作者： Lin, Qinghua Li, Zuoyong Zeng, Kun Fan, Haoyi Li, Wei Zhou, Xiaoguang School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Automation Beijing University of Post and Telecommunications Beijing100876 China School of Economics and Management Minjiang University Fuzhou350121 China

Deep learning techniques have greatly enhanced the performance of fire detection in videos. However, video-based fire detection models heavily rely on labeled data, and the process of data labeling is particularly costly and time-consuming, especially when dealing with videos. Considering the limited quantity of labeled video data, we propose a semi-supervised fire detection model called FireMatch, which is based on consistency regularization and adversarial distribution alignment. Specifically, we first combine consistency regularization with pseudo-label. For unlabeled data, we design video data augmentation to obtain corresponding weakly augmented and strongly augmented samples. The proposed model predicts weakly augmented samples and retains pseudo-label above a threshold, while training on strongly augmented samples to predict these pseudo-labels for learning more robust feature representations. Secondly, we generate video cross-set augmented samples by adversarial distribution alignment to expand the training data and alleviate the decline in classification performance caused by insufficient labeled data. Finally, we introduce a fairness loss to help the model produce diverse predictions for input samples, thereby addressing the issue of high confidence with the non-fire class in fire classification scenarios. The FireMatch achieved an accuracy of 76.92% and 91.81% on two real-world fire datasets, respectively. The experimental results demonstrate that the proposed method outperforms the current state-of-the-art semi-supervised classification methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Fires

来源：评论

学校读者我要写书评

暂无评论

Pay Less But Get More: A Dual-Attention-based Channel Estimation Network for Massive MIMO Systems with Low-Density Pilots

arXiv

引用

arXiv 2023年

作者： Zhou, Binggui Yang, Xi Ma, Shaodan Gao, Feifei Yang, Guanghua The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China The Shanghai Key Laboratory of Multidimensional Information Processing School of Communication and Electronic Engineering East China Normal University Shanghai200241 China Department of Automation Tsinghua University Beijing100084 China The School of Intelligent Systems Science and Engineering The GBA and B&R International Joint Research Center for Smart Logistics Jinan University Zhuhai519070 China

To reap the promising benefits of massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) is required through channel estimation. However, due to the complicated wireless propagation environment and large-scale antenna arrays, precise channel estimation for massive MIMO systems is significantly challenging and costs an enormous training overhead. Considerable time-frequency resources are consumed to acquire sufficient accuracy of CSI, which thus severely degrades systems’ spectral and energy efficiencies. In this paper, we propose a dual-attention-based channel estimation network (DACEN) to realize accurate channel estimation via low-density pilots, by jointly learning the spatial-temporal domain features of massive MIMO channels with the temporal attention module and the spatial attention module. To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period. Experimental results reveal that the proposed DACEN-based method achieves better channel estimation performance than the existing methods under various pilot-density settings and signal-to-noise ratios. Additionally, with the proposed parameter-instance transfer learning approach, the DACEN-based method achieves additional performance gain, thereby further demonstrating the effectiveness and superiority of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Channel estimation

来源：评论

学校读者我要写书评

暂无评论

Line Drawing Guided Progressive Inpainting of Mural Damage

arXiv

引用

arXiv 2022年

作者： Li, Luxi Zou, Qin Zhang, Fan Yu, Hongkai Chen, Long Song, Chengfang Huang, Xianfeng Wang, Xiaoguang Li, Qingquan Department of Computer Science Technology United International College of Beijing Normal University HongKong Baptist University Zhuhai China Machine Vision and Robotics Laboratory School of Computer Science Wuhan University Wuhan China State Key Laboratory of Surveying Mapping and Remote Sensing Information Engineering Wuhan University Wuhan China Department of Electrical Engineering and Computer Science Cleveland State University OH United States Institute of Automation Chinese Academy of Sciences Beijing China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan China Guangming Laboratory Shenzhen University Shenzhen China

Mural image inpainting is far less explored compared to its natural image counterpart and remains largely unsolved. Most existing image-inpainting methods tend to take the target image as the only input and directly repair the damage to generate a visually plausible result. These methods obtain high performance in restoration or completion of some pre-defined objects, e.g., human face, fabric texture, and printed texts, etc., however, are not suitable for repairing murals with varying subjects and large damaged areas. Moreover, due to discrete colors in paints, mural inpainting may suffer from apparent color bias. To this end, in this paper, we propose a line drawing guided progressive mural inpainting method. It divides the inpainting process into two steps: structure reconstruction and color correction, implemented by a structure reconstruction network (SRN) and a color correction network (CCN), respectively. In structure reconstruction, SRN utilizes the line drawing as an assistant to achieve large-scale content authenticity and structural stability. In color correction, CCN operates a local color adjustment for missing pixels which reduces the negative effects of color bias and edge jumping. The proposed approach is evaluated against the current state-of-the-art image inpainting methods. Qualitative and quantitative results demonstrate the superiority of the proposed method in mural image inpainting. The codes and data are available at https://***/qinnzou/mural-image-inpainting . © 2022, CC BY-NC-ND.

关键词： Restoration

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：